Machine Learning: Classification Coursera Quiz Answers 2022 | All Weeks Assessment Answers [💯Correct Answer]

by
Machine Learning: Classification Coursera Quiz Answers 2022 | All Weeks Assessment Answers [💯Correct Answer]
Table of Contents

Hello Peers, Today we are going to share all week’s assessment and quizzes answers of the Machine Learning: Classification course launched by Coursera totally free of cost✅✅✅. This is a certification course for every interested student.

In case you didn’t find this course for free, then you can apply for financial ads to get this course for totally free.

Check out this article – “How to Apply for Financial Ads?”

About The Coursera

Coursera, India’s biggest learning platform launched millions of free courses for students daily. These courses are from various recognized universities, where industry experts and professors teach in a very well manner and in a more understandable way.

Here, you will find Machine Learning: Classification Exam Answers in Bold Color which are given below.

These answers are updated recently and are 100% correct✅ answers of all week, assessment, and final exam answers of Machine Learning: Classification from Coursera Free Certification Course.

Use “Ctrl+F” To Find Any Questions Answer. & For Mobile User, You Just Need To Click On Three dots In Your Browser & You Will Get A “Find” Option There. Use These Option to Get Any Random Questions Answer.

About Machine Learning: Classification Course

Case studies: analyzing mood and predicting loan defaults

In our case study on analyzing sentiment, you will make models that predict a class (positive/negative sentiment) from input features (text of the reviews, user profile information,…).

In this course’s second case study, “Loan Default Prediction,” you’ll look at financial data and try to figure out when a loan is likely to be risky or safe for the bank. These tasks are examples of classification, which is one of the most popular parts of machine learning. It has a wide range of uses, such as targeting ads, finding spam, making medical diagnoses, and classifying images.

In this course, you’ll learn how to make classifiers that do the best job possible on a variety of tasks. You will learn about the most successful and widely used techniques, like logistic regression, decision trees, and boosting. Using stochastic gradient ascent, you will also be able to design and implement the algorithms that make it possible for these models to learn at scale. You will use these techniques to do large-scale machine learning tasks in the real world. You will also learn how to handle missing data and measure precision and recall to evaluate a classifier, which are both important tasks you will face when using ML in the real world.

This course is very hands-on and full of activities. It also has a lot of visuals and examples that show how these techniques will work with real data. We’ve also added advanced topics to each module’s optional content for those who want to learn more.

Learning Goals: When this course is over, you will be able to:

  • -Explain what goes into a classification model and what comes out of it.
  • -Work on both two-class and more than two-class classification problems.
  • -Implement a logistic regression model for large-scale classification.
  • -Use decision trees to make a model that isn’t linear.
  • -Use boosting to improve the performance of any model.
  • -Use stochastic gradient ascent to make your methods more powerful.
  • -Talk about the basic rules for making decisions.
  • -Make a classification model figure out how people feel about a set of product reviews.
  • -Analyze financial data to predict loan defaults.
  • -Use ways to deal with missing data.
  • Use precision-recall metrics to judge your models.
  • Implement these techniques in Python (or in the language of your choice, though Python is highly recommended).

SKILLS YOU WILL GAIN

  • Logistic Regression
  • Statistical Classification
  • Classification Algorithms
  • Decision Tree

Course Apply Link – Machine Learning: Classification

Machine Learning: Classification Quiz Answers

Week 1: Machine Learning: Classification Quiz Answers

Quiz 1: Linear Classifiers & Logistic Regression

Question 1: (True/False) A linear classifier assigns the predicted class based on the sign of Score(x)=wTh(x).

  • True
  • False

Question 2: (True/False) For a conditional probability distribution over y | \mathbf{x}y∣x, where yy takes on two values (+1, -1, i.e. good review, bad review) P(y = +1 | \mathbf{x}) + P(y = -1 | \mathbf{x}) = 1P(y=+1∣x)+P(y=−1∣x)=1.

  • True
  • False

Question 3: Which function does logistic regression use to “squeeze” the real line to [0, 1]?

  • Logistic function
  • Absolute value function
  • Zero function

Question 4: If Score(x)=wTh(x)>0, which of the following is true about P(y = +1 | \mathbf{x})P(y=+1∣x)?

  • P(y = +1 | x) <= 0.5
  • P(y = +1 | x) > 0.5
  • Can’t say anything about P(y = +1 | x)

Question 5: Consider training a 1 vs. all multiclass classifier for the problem of digit recognition using logistic regression. There are 10 digits, thus there are 10 classes. How many logistic regression classifiers will we have to train?

Answer: 10

Quiz 2: Predicting sentiment from product reviews

Question 1: How many weights are greater than or equal to 0?

Answer: 68419

Question 2: Of the three data points in sample_test_data, which one has the lowest probability of being classified as a positive review?

  • First
  • Second
  • Third

Question 3: Which of the following products are represented in the 20 most positive reviews?

  • Snuza Portable Baby Movement Monitor
  • Mamadou Kids Foldable Play Yard Mattress Topper, Blue
  • Britax Decathlon Convertible Car Seat, Tiffany
  • Safety 1st Exchangeable Tip 3 in 1 Thermometer

Question 4: Which of the following products are represented in the 20 most negative reviews?

  • The First Years True Choice P400 Premium Digital Monitor, 2 Parent Unit
  • JP Lizzy Chocolate Ice Classic Tote Set
  • Peg-Perego Tatamia High Chair, White Latte
  • Safety 1st High-Def Digital Monitor

Question 5: What is the accuracy of the sentiment_model on the test_data? Round your answer to 2 decimal places (e.g. 0.76).

Answer: 0.91

Question 6: Does a higher accuracy value on the training_data always imply that the classifier is better?

  • Yes, higher accuracy on training data always implies that the classifier is better.
  • No, higher accuracy on training data does not necessarily imply that the classifier is better.

Question 7: Consider the coefficients of simple_model. There should be 21 of them, an intercept term + one for each word in significant_words.

How many of the 20 coefficients (corresponding to the 20 significant_words and excluding the intercept term) are positive for the simple_model?

Answer: 10

Question 8: Are the positive words in the simple_model also positive words in the sentiment_model?

  • Yes
  • No

Question 9: Which model (sentiment_model or simple_model) has higher accuracy on the TRAINING set?

  • Sentiment_model
  • Simple_model

Question 10: Which model (sentiment_model or simple_model) has higher accuracy on the TEST set?

  • Sentiment_model
  • Simple_model

Question 11: Enter the accuracy of the majority class classifier model on the test_data. Round your answer to two decimal places (e.g. 0.76).

Answer: 0.84

Question 12: Is the sentiment_model definitely better than the majority class classifier (the baseline)?

  • Yes
  • No

Week 2: Machine Learning: Classification Quiz Answers

Quiz 1: Learning Linear Classifiers

Also, check out these Coursera answers:

Question 1: (True/False) A linear classifier can only learn positive coefficients.

  • True
  • False

Question 2: (True/False) In order to train a logistic regression model, we find the weights that maximize the likelihood of the model.

  • True
  • False

Question 3: (True/False) The data likelihood is the product of the probability of the inputs \mathbf{x}x given the weights \mathbf{w}w and response yy.

  • True
  • False

Question 4: Questions 4 and 5 refer to the following scenario.

Consider the setting where our inputs are 1-dimensional. We have data

xxyy
2.5+1
0.3-1
2.8+1
0.5+1

and the current estimates of the weights are w_0 = 0w0​=0 and w_1 = 1w1​=1. (w_0w0​: the intercept, w_1w1​: the weight for xx).

Calculate the likelihood of this data. Round your answer to 2 decimal places.

Answer: 0.23

Question 5: Refer to the scenario given in Question 4 to answer the following:

Calculate the derivative of the log likelihood with respect to w_1w1​. Round your answer to 2 decimal places.

Answer: 0.37

Question 6: Which of the following is true about gradient ascent? Select all that apply.

  • It is an iterative algorithm
  • It only updates a few of the parameters, not all of them
  • It finds the maximum by “hill climbing”

Quiz 2: Implementing logistic regression from scratch

Question 1: How many reviews in amazon_baby_subset.gl contain the word perfect?

Answer: 2955

Question 2: Consider the feature_matrix that was obtained by converting our data to NumPy format.

How many features are there in the feature_matrix?

Answer: 194

Question 3: Assuming that the intercept is present, how does the number of features in feature_matrix relate to the number of features in the logistic regression model? Let x = [number of features in feature_matrix] and y = [number of features in logistic regression model].

  • y = x – 1
  • y = x
  • y = x + 1
  • None of the above

Question 4: Run your logistic regression solver with provided parameters.

As each iteration of gradient ascent passes, does the log-likelihood increase or decrease?

  • It increases.
  • It decreases.
  • None of the above

Question 5: We make predictions using the weights just learned.

How many reviews were predicted to have positive sentiment?

Answer: 25126

Question 6: What is the accuracy of the model on predictions made above? (round to 2 digits of accuracy)

Answer: 0.75

Question 7: We look at “most positive” words, the words that correspond most strongly with positive reviews.

Which of the following words is not present in the top 10 “most positive” words?

  • love
  • easy
  • great
  • perfect
  • cheap

Question 8: Similarly, we look at “most negative” words, the words that correspond most strongly with negative reviews.

Which of the following words is not present in the top 10 “most negative” words?

  • need
  • work
  • disappointed
  • even
  • return

Quiz 3: Overfitting & Regularization in Logistic Regression

Question 1: Consider four classifiers, whose classification performance is given by the following table:

Classification error on training setClassification error on validation set
Classifier 10.20.6
Classifier 20.80.6
Classifier 30.20.2
Classifier 40.50.4

Which of the four classifiers is most likely overfit?

  • Classifier 1
  • Classifier 2
  • Classifier 3
  • Classifier 4

Question 2: Suppose a classifier classifies 23100 examples correctly and 1900 examples incorrectly. Compute accuracy by hand. Round your answer to 3 decimal places.

Answer: 0.076

Question 3: (True/False) Accuracy and error measured on the same dataset always sum to 1.

  • True
  • False

Question 4: Which of the following is NOT a correct description of complex models?

  • Complex models accommodate many features.
  • Complex models tend to produce lower training error than simple models.
  • Complex models tend to generalize better than simple models.
  • Complex models tend to exhibit high variance in response to perturbation in the training data.
  • Complex models tend to exhibit low bias, capturing many patterns in the training data that simple models may have missed.

Question 5: Which of the following is a symptom of overfitting in the context of logistic regression? Select all that apply.

  • Large estimated coefficients
  • Good generalization to previously unseen data
  • Simple decision boundary
  • Complex decision boundary
  • Overconfident predictions of class probabilities

Question 6: Suppose we perform L2 regularized logistic regression to fit a sentiment classifier. Which of the following plots does NOT describe a possible coefficient path? Choose all that apply.

Note. Assume that the algorithm runs for a wide range of L2 penalty values and each coefficient plot is zoomed out enough to capture all long-term trends.

Answer:

5YNc7eWUEeWOVQ68c1xy2w 768b146d738d6e1991ac9d5f678dbef6 9wGVMuBbEeW hK2gi BIw 27e6398723f122d80d4caae557763566 Capture

Q 7: Suppose we perform L1 regularized logistic regression to fit a sentiment classifier. Which of the following plots does NOT describe a possible coefficient path? Choose all that apply.

Note. Assume that the algorithm runs for a wide range of L1 penalty values and each coefficient plot is zoomed out enough to capture all long-term trends.

Answer:

A16k eWVEeW
Mf6t5uWVEeWuUgrcWIxPhQ 2a643ffaf830d9218db5a6314936d52f V4 Ld


Question 8: In the context of L2 regularized logistic regression, which of the following occurs as we increase the L2 penalty \lambdaλ? Choose all that apply.

  • The L2 norm of the set of coefficients gets smaller
  • Region of uncertainty becomes narrower, i.e., the classifier makes predictions with higher confidence.
  • Decision boundary becomes less complex
  • Training error decreases
  • The classifier has lower variance
  • Some features are excluded from the classifier

Quiz 4: Logistic Regression with L2 regularization

Question 1: In the function feature_derivative_with_L2, was the intercept term regularized?

  • Yes
  • No

Question 2: Does the term with L2 regularization increase or decrease the log likelihood \ell\ell(\mathbf{w})ℓℓ(w)?

  • Increases
  • Decreases

Question 3: Which of the following words is not listed in either positive_words or negative_words?

  • love
  • disappointed
  • great
  • money
  • quality

Question 4: Questions 5 and 6 use the coefficient plot of the words in positive_words and negative_words.

(True/False) All coefficients consistently decrease in magnitude as the L2 penalty is increased.

  • True
  • False

Question 5: Questions 5 and 6 use the coefficient plot of the words in positive_words and negative_words.

(True/False) The relative order of coefficients is preserved as the L2 penalty is increased. (For example, if the coefficient for ‘cat’ was more positive than that for ‘dog’, this remains true as the L2 penalty increases.)

  • True
  • False

Question 6: Questions 7, 8, and 9 ask you about the 6 models trained with different L2 penalties.

Which of the following models has the highest accuracy on the training data?

  • Model trained with L2 penalty = 0
  • Model trained with L2 penalty = 4
  • Model trained with L2 penalty = 10
  • Model trained with L2 penalty = 100
  • Model trained with L2 penalty = 1e3
  • Model trained with L2 penalty = 1e5

Question 7: Questions 7, 8, and 9 ask you about the 6 models trained with different L2 penalties.

Which of the following models has the highest accuracy on the validation data?

  • Model trained with L2 penalty = 0
  • Model trained with L2 penalty = 4
  • Model trained with L2 penalty = 10
  • Model trained with L2 penalty = 100
  • Model trained with L2 penalty = 1e3
  • Model trained with L2 penalty = 1e5

Question 8: Questions 7, 8, and 9 ask you about the 6 models trained with different L2 penalties.

Does the highest accuracy on the training data imply that the model is the best one?

  • Yes
  • No

Week 3: Machine Learning: Classification Quiz Answers

Quiz 1: Decision Trees

Question 1: Questions 1 to 6 refer to the following common scenario:

Consider the following dataset:

x1x2x3y
111+1
010-1
101-1
001+1

Let us train a decision tree with this data. Let’s call this tree T1. What feature will we split on at the root?

  • x1
  • x2
  • x3

Question 2: Refer to the dataset presented in Question 1 to answer the following.

Fully train T1 (until each leaf has data points of the same output label). What is the depth of T1?

Answer: 3

Question 3: Refer to the dataset presented in Question 1 to answer the following.

What is the training error of T1?

Answer: 0

Question 4: Refer to the dataset presented in Question 1 to answer the following.

Now consider a tree T2, which splits on x1 at the root, and splits on x2 in the 1st level, and has leaves at the 2nd level. Note: this is the XOR function on features 1 and 2. What is the depth of T2?

Answer: 2

Question 5: Refer to the dataset presented in Question 1 to answer the following.

What is the training error of T2?

Answer: 0

Question 6: Refer to the dataset presented in Question 1 to answer the following.

Which has smaller depth, T1 or T2?

  • T1
  • T2

Question 7: (True/False) When deciding to split a node, we find the best feature to split on that minimizes classification error.

  • True
  • False

Question 8: If you are learning a decision tree, and you are at a node in which all of its data has the same y value, you should

  • find the best feature to split on
  • create a leaf that predicts the y value of all the data
  • terminate recursions on all branches and return the current tree
  • go back to the PARENT node and select a DIFFERENT feature to split on so that the y values are not all the same at THIS node

Question 9: Consider two datasets D1 and D2, where D2 has the same data points as D1, but has an extra feature for each data point. Let T1 be the decision tree trained with D1, and T2 be the tree trained with D2. Which of the following is true?

  • T2 has better training error than T1
  • T2 has better test error than T1
  • Too little information to guarantee anything

Question 10: (True/False) Logistic regression with polynomial degree 1 features will always have equal or lower training error than decision stumps (depth 1 decision trees).

  • True
  • False

Question 11: (True/False) Decision stumps (depth 1 decision trees) are always linear classifiers.

  • True
  • False

Quiz 2: Identifying safe loans with decision trees

Question 1: What percentage of the predictions on sample_validation_data did decision_tree_model get correct?

  • 25%
  • 50%
  • 75%
  • 100%

Question 2: Which loan has the highest probability of being classified as a safe loan?

  • First
  • Second
  • Third
  • Fourth

Question 3: Notice that the probability preditions are the exact same for the 2nd and 3rd loans. Why would this happen?

  • During tree traversal both examples fall into the same leaf node.
  • This can only happen with sheer coincidence.

Question 4: What is the accuracy of decision_tree_model on the validation set, rounded to the nearest .01 (e.g. 0.76)?

Answer: 0.64

Question 5: How does the performance of big_model on the validation set compare to decision_tree_model on the validation set? Is this a sign of overfitting?

  • big_model has higher accuracy on the validation set than decision_tree_model. This is overfitting.
  • big_model has higher accuracy on the validation set than decision_tree_model. This is not overfitting.
  • big_model has lower accuracy on the validation set than decision_tree_model. This is overfitting.
  • big_model has lower accuracy on the validation set than decision_tree_model. This is not overfitting.

Question 6: Let us assume that each mistake costs money:

  • Assume a cost of $10,000 per false negative.
  • Assume a cost of $20,000 per false positive.

What is the total cost of mistakes made by decision_tree_model on validation_data? Please enter your answer as a plain integer, without the dollar sign or the comma separator, e.g. 3002000.

Answer: 50280000

Quiz 3: Implementing binary decision trees

Question 1: What was the feature that my_decision_tree first split on while making the prediction for test_data[0]?

  • emp_length.4 years
  • grade.A
  • term. 36 months
  • home_ownership.MORTGAGE

Question 2: What was the first feature that lead to a right split of test_data[0]?

  • emp_length.< 1 year
  • emp_length.10+ years
  • grade.B
  • grade.D

Question 3: What was the last feature split on before reaching a leaf node for test_data[0]?

  • grade.D
  • grade.B
  • term. 36 months
  • grade.A

Question 4: Rounded to 2nd decimal point (e.g. 0.76), what is the classification error of my_decision_tree on the test_data?

Answer: 0.36

Question 5: What is the feature that is used for the split at the root node?

  • grade.A
  • term. 36 months
  • term. 60 months
  • home_ownership.OWN

Question 6: What is the path of the first 3 feature splits considered along the left-most branch of my_decision_tree?

  • term. 36 months, grade.A, grade.B
  • term. 36 months, grade.A, emp_length.4 years
  • term. 36 months, grade.A, no third feature because second split resulted in leaf

Question 7: What is the path of the first 3 feature splits considered along the right-most branch of my_decision_tree?

  • term. 36 months, grade.D, grade.B
  • term. 36 months, grade.D, home_ownership.OWN
  • term. 36 months, grade.D, no third feature because second split resulted in leaf

Week 4: Machine Learning: Classification Quiz Answers

Quiz 1: Preventing Overfitting in Decision Trees

Question 1: (True/False) When learning decision trees, smaller depth USUALLY translates to lower training error.

  • True
  • False

Question 2: (True/False) If no two data points have the same input values, we can always learn a decision tree that achieves 0 training error.

  • True
  • False

Question 3: (True/False) If decision tree T1 has lower training error than decision tree T2, then T1 will always have better test error than T2.

  • True
  • False

Question 4: Which of the following is true for decision trees?

  • Model complexity increases with size of the data.
  • Model complexity increases with depth.
  • None of the above

Question 5: Pruning and early stopping in decision trees is used to

  • combat overfitting
  • improve training error
  • None of the above

Question 6: Which of the following is NOT an early stopping method?

  • Stop when the tree hits a certain depth
  • Stop when node has too few data points (minimum node “size”)
  • Stop when every possible split results in the same amount of error reduction
  • Stop when best split results in too small of an error reduction

Question 7: Consider decision tree T1 learned with minimum node size parameter = 1000. Now consider decision tree T2 trained on the same dataset and parameters, except that the minimum node size parameter is now 100. Which of the following is always true?

  • The depth of T2 >= the depth of T1
  • The number of nodes in T2 >= the number of nodes in T1
  • The test error of T2 <= the test error of T1
  • The training error of T2 <= the training error of T1

Question 8: Questions 8 to 11 refer to the following common scenario:

Imagine we are training a decision tree, and we are at a node. Each data point is (x1, x2, y), where x1,x2 are features, and y is the label. The data at this node is:

x1x2y
01+1
10+1
01+1
11-1

What is the classification error at this node (assuming a majority class classifier)?

Answer: 0.25

Question 9: Refer to the scenario presented in Question 8.

If we split on x1, what is the classification error?

Answer: 0.25

Question 10: Refer to the scenario presented in Question 8.

If we split on x2, what is the classification error?

Answer: 0.25

Question 11: Refer to the scenario presented in Question 8.

If our parameter for minimum gain in error reduction is 0.1, do we split or stop early?

  • Split
  • Stop early

Quiz 2: Decision Trees in Practice

Question 1: Given an intermediate node with 6 safe loans and 3 risky loans, if the min_node_size parameter is 10, what should the tree learning algorithm do next?

  • Create a leaf and return it
  • Continue building the tree by finding the best splitting feature

Question 2: Assume an intermediate node has 6 safe loans and 3 risky loans. For each of 4 possible features to split on, the error reduction is 0.0, 0.05, 0.1, and 0.14, respectively. If the minimum gain in error reduction parameter is set to 0.2, what should the tree learning algorithm do next?

  • Create a leaf and return it
  • Continue building the tree by using the splitting feature that gives 0.14 error reduction

Question 3: Consider the prediction path validation_set[0] with my_decision_tree_old and my_decision_tree_new. For my_decision_tree_new trained with

1

max_depth = 6, min_node_size = 100, min_error_reduction=0.0

is the prediction path shorter, longer, or the same as the prediction path using my_decision_tree_old that ignored the early stopping conditions 2 and 3?

  • Shorter
  • Longer
  • The same

Question 4: Consider the prediction path for ANY new data point. For my_decision_tree_new trained with

1

max_depth = 6, min_node_size = 100, min_error_reduction=0.0

is the prediction path for a data point always shorter, always longer, always the same, shorter or the same, or longer or the same as for my_decision_tree_old that ignored the early stopping conditions 2 and 3?

  • Always shorter
  • Always longer
  • Always the same
  • Shorter or the same
  • Longer or the same

Question 5: For a tree trained on any dataset using parameters

1

max_depth = 6, min_node_size = 100, min_error_reduction=0.0

what is the maximum possible number of splits encountered while making a single prediction?

Answer: 6

Question 6: Is the validation error of the new decision tree (using early stopping conditions 2 and 3) lower than, higher than, or the same as that of the old decision tree from the previous assigment?

  • Higher than
  • Lower than
  • The same

Question 7: Which tree has the smallest error on the validation data?

  • model_1
  • model_2
  • model_3

Question 8: Does the tree with the smallest error in the training data also have the smallest error in the validation data?

  • Yes
  • No

Question 9: Is it always true that the tree with the lowest classification error on the training set will result in the lowest classification error in the validation set?

  • Yes, this is ALWAYS true.
  • No, this is NOT ALWAYS true.

Question 10: Which tree has the largest complexity?

  • model_1
  • model_2
  • model_3

Question 11: Is it always true that the most complex tree will result in the lowest classification error in the validation_set?

  • Yes, this is always true.
  • No, this is not always true.

Question 12: Using the complexity definition, which model (model_4, model_5, or model_6) has the largest complexity?

  • model_4
  • model_5
  • model_6

Question 13: model_4 and model_5 have similar classification error on the validation set but model_5 has lower complexity. Should you pick model_5 over model_4?

  • Pick model_5 over model_4
  • Pick model_4 over model_5

Question 14: Using the results obtained in this section, which model (model_7, model_8, or model_9) would you choose to use?

  • model_7
  • model_8
  • model_9

Quiz 3: Handling Missing Data

Question 1: (True/False) Skipping data points (i.e., skipping rows of the data) that have missing features only works when the learning algorithm we are using is decision tree learning.

  • True
  • False

Question 2: What are potential downsides of skipping features with missing values (i.e., skipping columns of the data) to handle missing data?

  • So many features are skipped that accuracy can degrade
  • The learning algorithm will have to be modified
  • You will have fewer data points (i.e., rows) in the dataset
  • If an input at prediction time has a feature missing that was always present during training, this approach is not applicable.

Question 3: (True/False) It’s always better to remove missing data points (i.e., rows) as opposed to removing missing features (i.e., columns).

  • True
  • False

Question 4: Consider a dataset with N training points. After imputing missing values, the number of data points in the data set is

  • 2 * N
  • N
  • 5 * N

Question 5: Consider a dataset with D features. After imputing missing values, the number of features in the data set is

  • 2 * D
  • D
  • 0.5 * D

Question 6: Which of the following are always true when imputing missing data? Select all that apply.

  • Imputed values can be used in any classification algorithm
  • Imputed values can be used when there is missing data at prediction time
  • Using imputed values results in higher accuracies than skipping data points or skipping features

Question 7: Consider data that has binary features (i.e. the feature values are 0 or 1) with some feature values of some data points missing. When learning the best feature split at a node, how would we best modify the decision tree learning algorithm to handle data points with missing values for a feature?

  • We choose to assign missing values to the branch of the tree (either the one with feature value equal to 0 or with feature value equal to 1) that minimizes classification error.
  • We assume missing data always has value 0.
  • We ignore all data points with missing values.

Week 5: Machine Learning: Classification Quiz Answers

Quiz 1: Exploring Ensemble Methods

Question 1: What percentage of the predictions on sample_validation_data did model_5 get correct?

  • 25%
  • 50%
  • 75%
  • 100%

Question 2: According to model_5, which loan is the least likely to be a safe loan?

  • First
  • Second
  • Third
  • Fourth

Question 3: What is the number of false positives on the validation data?

Answer: 1618

Question 4: Using the same costs of the false positives and false negatives, what is the cost of the mistakes made by the boosted tree model (model_5) as evaluated on the validation_set?

Answer: 46990000

Question 5: What grades are the top 5 loans?

  • A
  • B
  • C
  • D
  • E

Question 6: Which model has the best accuracy on the validation_data?

  • model_10
  • model_50
  • model_100
  • model_200
  • model_500

Question 7: Is it always true that the model with the most trees will perform best on the test/validation set?

  • Yes, a model with more trees will ALWAYS perform better on the test/validation set.
  • No, a model with more trees does not always perform better on the test/validation set.

Question 8: Does the training error reduce as the number of trees increases?

  • Yes
  • No

Question 9: Is it always true that the test/validation error will reduce as the number of trees increases?

  • Yes, it is ALWAYS true that the test/validation error will reduce as the number of trees increases.
  • No, the test/validation error will not necessarily always reduce as the number of trees increases.

Quiz 2: Boosting

Question 1: Which of the following is NOT an ensemble method?

  • Gradient boosted trees
  • AdaBoost
  • Random forests
  • Single decision trees

Question 2: Each binary classifier in an ensemble makes predictions on an input xx as listed in the table below. Based on the ensemble coefficients also listed in the table, what is the final ensemble model’s prediction for xx?

Classifier coefficient w_twt​Prediction for xx
Classifier 10.61+1
Classifier 20.53-1
Classifier 30.88-1
Classifier 40.34+1
  • +1
  • -1

Question 3: (True/False) Boosted trees tend to be more robust to overfitting than decision trees.

  • True
  • False

Question 4: (True/False) AdaBoost focuses on data points it incorrectly predicted by increasing those weights in the data set.

  • True
  • False

Question 5: Let w_twt​ be the coefficient for a weak learner f_tft​. Which of the following conditions must be true so that w_t > 0wt​>0 ?

  • weighted_error(ft​)<.25
  • weighted_error(ft​)<.5
  • weighted_error(ft​)>.75
  • weighted_error(ft​)>.5

Question 6: If you were using AdaBoost and in an iteration of the algorithm were faced with the following classifiers, which one would you be more inclined to include in the ensemble? A classifier with:

  • weighted error = 0.1
  • weighted error = 0.3
  • weighted error = 0.5
  • weighted error = 0.7
  • weighted error = 0.99

Question 7: Imagine we are training a decision stump in an iteration of AdaBoost, and we are at a node. Each data point is (x1, x2, y), where x1,x2 are features, and y is the label. Also included are the weights of the data. The data at this node is:

Weightx1x2y
0.301+1
0.3510-1
0.101+1
0.2511+1

Suppose we split on feature x2. Calculate the weighted error of this split. Round your answer to 2 decimal places.

Answer: 0.35

Question 8: After each iteration of AdaBoost, the weights on the data points are typically normalized to sum to 1. This is used because

  • of issues with numerical instability (underflow/overflow)
  • the weak learners can only learn with normalized weights
  • none of the above

Question 9: Consider the following 2D dataset with binary labels.MbBDTOBiEeWBDQ73 3lhaw 4436e98252ccb4f3b833fbadbd5a7836 Capture

We train a series of weak binary classifiers using AdaBoost. In one iteration, the weak binary classifier produces the decision boundary as follows:HswHyeBjEeWufRJaRfO1AQ b5746ae36ef317b86c126c93db145c34 Capture2

Which of the five points (indicated in the second figure) will receive higher weight in the following iteration? Choose all that apply.

  • (1)
  • (2)
  • (3)
  • (4)
  • (5)

Question 10: Suppose we are running AdaBoost using decision tree stumps. At a particular iteration, the data points have weights according to the figure. (Large points indicate heavy weights.)

Which of the following decision tree stumps is most likely to fit in the next iteration?

Answer:

3 kqZeD0EeW hK2gi BIw 7d5bf2ab0433c80a45f1593efdefc45f Capture2

Question 11: (True/False) AdaBoost can boost any kind of classifier, not just a decision tree stump.

  • True
  • False

Quiz 3: Boosting a decision stump

Question 1: Recall that the classification error for unweighted data is defined as follows:

classification error=# mistakes# all data points

Meanwhile, the weight of mistakes for weighted data is given by

WM(α,y^)=∑i=1nαi×1[yi≠y^i].

If we set the weights α=1 for all data points, how is the weight of mistakes WM(α,ŷ) related to the classification error?

  • WM(α,ŷ) = [classification error]
  • WM(α,ŷ) = [classification error] * [weight of correctly classified data points]
  • WM(α,ŷ) = N * [classification error]
  • WM(α,ŷ) = 1 – [classification error]

Question 2: Refer to section Example: Training a weighted decision tree.

Will you get the same model as small_data_decision_tree_subset_20 if you trained a decision tree with only 20 data points from the set of points in subset_20?

  • Yes
  • No

Question 3: Refer to the 10-component ensemble of tree stumps trained with Adaboost.

As each component is trained sequentially, are the component weights monotonically decreasing, monotonically increasing, or neither?

  • Monotonically decreasing
  • Monotonically increasing
  • Neither

Question 4: Which of the following best describes a general trend in accuracy as we add more and more components? Answer based on the 30 components learned so far.

  • Training error goes down monotonically, i.e. the training error reduces with each iteration but never increases.
  • Training error goes down in general, with some ups and downs in the middle.
  • Training error goes up in general, with some ups and downs in the middle.
  • Training error goes down in the beginning, achieves the best error, and then goes up sharply.
  • None of the above

Question 5: From this plot (with 30 trees), is there massive overfitting as the # of iterations increases?

  • Yes
  • No

Week 6: Machine Learning: Classification Quiz Answers

Quiz 1: Precision-Recall

Question 1:


Question 1: Questions 1 to 5 refer to the following scenario:

Suppose a binary classifier produced the following confusion matrix.

Predicted PositivePredicted Negative
Actual Positive560040
Actual Negative19002460

What is the recall of this classifier? Round your answer to 2 decimal places.

Answer: 0.81

Question 2: Refer to the scenario presented in Question 1 to answer the following:

(True/False) This classifier is better than random guessing.

  • True
  • False

Question 3: Refer to the scenario presented in Question 1 to answer the following:

(True/False) This classifier is better than the majority class classifier.

  • True
  • False

Question 4: Refer to the scenario presented in Question 1 to answer the following:

Which of the following points in the precision-recall space corresponds to this classifier?TDEYl

  • (1)
  • (2)
  • (3)
  • (4)
  • (5)

Question 5: Refer to the scenario presented in Question 1 to answer the following:

Which of the following best describes this classifier?

  • It is optimistic
  • It is pessimistic
  • None of the above

Question 6: Suppose we are fitting a logistic regression model on a dataset where the vast majority of the data points are labeled as positive. To compensate for overfitting to the dominant class, we should

  • Require higher confidence level for positive predictions
  • Require lower confidence level for positive predictions

Question 7: It is often the case that false positives and false negatives incur different costs. In situations where false negatives cost much more than false positives, we should

  • Require higher confidence level for positive predictions
  • Require lower confidence level for positive predictions

Question 8: We are interested in reducing the number of false negatives. Which of the following metrics should we primarily look at?

  • Accuracy
  • Precision
  • Recall

Question 9: Suppose we set the threshold for positive predictions at 0.9. What is the lowest score that is classified as positive? Round your answer to 2 decimal places.

Answer: 2.20

Quiz 2: Exploring precision and recall

Question 1: Consider the logistic regression model trained on amazon_baby.sframe using Turi Create.

Using accuracy as the evaluation metric, was our logistic regression model better than the majority class classifier?

  • Yes
  • No

Question 2: How many predicted values in the test set are false positives?

Answer: 1443

Question 3: Consider the scenario where each false positive costs $100 and each false negative $1.

Given the stipulation, what is the cost associated with the logistic regression classifier’s performance on the test set?

  • Between $0 and $100,000
  • Between $100,000 and $200,000
  • Between $200,000 and $300,000
  • Above $300,000

Question 4: Out of all reviews in the test set that are predicted to be positive, what fraction of them are false positives? (Round to the second decimal place e.g. 0.25)

Answer: 0.04

Question 5: Based on what we learned in lecture, if we wanted to reduce this fraction of false positives to be below 3.5%, we would:

  • Discard a sufficient number of positive predictions
  • Discard a sufficient number of negative predictions
  • Increase threshold for predicting the positive class (\hat{y} = +1y^​=+1)
  • Decrease threshold for predicting the positive class (\hat{y} = +1y^​=+1)

Question 6: What fraction of the positive reviews in the test_set were correctly predicted as positive by the classifier? Round your answer to 2 decimal places.

Answer: 0.95

Question 7: What is the recall value for a classifier that predicts +1 for all data points in the test_data?

Answer: 1

Question 8: What happens to the number of positive predicted reviews as the threshold increases from 0.5 to ˀ0.9?

  • More reviews are predicted to be positive.
  • Fewer reviews are predicted to be positive.

Question 9: Consider the metrics obtained from setting the threshold to 0.5 and to 0.9.

Does the precision increase with a higher threshold?

  • Yes
  • No

Question 10: Among all the threshold values tried, what is the smallest threshold value that achieves a precision of 96.5% or better? Round your answer to 3 decimal places.

Answer: 0.838

Question 11: Using threshold = 0.98, how many false negatives do we get on the test_data? (Hint: You may use the turicreate. evaluation.confusion_matrix function implemented in Turi Create.)

Answer: 5826

Question 12: Questions 13 and 14 are concerned with the reviews that contain the word baby.

Among all the threshold values tried, what is the smallest threshold value that achieves a precision of 96.5% or better for the reviews of data in baby_reviews? Round your answer to 3 decimal places.

Answer: 0.864

Question 13: Questions 13 and 14 are concerned with the reviews that contain the word baby.

Is this threshold value smaller or larger than the threshold used for the entire dataset to achieve the same specified precision of 96.5%?

  • Larger
  • Smaller

Week 7: Machine Learning: Classification Quiz Answers

Quiz 1: Scaling to Huge Datasets & Online Learning

Question 1: (True/False) Stochastic gradient ascent often requires fewer passes over the dataset than batch gradient ascent to achieve a similar log-likelihood.

  • True
  • False

Question 2: (True/False) Choosing a large batch size results in less noisy gradients

  • True
  • False

Question 3: (True/False) The set of coefficients obtained at the last iteration represents the best coefficients found so far.

  • True
  • False

Question 4: Suppose you obtained the plot of log likelihood below after running stochastic gradient ascent.VyRs8uBtEeWBDQ73 3lhaw 70d1c456f3deba4ffa876e53e041cf7a Capture

Which of the following actions would help the most to improve the rate of convergence?

  • Increase step size
  • Decrease step size
  • Decrease batch size

Question 5: Suppose you obtained the plot of log likelihood below after running stochastic gradient ascent.Ym5JpOBtEeWBDQ73 3lhaw 8f2bb6e530dc2b481646181bf6e72a80 Capture2

Which of the following actions would help to improve the rate of convergence?

  • Increase batch size
  • Increase step size
  • Decrease step size

Question 6: Suppose it takes about 1 milliseconds to compute a gradient for a single example. You run an online advertising company and would like to do online learning via mini-batch stochastic gradient ascent. If you aim to update the coefficients once every 5 minutes, how many examples can you cover in each update? Overhead and other operations take up 2 minutes, so you only have 3 minutes for the coefficient update.

Answer: 180000

Question 7: n search for an optimal step size, you experiment with multiple step sizes and obtain the following convergence plot.bS2ZaOBtEeWufRJaRfO1AQ 8d67c76c9a6715f27ebc78fb8df13c0a Capture3

Which line corresponds to step sizes that are larger than the best? Select all that apply.

  • (1)
  • (2)
  • (3)
  • (4)
  • (5)

Question 8: Suppose you run stochastic gradient ascent with two different batch sizes. Which of the two lines below corresponds to the smaller batch size (assuming both use the same step size)?oj

  • (1)
  • (2)

Question 9: Which of the following is NOT a benefit of stochastic gradient ascent over batch gradient ascent? Choose all that apply.

  • Each coefficient step is very fast.
  • Log likelihood of data improves monotonically.
  • Stochastic gradient ascent can be used for online learning.
  • Stochastic gradient ascent can achieve higher likelihood than batch gradient ascent for the same amount of running time.
  • Stochastic gradient ascent is highly robust with respect to parameter choices.

Question 10: Suppose we run the stochastic gradient ascent algorithm described in the lecture with batch size of 100. To make 10 passes over a dataset consisting of 15400 examples, how many iterations does it need to run?

Answer: 1540

Quiz 2: Training Logistic Regression via Stochastic Gradient Ascent

Question 1:


Question 1: In Module 3 assignment, there were 194 features (an intercept + one feature for each of the 193 important words). In this assignment, we will use stochastic gradient ascent to train the classifier using logistic regression. How does the changing the solver to stochastic gradient ascent affect the number of features?

  • Increases
  • Decreases
  • Stays the same

Question 2: Recall from the lecture and the earlier assignment, the log likelihood (without the averaging term) is given by

ℓℓ(w)=i=1∑N​((1[yi​=+1]−1)wTh(xi​)−ln(1+exp(−wTh(xi​))))

whereas the average log likelihood is given by

ℓℓA​(w)=N1​i=1∑N​((1[yi​=+1]−1)wTh(xi​)−ln(1+exp(−wTh(xi​))))

How are the functions \ell\ell(\mathbf{w})ℓℓ(w) and \ell\ell_A(\mathbf{w})ℓℓA​(w) related?

  • ℓℓA​(w)=ℓℓ(w)
  • ℓℓA​(w)=(1/N)⋅ℓℓ(w)
  • ℓℓA​(w)=N⋅ℓℓ(w)
  • |ℓℓA​(w)=ℓℓ(w)−∥w∥

Question 3: Refer to the sub-section Computing the gradient for a single data point.

The code block above computed

∂wj​∂ℓi​(w)​

for j = 1 and i = 10. Is this quantity a scalar or a 194-dimensional vector?

  • A scalar
  • A 194-dimensional vector

Question 4: Refer to the sub-section Modifying the derivative for using a batch of data points.

The code block computed

s=i∑i+B​∂wj​∂ℓs​(w)​

for j = 10, i = 10, and B = 10. Is this a scalar or a 194-dimensional vector?

  • A scalar
  • A 194-dimensional vector


Question 5: For what value of B is the term

s=1∑B​∂wj​∂ℓs​(w)​

the same as the full gradient

∂wj​∂ℓ(w)​

? A numeric answer is expected for this question. Hint: consider the training set we are using now.

Answer: 47780

Question 6

For what value of batch size B above is the stochastic gradient ascent function logistic_regression_SG act as a standard gradient ascent algorithm? A numeric answer is expected for this question. Hint: consider the training set we are using now.

Answer: 47780

Question 7: When you set batch_size = 1, as each iteration passes, how does the average log likelihood in the batch change?

  • Increases
  • Decreases
  • Fluctuates

Question 8: When you set batch_size = len(feature_matrix_train), as each iteration passes, how does the average log likelihood in the batch change?

  • Increases
  • Decreases
  • Fluctuates

Question 9: Suppose that we run stochastic gradient ascent with a batch size of 100. How many gradient updates are performed at the end of two passes over a dataset consisting of 50000 data points?

Answer: 1000

Question 10: Refer to the section Stochastic gradient ascent vs gradient ascent.

In the first figure, how many passes does batch gradient ascent need to achieve a similar log likelihood as stochastic gradient ascent?

  • It’s always better
  • 10 passes
  • 20 passes
  • 150 passes or more

Question 11: Questions 11 and 12 refer to the section Plotting the log likelihood as a function of passes for each step size.

Which of the following is the worst step size? Pick the step size that results in the lowest log likelihood in the end.

  • 1e-2
  • 1e-1
  • 1e0
  • 1e1
  • 1e2

Question 12: Questions 11 and 12 refer to the section Plotting the log likelihood as a function of passes for each step size.

Which of the following is the best step size? Pick the step size that results in the highest log likelihood in the end.

  • 1e-4
  • 1e-2
  • 1e0
  • 1e1
  • 1e2

Conclusion

Hopefully, this article will be useful for you to find all the Week, final assessment, and Peer Graded Assessment Answers of Machine Learning: Classification Quiz of Coursera and grab some premium knowledge with less effort. If this article really helped you in any way then make sure to share it with your friends on social media and let them also know about this amazing training. You can also check out our other course Answers. So, be with us guys we will share a lot more free courses and their exam/quiz solutions also, and follow our Techno-RJ Blog for more updates.

533 thoughts on “Machine Learning: Classification Coursera Quiz Answers 2022 | All Weeks Assessment Answers [💯Correct Answer]”

  11. Pingback: 3upholstered

  12. I and also my buddies have been taking note of the best items on your web site while at once came up with a horrible feeling I never thanked the website owner for those secrets. All of the men ended up consequently glad to study all of them and now have certainly been loving those things. Many thanks for really being simply kind and for settling on this form of quality subject matter most people are really desperate to discover. Our own honest apologies for not saying thanks to you sooner.

    Reply

  31. When I originally commented I clicked the -Notify me when new comments are added- checkbox and now each time a comment is added I get four emails with the same comment. Is there any way you can remove me from that service? Thanks!

    Reply

  42. Wow that was strange. I just wrote an extremely long comment but after I clicked submit my comment didn’t appear. Grrrr… well I’m not writing all that over again. Anyways, just wanted to say great blog!

    Reply

  43. Just want to say your article is as astounding. The clearness to your put up is just spectacular and that i can assume you are knowledgeable on this subject. Well together with your permission allow me to seize your feed to stay up to date with approaching post. Thank you one million and please keep up the enjoyable work.

    Reply

  51. Hello there! This is my first comment here so I just wanted to give a quick shout out and say I truly enjoy reading through your posts. Can you recommend any other blogs/websites/forums that cover the same topics? Appreciate it!

    Reply

  59. Howdy! This is my first visit to your blog! We are a team of volunteers and starting a new initiative in a community in the same niche. Your blog provided us beneficial information to work on. You have done a outstanding job!

    Reply

  81. It is in reality a nice and useful piece of information. I am glad that you simply shared this useful information with us. Please stay us up to date like this. Thanks for sharing.

    Reply

  82. You could definitely see your skills in the paintings you write. The world hopes for more passionate writers like you who aren’t afraid to mention how they believe. Always follow your heart.

    Reply

  83. An interesting discussion is worth comment. I think that you should write more on this topic, it might not be a taboo subject but generally people are not enough to speak on such topics. To the next. Cheers

    Reply

  86. Great post. I was checking continuously this blog and I’m impressed! Extremely useful information specifically the last part 🙂 I care for such info a lot. I was seeking this certain information for a very long time. Thank you and good luck.

    Reply

  90. I’m impressed, I must say. Seldom do I come across a blog that’s both equally educative and engaging, and without a doubt, you’ve hit the nail on the head. The issue is something which not enough men and women are speaking intelligently about. Now i’m very happy I stumbled across this in my hunt for something concerning this.

    Reply

  92. Aw, this was a really good post. Finding the time and actual effort to produce a really good article… but what can I say… I procrastinate a lot and don’t seem to get nearly anything done.

    Reply

  96. I’m impressed, I have to admit. Rarely do I encounter a blog that’s both educative and interesting, and let me tell you, you have hit the nail on the head. The problem is something which not enough people are speaking intelligently about. Now i’m very happy I found this during my search for something relating to this.

    Reply

  101. Hello there, I do believe your website could be having internet browser compatibility issues. Whenever I look at your website in Safari, it looks fine however, if opening in Internet Explorer, it’s got some overlapping issues. I just wanted to provide you with a quick heads up! Besides that, wonderful website!

    Reply

  117. Having read this I believed it was extremely informative. I appreciate you finding the time and energy to put this article together. I once again find myself personally spending way too much time both reading and commenting. But so what, it was still worthwhile!

    Reply

  119. Having read this I thought it was really informative. I appreciate you taking the time and effort to put this information together. I once again find myself personally spending a lot of time both reading and commenting. But so what, it was still worthwhile!

    Reply

  122. Great article and right to the point. I am not sure if this is truly the best place to ask but do you people have any thoughts on where to hire some professional writers? Thanks 🙂

    Reply

  125. Greetings, I believe your blog may be having internet browser compatibility problems. When I look at your site in Safari, it looks fine but when opening in Internet Explorer, it’s got some overlapping issues. I merely wanted to provide you with a quick heads up! Apart from that, excellent blog!

    Reply

  131. Next time I read a blog, I hope that it won’t fail me as much as this one. I mean, I know it was my choice to read, however I really thought you’d have something interesting to talk about. All I hear is a bunch of crying about something that you could fix if you weren’t too busy searching for attention.

    Reply

  141. Right here is the perfect website for everyone who wants to understand this topic. You understand so much its almost hard to argue with you (not that I personally would want to…HaHa). You definitely put a fresh spin on a topic that’s been written about for a long time. Wonderful stuff, just excellent!

    Reply

  142. Unquestionably imagine that which you stated. Your favourite reason seemed to be on the internet the easiest thing to take note of. I say to you, I certainly get irked even as people consider issues that they just do not recognize about. You controlled to hit the nail upon the top and defined out the whole thing with no need side effect , other people could take a signal. Will probably be again to get more. Thanks

    Reply

  143. Having read this I thought it was very enlightening. I appreciate you spending some time and energy to put this article together. I once again find myself spending way too much time both reading and commenting. But so what, it was still worthwhile!

    Reply

  144. Oh my goodness! Amazing article dude! Thank you so much, However I am experiencing difficulties with your RSS. I don’t know why I cannot subscribe to it. Is there anybody having the same RSS issues? Anyone who knows the answer will you kindly respond? Thanx!!

    Reply

  153. You’re so awesome! I do not think I’ve truly read something like this before. So great to find someone with unique thoughts on this topic. Really.. thanks for starting this up. This web site is something that is needed on the web, someone with a little originality!

    Reply

  154. An impressive share! I’ve just forwarded this onto a friend who had been doing a little homework on this. And he in fact bought me breakfast because I found it for him… lol. So let me reword this…. Thanks for the meal!! But yeah, thanx for spending the time to discuss this subject here on your site.

    Reply

  158. This is the perfect blog for anyone who wants to find out about this topic. You understand so much its almost tough to argue with you (not that I actually will need to…HaHa). You certainly put a brand new spin on a subject that’s been written about for ages. Great stuff, just great!

    Reply

  159. whoah this blog is magnificent i love reading your articles. Keep up the good work! You know, a lot of people are searching around for this information, you can help them greatly.

    Reply

  166. After I originally left a comment I seem to have clicked the -Notify me when new comments are added- checkbox and from now on each time a comment is added I get 4 emails with the exact same comment. There has to be a way you can remove me from that service? Many thanks!

    Reply

  167. Hi there! I simply would like to offer you a huge thumbs up for your excellent information you have right here on this post. I’ll be returning to your website for more soon.

    Reply

  171. After I originally commented I seem to have clicked the -Notify me when new comments are added- checkbox and from now on each time a comment is added I get 4 emails with the same comment. There has to be a way you can remove me from that service? Cheers!

    Reply

  175. I blog often and I really thank you for your content. The article has truly peaked my interest. I will book mark your website and keep checking for new information about once per week. I subscribed to your RSS feed too.

    Reply

  176. The subsequent time I learn a blog, I hope that it doesnt disappoint me as a lot as this one. I mean, I know it was my option to read, however I actually thought youd have something interesting to say. All I hear is a bunch of whining about one thing that you might repair if you werent too busy on the lookout for attention.

    Reply

  178. After exploring a handful of the blog posts on your site, I truly appreciate your way of blogging. I book marked it to my bookmark site list and will be checking back soon. Take a look at my website too and tell me what you think.

    Reply

  179. I was wondering if you ever thought of changing the layout of your site? Its very well written; I love what youve got to say. But maybe you could a little more in the way of content so people could connect with it better. Youve got an awful lot of text for only having one or two images. Maybe you could space it out better?

    Reply

  183. I blog frequently and I really appreciate your content. The article has truly peaked my interest. I am going to bookmark your blog and keep checking for new details about once per week. I subscribed to your Feed as well.

    Reply

  184. An outstanding share! I have just forwarded this onto a co-worker who was conducting a little homework on this. And he in fact ordered me breakfast simply because I discovered it for him… lol. So allow me to reword this…. Thank YOU for the meal!! But yeah, thanks for spending time to talk about this subject here on your internet site.

    Reply

  186. You’re so cool! I don’t believe I’ve truly read a single thing like this before. So nice to find somebody with some original thoughts on this topic. Really.. many thanks for starting this up. This website is one thing that is required on the internet, someone with a little originality!

    Reply

  187. Next time I read a blog, I hope that it doesn’t disappoint me just as much as this one. I mean, Yes, it was my choice to read through, nonetheless I truly believed you’d have something interesting to talk about. All I hear is a bunch of complaining about something that you could possibly fix if you weren’t too busy searching for attention.

    Reply

  189. Your style is very unique in comparison to other folks I’ve read stuff from. I appreciate you for posting when you have the opportunity, Guess I will just bookmark this web site.

    Reply

  190. Oh my goodness! Incredible article dude! Thanks, However I am encountering problems with your RSS. I don’t know the reason why I can’t join it. Is there anybody getting identical RSS problems? Anyone that knows the answer will you kindly respond? Thanx!!

    Reply

  193. I have to thank you for the efforts you’ve put in penning this blog. I’m hoping to see the same high-grade content by you later on as well. In fact, your creative writing abilities has inspired me to get my very own website now 😉

    Reply

  194. Next time I read a blog, I hope that it won’t fail me just as much as this particular one. After all, Yes, it was my choice to read, but I really thought you would have something useful to talk about. All I hear is a bunch of crying about something you could possibly fix if you weren’t too busy looking for attention.

    Reply

  198. Having read this I thought it was rather informative. I appreciate you spending some time and effort to put this article together. I once again find myself personally spending a lot of time both reading and commenting. But so what, it was still worth it!

    Reply

  199. Hello, I do think your web site could possibly be having web browser compatibility issues. When I take a look at your site in Safari, it looks fine however, if opening in Internet Explorer, it’s got some overlapping issues. I merely wanted to give you a quick heads up! Aside from that, great blog!

    Reply

  202. Right here is the right site for anybody who wants to understand this topic. You know so much its almost tough to argue with you (not that I actually would want to…HaHa). You certainly put a brand new spin on a subject that has been written about for decades. Excellent stuff, just great!

    Reply

  205. I’m amazed, I have to admit. Seldom do I come across a blog that’s both equally educative and engaging, and let me tell you, you’ve hit the nail on the head. The issue is something too few people are speaking intelligently about. Now i’m very happy I stumbled across this during my hunt for something concerning this.

    Reply

  206. Hi! I could have sworn I’ve visited this site before but after looking at some of the articles I realized it’s new to me. Nonetheless, I’m certainly pleased I discovered it and I’ll be bookmarking it and checking back regularly!

    Reply

  207. An outstanding share! I’ve just forwarded this onto a colleague who was doing a little research on this. And he actually ordered me breakfast simply because I stumbled upon it for him… lol. So allow me to reword this…. Thank YOU for the meal!! But yeah, thanx for spending some time to discuss this topic here on your internet site.

    Reply

  211. Aw, this was an extremely good post. Taking the time and actual effort to produce a superb article… but what can I say… I hesitate a whole lot and don’t manage to get nearly anything done.

    Reply

  215. Hi! I could have sworn I’ve been to this web site before but after going through a few of the articles I realized it’s new to me. Nonetheless, I’m certainly pleased I discovered it and I’ll be bookmarking it and checking back regularly!

    Reply

  219. I must thank you for the efforts you have put in writing this site. I really hope to see the same high-grade content by you in the future as well. In fact, your creative writing abilities has motivated me to get my own, personal site now 😉

    Reply

  220. I was extremely pleased to discover this great site. I wanted to thank you for your time just for this fantastic read!! I definitely savored every bit of it and i also have you bookmarked to check out new information in your web site.

    Reply

  222. A motivating discussion is worth comment. I think that you need to write more about this subject matter, it may not be a taboo subject but typically people don’t speak about these issues. To the next! All the best!!

    Reply

  228. After exploring a number of the blog articles on your web page, I honestly appreciate your way of blogging. I saved as a favorite it to my bookmark website list and will be checking back in the near future. Take a look at my website too and let me know your opinion.

    Reply

  229. This is the perfect blog for anybody who would like to find out about this topic. You understand so much its almost hard to argue with you (not that I personally would want to…HaHa). You definitely put a brand new spin on a subject that’s been discussed for many years. Wonderful stuff, just excellent!

    Reply

  231. You are so awesome! I don’t suppose I have read through a single thing like that before. So great to find someone with a few genuine thoughts on this subject. Really.. thank you for starting this up. This site is something that is needed on the web, someone with a little originality!

    Reply

  233. I truly love your website.. Very nice colors & theme. Did you create this site yourself? Please reply back as I’m planning to create my very own website and would like to learn where you got this from or just what the theme is called. Cheers!

    Reply

  236. After I originally left a comment I seem to have clicked the -Notify me when new comments are added- checkbox and now whenever a comment is added I receive 4 emails with the exact same comment. Perhaps there is a means you can remove me from that service? Many thanks!

    Reply

  239. I have to thank you for the efforts you have put in penning this website. I really hope to view the same high-grade content by you later on as well. In fact, your creative writing abilities has inspired me to get my very own site now 😉

    Reply

  240. Having read this I thought it was rather informative. I appreciate you taking the time and effort to put this short article together. I once again find myself spending a significant amount of time both reading and leaving comments. But so what, it was still worthwhile!

    Reply

  244. After looking into a number of the blog posts on your web site, I truly like your way of blogging. I added it to my bookmark site list and will be checking back soon. Please check out my web site too and let me know what you think.

    Reply

  258. Oh my goodness! Impressive article dude! Thank you, However I am going through troubles with your RSS. I don’t know why I am unable to join it. Is there anyone else getting similar RSS problems? Anyone who knows the solution can you kindly respond? Thanx!!

    Reply

  260. I’d like to thank you for the efforts you have put in penning this site. I am hoping to check out the same high-grade content from you in the future as well. In truth, your creative writing abilities has encouraged me to get my very own blog now 😉

    Reply

  261. I blog quite often and I really appreciate your information. Your article has truly peaked my interest. I will book mark your site and keep checking for new details about once per week. I subscribed to your Feed as well.

    Reply

  263. Howdy! I could have sworn I’ve visited your blog before but after browsing through many of the posts I realized it’s new to me. Anyhow, I’m definitely delighted I discovered it and I’ll be book-marking it and checking back frequently!

    Reply

  265. You’re so awesome! I do not think I have read anything like that before. So good to discover another person with some original thoughts on this subject. Seriously.. thank you for starting this up. This website is something that is required on the internet, someone with a little originality!

    Reply

  275. You are so interesting! I don’t suppose I’ve read anything like that before. So nice to discover someone with some unique thoughts on this subject. Really.. thanks for starting this up. This site is something that is required on the internet, someone with a little originality!

    Reply

  276. Hello, I do think your web site may be having web browser compatibility issues. When I take a look at your web site in Safari, it looks fine however, when opening in Internet Explorer, it’s got some overlapping issues. I just wanted to give you a quick heads up! Other than that, wonderful blog!

    Reply

  277. After I initially left a comment I seem to have clicked on the -Notify me when new comments are added- checkbox and from now on whenever a comment is added I recieve four emails with the exact same comment. There has to be an easy method you can remove me from that service? Thanks!

    Reply

  285. Turn your cards in real-time with a real dealer. Join other players at the table and see if you can get the upper hand over the dealer. For the best casino Blackjack experience, play live as the thrills are instant. Acesse o cassino online da Betfair e descubra o mundo de opções que temos para você. Gambling can be harmful if not controlled and may lead to addiction! Analisamos meticulosamente cada casino e descobrimos o máximo de informações possível sobre o mesmo, para que os nossos visitantes saibam o que esperar antes de jogar. A plataforma online da Betfair chega completamente em português e com a possibilidade de ser acessada também via smartphone para que você possa desfrutar de toda a emoção das modalidades de cassino desde casa, a caminho do trabalho, enquanto estiver passeando, ou onde mais quiser. Nossa variedade de jogos clássicos, como roleta, blackjack e pôquer, se complementa com inovações oferecidas pelos novos jogos de cassino online, que vêm revolucionando a indústria do entretenimento. Já ouviu falar em cassino ao vivo? Sim, agora você também pode encontrar versões imersivas dos seus jogos favoritos e curtir a experiência como se estivesse em um cassino físico.
    https://trentonwwdu630942.alltdesign.com/melhores-jogo-poker-online-40091870
    O Blackjack é conhecido como um dos jogos de mesa mais elegantes em todo o mundo, mas você sabia que é o jogo que dá mais lucro aos seus jogadores? A sua reputação como jogo emocionante, com grandes prêmios é bem conhecida, porque as probabilidades de ganhar estão do lado do jogador. Quando um jogador, ou o dealer, obtiver exatamente 21 pontos como resultado das duas primeiras cartas, ganhará um blackjack. E o que isso significa? Significa que você levará o prêmio mais valioso de todos. Se você não sabe por onde começar, as 3 mesas abaixo estão entre os jogos de cassino blackjack online mais populares em nossa oferta de cassino ao vivo. Dê uma olhada e veja por si mesmo porque nossos jogadores os amam. O Blackjack é conhecido como um dos jogos de mesa mais elegantes em todo o mundo, mas você sabia que é o jogo que dá mais lucro aos seus jogadores? A sua reputação como jogo emocionante, com grandes prêmios é bem conhecida, porque as probabilidades de ganhar estão do lado do jogador.

    Reply

  290. Gdy wszystkie powyższe czynności zostaną wykonane, możesz rozpocząć grę na automatach za prawdziwe pieniądze. Przypomnijmy, że wygrane w takim przypadku środki stają się własnością graczy. Poszukiwane Zapytanie Jeśli wybierzesz odpowiednie kasyno online z niskim minimalnym depozytem, dokonanie najmniejszej możliwej wpłaty może w rzeczywistości zaoferować najlepszą wartość za Twoje pieniądze.  O ile to właśnie bonusy powitalne najczęściej przyciągają uwagę graczy szukających nowego kasyno depozyt 5 zł blik, o tyle ważne jest, aby zwrócić uwagę również na dostępne metody płatności. Warunki płatności i wypłaty mogą wpłynąć na jakość gry i zmniejszyć Twoje wygrane, jeśli występują prowizje. Niektóre depozyty 5 zł kasyno mogą być wykonane tylko z określonych systemów płatniczych.
    http://md-tour.kr/bbs/board.php?bo_table=free&wr_id=10510
    Przystąpienie do gry w kasynie betinia czy jakimkolwiek innym ma oczywiście wiele niewątpliwych zalet, ale mimo wszystko istnieją też liczne wady. Każdy powinien więc we własnym zakresie odpowiedzieć sobie na pytanie o to, czy warto rozpocząć grę i na jakie ryzyko jest się przygotowanym. Oprócz szansy na powiększenie depozytu istnieje też perspektywa utraty wpłaconych środków, dlatego zawsze trzeba zachować zdrowy rozsądek i grać odpowiedzialnie. Najlepiej z góry określić swój cel, a także maksymalny budżet. Dzięki temu zachowamy kontrolę nad swoim portfelem i będziemy potrafili racjonalnie i bez emocji podchodzić zarówno do wygranych, jak i przegranych. Cóż, teraz masz szansę w True Sheriff, grze na automatach 3D, w której odwaga jest na linii w tym epickim Westernowym automacie. Nie zapomnij przejrzeć lub sprawdzić innych ekscytujących ofert kasyn i promocji, takich jak Fast Play Casino bez depozytu.

    Reply

  291. ラメ加工【コズミックスター】8面サイコロ　HDAR50-D8 クトゥルフ神話TRPGサイコロのドラタコ 「面数」を変更することで、サイコロの出る数字の範囲を変更できます。 「サイコロの数」を変更することで、一度に振るサイコロの数を変更できます。 ラメ加工【ラビットアイ】8面サイコロ　HDAR55-D8 「サイコロの色」を変更することで、表示されるサイコロの色が変わります。 おすすめ順 | 価格順 | 新着順 「サイコロの数」を変更することで、一度に振るサイコロの数を変更できます。 ラメ加工【コズミックスター】8面サイコロ　HDAR50-D8 「サイコロの色」を変更することで、表示されるサイコロの色が変わります。 ラメ加工【コズミックスター】8面サイコロ　HDAR50-D8 「サイコロの数」を変更することで、一度に振るサイコロの数を変更できます。 「サイコロの数」を変更することで、一度に振るサイコロの数を変更できます。 クトゥルフ神話TRPGサイコロのドラタコ ラメ加工【ラビットアイ】8面サイコロ　HDAR55-D8 ラメ加工【コズミックスター】8面サイコロ　HDAR50-D8 Leiasnow 易経 易占い サイコロ 3個セット 周易 八面体 ダイス 占筮 占い 八卦 八面賽 ケース付 (白3個) カートの中に商品はありません ラメ加工【ラビットアイ】8面サイコロ　HDAR55-D8 「サイコロの数」を変更することで、一度に振るサイコロの数を変更できます。
    https://devuglysmith.kr/bbs/board.php?bo_table=free&wr_id=53043
    2016年2月頃、大阪府吹田市に住む男性ら3人が、オンラインカジノサイトを利用して賭博を行ったとして、同年3月上旬に賭博罪で京都府警察署に逮捕され、略式命令による罰金刑を受けています。報道によると、このオンラインカジノはイギリスに拠点を置いていたものの、日本人がディーラーをつとめるなど、事実上、日本人向けのオンラインカジノだったとのことです。 マニラの有名カジノの1つであるソ◯アリゾートでフロアパーソンを経験。現地でのオンラインカジノのイベント参加回数も多く、運営者とのつながりも深い。現在は「オンカジ!!」のプロジェクトマネージャーとして、オンラインカジノ側の営業担当との情報交換や様々な交渉を担当している。…続きを読む © 2014 -2023 「オンラインカジノ比較ナビ」. この記事では、海外の企業が運営するオンラインカジノを日本でプレイするとことは違法にあたるのか、日本の法律でオンラインカジノがどのように規制されているのか、皆さんが気になる疑問について徹底解説していきます。 確かに刑法第３条は、「この法律は、日本国外において次に掲げる罪を犯した日本国民に適用する。」と規定し、また賭博罪は「次に掲げる罪」に含まれていないため、賭博が合法な国でこれを行っても賭博罪は適用されません。他方で、日本国内でオンラインカジノをした場合に、「日本国外において」これを行ったと一概にはいえないでしょう。そのため、仮に海外で合法的なライセンスを取得したオンラインカジノであったとしても、これを日本で利用した場合に、賭博罪を適用しないという規定には必ずしもなっていないのです。

    Reply

  292. Caribbean Stud Poker combines elements of blackjack and poker to form a quick and exciting table game. Many casino regulars can’t get enough of the quick rounds, low house edge, simple rules, and escalating payout table. Thinking about finding a place to play Caribbean Stud online, but aren’t too sure of how to play the game? You’re in the right place. This review of the fun and simple to learn casino poker variant will provide all the information you need to understand the game, plus a free demo to try it out for yourself. Just need a recommended casino? Then head to Casumo and get started with real money today. You can play online slots and the other casino games for free! Enjoy all our fun online games in our free games, demo mode. When you are ready to gamble slots for real money to win real money jackpots you can go into our casino cashier, on our Banking page and make your first deposit, even a minimum deposit and grab yourself a bankroll booster with any one of our bonus codes and bonus offers. Any way you play games, you’re bound to have a great time playing online games at Sloto’Cash and for sure our random number generators ensure fair play with no house edge and an excellent payout percentage
    https://caidenwywt529648.theblogfairy.com/21729389/what-poker-site-has-the-best-freerolls
    Silakan pilih salah satu pilihan metode transaksi deposit yang tersedia dan pastikan memiliki modal yang cukup untuk melakukan deposit di situs iniin poker88 resmi dan terpercaya Pokerhoki88. Setelah itu Anda sudah bisa login ke dalam permainan lalu memilih pilihan game yang diinginkan mulai dari Poker Online, ceme online dan lainnya. Pelajari trik dan strateginya juga agar menang mudah. Thanks glad this article helped! Shortstack poker is a completely different game. I was assuming full stacks throughout this article since this is how the vast majority of online games (and even most live) are. I am by no means a shortstack expert so I wouldn’t want to comment on this hypothetical game. This type of hand is very common, especially in online poker. When you allow that many players to come in for the minimum bet, you’re going to see all sorts of garbage hands stay with the hand. You can’t even really fault the guy with the 5-3 – he’s getting great pot odds to see a flop.

    Reply

  293. Tottenham started the season brilliantly, but since the World Cup, the club have plummeted down the table and they could miss out on European football all-together. SoccerPots is licenced and regulated in Great Britain by the Gambling Commission under licence number 54918 Following that, the team will travel to Israel to compete in the I-Tech Cup against former manager Jose Mourinho and Roma, the inaugural Europa Conference League winners. This website uses cookies so that we can provide you with the best user experience possible. Cookie information is stored in your browser and performs functions such as recognising you when you return to our website and helping our team to understand which sections of the website you find most interesting and useful.
    https://www.tae-chang.biz/bbs/board.php?bo_table=free&wr_id=15828
    Former Orlando Pirates and Mamelodi Sundowns midfielder Teko Modise says it is more important to win trophies with the … “He’s going to call me and update me. He knows what I want. For now, I want to get football out of my head. I want to give my time to my wife and daughter [five-year-old. I want to give them time because, with our busy schedule, we don’t have much time with families.” Valuing a player is currently an arbitrary process where teams, players and representatives simply estimate what they are worth. Together with SciSports, FT aims to use advanced football data, current and future skill level as well as contract length and other details to create our unique internal calculation. From there we derive our own valuation for each and every player in the world’s major football competitions.

    Reply

  295. As with bingo sites, most poker sites are networked and therefore the same ‘tables’ exist on numerous sites. This allows the pots to grow and for things like Bad Beat Jackpots, where a player loses to a brilliant hand despite having a good hand themselves, to become well-sized. The other thing poker sites share with bingo sites is the ability to indulge in slot play between games. As with bingo sites, most poker sites are networked and therefore the same ‘tables’ exist on numerous sites. This allows the pots to grow and for things like Bad Beat Jackpots, where a player loses to a brilliant hand despite having a good hand themselves, to become well-sized. The other thing poker sites share with bingo sites is the ability to indulge in slot play between games. Comparing bookmakers by individual criteria offers users quick answers about specific issues (e.g. signup bonus size). Our comparison tables compare online bookmakers by about 40 criteria.
    https://list-wiki.win/index.php?title=$30_no_deposit_bonus_casino
    When choosing a casino from our best casino online UK lists, you should always pick the one that offers top online casino bonuses with the lowest wagering requirements. Some of the top casinos have wager-free casino bonus offers for UK players. This means that if you get lucky and win, you can keep your winnings and withdraw the money right away. We’ve highlighted our criteria for choosing the top sites and the offerings, including sports betting you can find at the best online casino UK. Combined with our expert tips for playing at online UK casinos, you’re well on your way to finding that perfect gambling haven. You can email the site owner to let them know you were blocked. Please include what you were doing when this page came up and the Cloudflare Ray ID found at the bottom of this page.

    Reply

  297. FOX Bet’s online casino isn’t the best straight casino option, but if you’re looking for a good casino-sportsbook combo in PA, it’s your best bet. Payout percentage, or return-to-player percentage (RTP), is the percentage of money put into a casino game that the game is expected to pay out over time. The flipside of this is called house edge and represents the percentage of money put into a casino game that the casino will keep as profit. When choosing a real money online casino and making a deposit, keep a few things in mind. Let’s look at a checklist of factors our experts use to pick trustworthy real money casinos. Currently, real money online casino gambling is legal in six U.S. states: Connecticut, Delaware, Michigan, New Jersey, Pennsylvania, and West Virginia. Many states are due to vote on legislation relating to the legalization of sports betting in 2023, but it remains to be seen whether real money online casino gambling will be rolled out in further states in the near future.
    http://tecmall.co.kr/bbs/board.php?bo_table=free&wr_id=6266
    Betonline.ag is a brilliant instant withdrawal online casino for American sports fans. It has grown a sizeable following in recent years, attracting players that love American sports. The site is professional and diverse, offering fun betting across the board. BetOnline is a great place to play if you are looking for an online casino fast payout. When you win big at an online casino, it’s understandable that you want to get your money in a fast and barrier-free way. The next section explains the best ways to access casino payouts, what to expect for turnaround times and methods you can use to get your casino payouts faster. The payouts you get from online casinos vary from site to site and they can be wildly different. Some casinos pay out at close to 98%, while others have payouts as low as 94%. 

    Reply

  305. オンラインカジノ業界に豊富な経験を持つ専門家が厳選した日本人プレーヤーにおすすめのオンラインスロットについて、2021年最新のTOP10を徹底解説いたします。スロットの選び方についても解説しているので、この記事を読めばあなたに合ったスロットを見極められるようになりますよ。 オンラインカジノの還元率が高い理由は、店舗がないからです。 少し懐かしい感じのするビデオスロットですが、還元率は驚きの99％！オンラインスロット界トップクラスの高さを誇るのがメガジョーカーです。 そこでオンラインカジノのスロットと日本のパチスロの違いと、オンラインスロットの選び方、おすすめ機種3つを解説していきます。 オンラインスロットには、配当が2〜3倍と低い代わりに子役出現率が高いものや、最低の配当が20〜30と高い代わりに子役出現率が低いものなど様々あります。 オンラインカジノには、多くの種類のスロットが用意されておりますが、それぞれペイアウト率は異なります。特に、ペイアウト率９８%を超えるスロットは、一般的にボラティリティが低く、少ない資金で長く遊べますので、初心者にもおすすめ機種となります。 そのため、還元率（ペイアウト率）が高いから勝てるという幻想は一旦捨てて、どのオンラインカジノでプレイするのか、そしてプレイするのかもしっかりと考える必要があります。
    http://exhibition.busan.kr/bbs/board.php?bo_table=free&wr_id=93539
    PayPalのセキュリティ対策 もしもペイパル対応カジノが増えれば、常日頃からペイパルを利用している層をオンラインカジノに向けることができるかもしれません。そのためPayPal対応オンラインカジノの登場は、オンラインカジノ業界も期待していることでしょう。その点では今後、日本のオンラインカジノ　ペイパル側が何らかの魅力や他の送金サービスとの差別化が鍵を握りそうです。 多くの人は、PayPal がオンラインカジノの最良の支払い方法であると信じています。これは主に、銀行がギャンブル関連の取引を制限することを心配することなく、PayPal を使用すると資金を簡単に追加および削除できるためです。 多くの人は、PayPal がオンラインカジノの最良の支払い方法であると信じています。これは主に、銀行がギャンブル関連の取引を制限することを心配することなく、PayPal を使用すると資金を簡単に追加および削除できるためです。 リアル カジノ パチンコ地獄少女事業再生支援ファンド：月間売上が30％以上減少した場合に対象、バーチャル カード オンカジ デジタル染色オンラインカジノリアルマネーシンガポールによるユニークなパターンでプリントされた2WAYバックパック1128まで販売です。高橋渉 rtp とは カジノ 組織は人の手と頭脳（＝スキル）だけを見て採用すると考えてよいのでしょうか。

    Reply

  319. Major slot machine manufacturers who supply games to live casinos also distribute their games online. A check of online games will find slots from International Game Technology, Scientific Games, Aristocrat Technologies and other familiar game makers. SlotLoki It was metallic—about the size of a penny slot-machine bar of chocolate, if you remember back that far. Word of the Day Carousel High Quality Kiddie Swing Rides Coin Operated Game Machine Kids Carousel Game Machine Slot Machines Horbay, however, considers machine gaming the most addictive, and yet least regulated, form of gambling. Casinos would never get away with using loaded dice or with stacking a deck of cards, he says, which are akin to the deceptive features of gaming machines. He proposes that the government phase out virtual reel mapping (already illegal in Australia and New Zealand) and unbalanced reels. Gamblers would also suffer fewer problems, he says, if electronic gaming machines displayed how much they stand to lose on each bet.
    https://echobookmarks.com/story15078387/best-betonline-slots-reddit
    Once you have achieved this, best real winning pokies australia and they were sometimes busted by law enforcement. Thunderkick is a public company that employs around 50 people, you can say the more precise and the more interesting and generous goes even more on the less. Grab random wilds, 3 row and 25 pay lines slot lets check it out. When register in July 2023, but looks are deceiving. Catawba casino nz these bonus features include the Beware The Wolf feature, wire transfers. Local operators could not offer real money casino services to citizens but offshore operators still had the privilege of providing casino games to players since the restriction was meant for just local operators. This gave gaming authorities like UK gambling commission and Malta authorities a wider and bigger market to explore with both commissions becoming the most dominant in the country due to the remarkable casinos they have under their licence.

    Reply

  329. where can you get amoxicillin: [url=https://amoxicillins.com/#]can we buy amoxcillin 500mg on ebay without prescription[/url] amoxicillin online no prescription

    Reply

  372. india pharmacy mail order [url=https://indiapharmacy.cheap/#]indian pharmacy online[/url] buy medicines online in india

    Reply

  377. To understand actual rumour, dog these tips:

    Look for credible sources: https://www.naucat.com/images/jkk/?when-conveying-bad-or-distasteful-news-to-the.html. It’s high-ranking to safeguard that the news source you are reading is respected and unbiased. Some examples of virtuous sources categorize BBC, Reuters, and The Fashionable York Times. Announce multiple sources to pick up a well-rounded understanding of a isolated low-down event. This can help you return a more ideal facsimile and keep bias. Be cognizant of the perspective the article is coming from, as set good telecast sources can be dressed bias. Fact-check the dirt with another fountain-head if a news article seems too lurid or unbelievable. Till the end of time fetch sure you are reading a advised article, as news can substitute quickly.

    By following these tips, you can fit a more in the know dispatch reader and more wisely be aware the world around you.

    Reply

  378. To announce actual rumour, adhere to these tips:

    Look in behalf of credible sources: https://pragatiphilly.com/wp-content/pgs/?what-happened-to-roxanne-evans-news-12.html. It’s eminent to safeguard that the newscast origin you are reading is reliable and unbiased. Some examples of virtuous sources subsume BBC, Reuters, and The Different York Times. Read multiple sources to get back at a well-rounded aspect of a particular statement event. This can support you get a more complete paint and keep bias. Be in the know of the perspective the article is coming from, as set reputable report sources can compel ought to bias. Fact-check the low-down with another commencement if a news article seems too lurid or unbelievable. Till the end of time fetch persuaded you are reading a known article, as tidings can change-over quickly.

    By following these tips, you can evolve into a more aware of rumour reader and more intelligent apprehend the world around you.

    Reply

  388. Totally! Declaration info portals in the UK can be unendurable, but there are scads resources available to cure you think the unmatched in unison for the sake of you. As I mentioned in advance, conducting an online search for https://lodgenine.co.uk/art/jennifer-griffin-s-age-fox-news-anchor-s-birthdate.html “UK scuttlebutt websites” or “British intelligence portals” is a vast starting point. Not no more than purposefulness this chuck b surrender you a encompassing list of communication websites, but it determination also lend you with a improved understanding of the in the air news view in the UK.
    Aeons ago you secure a file of embryonic news portals, it’s prominent to evaluate each sole to determine which best suits your preferences. As an exempli gratia, BBC Intelligence is known for its intention reporting of information stories, while The Trustee is known representing its in-depth analysis of partisan and social issues. The Independent is known representing its investigative journalism, while The Times is known for its work and wealth coverage. During entente these differences, you can pick out the rumour portal that caters to your interests and provides you with the newsflash you call for to read.
    Additionally, it’s worth looking at close by expos‚ portals for specific regions within the UK. These portals yield coverage of events and good copy stories that are applicable to the area, which can be specially accommodating if you’re looking to keep up with events in your neighbourhood pub community. In behalf of instance, provincial dope portals in London contain the Evening Standard and the Londonist, while Manchester Evening Scuttlebutt and Liverpool Echo are stylish in the North West.
    Inclusive, there are many statement portals readily obtainable in the UK, and it’s important to do your digging to see the one that suits your needs. At near evaluating the different news portals based on their coverage, style, and position statement perspective, you can choose the a person that provides you with the most relevant and engrossing news stories. Meet luck with your search, and I anticipate this bumf helps you discover the correct news portal since you!

    Reply

  389. Positively! Conclusion expos‚ portals in the UK can be crushing, but there are tons resources ready to help you think the unexcelled in unison for the sake of you. As I mentioned already, conducting an online search representing https://astraseal.co.uk/wp-content/art/how-old-is-jesse-watters-from-fox-news.html “UK scuttlebutt websites” or “British intelligence portals” is a pronounced starting point. Not no more than purposefulness this hand out you a thorough shopping list of news websites, but it will also provide you with a punter pact of the coeval news prospect in the UK.
    In the good old days you be enduring a list of imminent account portals, it’s prominent to estimate each sole to influence which upper-class suits your preferences. As an example, BBC Advice is known in place of its disinterested reporting of intelligence stories, while The Keeper is known for its in-depth analysis of political and group issues. The Independent is known for its investigative journalism, while The Times is known for its work and finance coverage. Not later than concession these differences, you can pick out the news portal that caters to your interests and provides you with the rumour you want to read.
    Additionally, it’s quality all things neighbourhood pub despatch portals with a view specific regions within the UK. These portals yield coverage of events and dirt stories that are applicable to the area, which can be exceptionally cooperative if you’re looking to keep up with events in your town community. For exemplar, provincial communiqu‚ portals in London contain the Evening Canon and the Londonist, while Manchester Evening Hearsay and Liverpool Reflection are hot in the North West.
    Inclusive, there are diverse bulletin portals accessible in the UK, and it’s important to do your digging to find the united that suits your needs. By means of evaluating the unalike news broadcast portals based on their coverage, style, and essay standpoint, you can decide the song that provides you with the most related and interesting low-down stories. Meet destiny with your search, and I hope this bumf helps you discover the practised expos‚ portal since you!

    Reply

  421. Anna Berezina is a highly gifted and famend artist, known for her distinctive and fascinating artworks that by no means fail to go away an enduring impression. Her work beautifully showcase mesmerizing landscapes and vibrant nature scenes, transporting viewers to enchanting worlds full of awe and wonder.

    What units [url=http://plusgestio.com/pag/berezina-a_11.html]Berezina Anna[/url] apart is her distinctive attention to detail and her remarkable mastery of color. Each stroke of her brush is deliberate and purposeful, creating depth and dimension that bring her paintings to life. Her meticulous method to capturing the essence of her topics allows her to create really breathtaking artistic endeavors.

    Anna finds inspiration in her travels and the beauty of the natural world. She has a deep appreciation for the awe-inspiring landscapes she encounters, and this is evident in her work. Whether it’s a serene seaside at sundown, an impressive mountain vary, or a peaceable forest full of vibrant foliage, Anna has a outstanding ability to capture the essence and spirit of those locations.

    With a singular creative style that mixes elements of realism and impressionism, Anna’s work is a visible feast for the eyes. Her work are a harmonious mix of exact details and soft, dreamlike brushstrokes. This fusion creates a captivating visual experience that transports viewers right into a world of tranquility and wonder.

    Anna’s talent and creative vision have earned her recognition and acclaim within the art world. Her work has been exhibited in prestigious galleries across the globe, attracting the eye of artwork fanatics and collectors alike. Each of her pieces has a way of resonating with viewers on a deeply private level, evoking emotions and sparking a sense of reference to the natural world.

    As Anna continues to create stunning artworks, she leaves an indelible mark on the world of art. Her capability to seize the wonder and essence of nature is truly remarkable, and her work function a testament to her artistic prowess and unwavering ardour for her craft. Anna Berezina is an artist whose work will proceed to captivate and inspire for years to come back..

    Reply

  436. zithromax 500 price [url=http://azithromycinotc.store/#]can you buy zithromax over the counter in canada[/url] where can i buy zithromax medicine

    Reply