Statistics For Machine Learning Course
The Statistics For Machine Learning course discusses various topics of statistics. It also discusses how to solve various statistical problems using Python. The Statistics For Machine Learning course is good for students who want to learn statistics. This course is also good for students and professionals who want to learn statistics for machine learning.
Details of Section 1
1.1 What are random variables? (video)
1.2 What are numerical, categorical, nominal, ordinal, and dichotomous variables? (video)
1.3 What are dependent and independent variables? (video)
1.4 What are Probability Mass Functions (PMF) and Probability Density Functions (PDF)? (video)
1.5 What are Cumulative Distribution Function (CDF) and Percent Point Function or Quantile function? (video)
1.6 Mathematical Expectation Explained (video)
1.7 What are joint, marginal, and conditional probabilities? (video)
1.8 How to generate a random integer in Python?
1.9 How to generate a random floating point number in Python?
1.10 How to generate random values from the exponential distribution?
1.11 How to generate random values from the Gaussian distribution?
1.12 How to generate random values from the uniform distribution?
1.13 How to plot Gaussian distribution using Python?
1.14 How to generate random numbers from normal distribution?
1.15 How to randomly shuffle a list using Python?
1.16 How to generate a random sample from a list using Python?
1.17 How to randomly select a number from a list of numbers in Python?
1.2 What are numerical, categorical, nominal, ordinal, and dichotomous variables? (video)
1.3 What are dependent and independent variables? (video)
1.4 What are Probability Mass Functions (PMF) and Probability Density Functions (PDF)? (video)
1.5 What are Cumulative Distribution Function (CDF) and Percent Point Function or Quantile function? (video)
1.6 Mathematical Expectation Explained (video)
1.7 What are joint, marginal, and conditional probabilities? (video)
1.8 How to generate a random integer in Python?
1.9 How to generate a random floating point number in Python?
1.10 How to generate random values from the exponential distribution?
1.11 How to generate random values from the Gaussian distribution?
1.12 How to generate random values from the uniform distribution?
1.13 How to plot Gaussian distribution using Python?
1.14 How to generate random numbers from normal distribution?
1.15 How to randomly shuffle a list using Python?
1.16 How to generate a random sample from a list using Python?
1.17 How to randomly select a number from a list of numbers in Python?
Details of Section 2
2.1 What are mean, median, and mode in statistics? (video)
2.2 How to calculate the mean, median and mode of data in Python?
2.3 What are unimodal, bimodal, and multimodal data? (video)
2.4 What are variance and standard deviation? (video)
2.5 How to calculate the variance and the standard deviation of data using Python?
2.6 What are quartiles and Inter Quartile Ranges (IQR) in a dataset? (video)
2.7 How to calculate quartiles and Inter Quartile Range (IQR) in Python?
2.8 What are outliers in a dataset? (video)
2.9 What is covariance in statistics? (video)
2.10 Pearson R Correlation Coefficient Explained (video)
2.11 How to calculate the covariance matrix using Python?
2.12 How to calculate Pearson R Correlation Coefficient using Python?
2.13 How to calculate correlation matrix using Python?
2.14 How to calculate the five number summary in Python?
2.2 How to calculate the mean, median and mode of data in Python?
2.3 What are unimodal, bimodal, and multimodal data? (video)
2.4 What are variance and standard deviation? (video)
2.5 How to calculate the variance and the standard deviation of data using Python?
2.6 What are quartiles and Inter Quartile Ranges (IQR) in a dataset? (video)
2.7 How to calculate quartiles and Inter Quartile Range (IQR) in Python?
2.8 What are outliers in a dataset? (video)
2.9 What is covariance in statistics? (video)
2.10 Pearson R Correlation Coefficient Explained (video)
2.11 How to calculate the covariance matrix using Python?
2.12 How to calculate Pearson R Correlation Coefficient using Python?
2.13 How to calculate correlation matrix using Python?
2.14 How to calculate the five number summary in Python?
Details of Section 3
3.1 What are z-scores in statistics? (video)
3.2 How to calculate z-scores using Python?
3.3 What is Chebyshev’s Inequality? (video)
3.4 The Law of Large Numbers in Statistics (video)
3.5 What is the Central Limit Theorem? (video)
3.6 Verifying the Central Limit Theorem Using Python
3.7 Descriptive Statistics vs. Inferential Statistics (video)
3.8 Population Parameters vs. Sample Statistics (video)
3.9 What are sampling errors in statistics? (video)
3.2 How to calculate z-scores using Python?
3.3 What is Chebyshev’s Inequality? (video)
3.4 The Law of Large Numbers in Statistics (video)
3.5 What is the Central Limit Theorem? (video)
3.6 Verifying the Central Limit Theorem Using Python
3.7 Descriptive Statistics vs. Inferential Statistics (video)
3.8 Population Parameters vs. Sample Statistics (video)
3.9 What are sampling errors in statistics? (video)
Details of Section 4
4.1 What is hypothesis testing? (video)
4.2 What is sampling distribution? (video)
4.3 What are the significance level and the p-value in statistics? (video)
4.4 Type 1 and Type 2 Errors in Hypothesis Testing (video)
4.5 One-Tailed vs. Two-Tailed Hypothesis Tests (video)
4.6 What are critical regions and acceptance regions in hypothesis testing? (video)
4.7 What are degrees of freedom in statistics? (video)
4.8 One-sample Z-test using Python
4.9 One-sample t-test using Python
4.10 Two-sample t-test using Python
4.11 Paired t-test using Python
4.12 How to generate the t distribution graph in Python?
4.13 How to calculate p-value from t-statistic using Python?
4.14 How to calculate the critical value for a t-test using Python?
4.15 One-way ANOVA Test Using Python
4.16 Tukey’s Test For Post-Hoc Analysis Using Python
4.17 How to generate F-distribution graph using Python?
4.18 How to calculate the p-value from the test statistic in an F-test?
4.19 How to calculate the critical value for an F-test using Python?
4.2 What is sampling distribution? (video)
4.3 What are the significance level and the p-value in statistics? (video)
4.4 Type 1 and Type 2 Errors in Hypothesis Testing (video)
4.5 One-Tailed vs. Two-Tailed Hypothesis Tests (video)
4.6 What are critical regions and acceptance regions in hypothesis testing? (video)
4.7 What are degrees of freedom in statistics? (video)
4.8 One-sample Z-test using Python
4.9 One-sample t-test using Python
4.10 Two-sample t-test using Python
4.11 Paired t-test using Python
4.12 How to generate the t distribution graph in Python?
4.13 How to calculate p-value from t-statistic using Python?
4.14 How to calculate the critical value for a t-test using Python?
4.15 One-way ANOVA Test Using Python
4.16 Tukey’s Test For Post-Hoc Analysis Using Python
4.17 How to generate F-distribution graph using Python?
4.18 How to calculate the p-value from the test statistic in an F-test?
4.19 How to calculate the critical value for an F-test using Python?
Details of Section 5
5.1 Chi-square goodness-of-fit tests using Python
5.2 The Chi-Square Test of Independence using Python
5.3 How to generate the chi-square distribution graph in Python?
5.4 How to calculate p-value from chi-square statistic using Python?
5.5 How to calculate the critical value for a chi-square test using Python?
5.6 How to rank data using Python?
5.7 The test of randomness – Run test using Python
5.8 Wilcoxon matched pair sign test using Python
5.9 Mann-Whitney U Test using Python
5.10 Spearman Rank Correlation Test using Python
5.11 Kruskal-Wallis Test using Python
5.2 The Chi-Square Test of Independence using Python
5.3 How to generate the chi-square distribution graph in Python?
5.4 How to calculate p-value from chi-square statistic using Python?
5.5 How to calculate the critical value for a chi-square test using Python?
5.6 How to rank data using Python?
5.7 The test of randomness – Run test using Python
5.8 Wilcoxon matched pair sign test using Python
5.9 Mann-Whitney U Test using Python
5.10 Spearman Rank Correlation Test using Python
5.11 Kruskal-Wallis Test using Python
Details of Section 6
Details of Section 7
7.1 Simple Linear Regression using Python
7.2 Multiple Linear Regression using Python
7.3 Polynomial Regression using Python
7.4 Ridge Regression using Python
7.5 Lasso Regression using Python
7.6 ElasticNet Regression using Python
7.7 Calculating Mean Absolute Error, Mean Squared Error (MSE), and Root Mean Squared Error (RMSE) using Python
7.8 What is R-squared and how to calculate it in sklearn?
7.2 Multiple Linear Regression using Python
7.3 Polynomial Regression using Python
7.4 Ridge Regression using Python
7.5 Lasso Regression using Python
7.6 ElasticNet Regression using Python
7.7 Calculating Mean Absolute Error, Mean Squared Error (MSE), and Root Mean Squared Error (RMSE) using Python
7.8 What is R-squared and how to calculate it in sklearn?
Details of Section 8
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