Probability and Statistics for Machine Learning Live Lessons

Probability and Statistics for Machine Learning LiveLessons (Video Training)


Probability and Statistics for Machine Learning LiveLessons (Video Training), is a practical training course on probability theory and statistical modeling with a focus on machine learning applications.

What you will learn in the Probability and Statistics for Machine Learning LiveLessons (Video Training) course:

  • Understand types of variables and statistical distributions for data display
  • Applying information theory to quantify the proportion of valuable signs that appear in a probability distribution.
  • Understanding the basics of frequentist and Bayesian statistics
  • Using historical data to predict the future using regression models

Course details

Publisher: InformIT
Instructors: Jon Krohn
English language
Education level: Intermediate
Number of courses: 90
Duration: 9 hours

Course topics:

Introduction to Probability and Statistics for Machine Learning (Machine Learning Foundations) LiveLessons

Lesson 1: Introduction to Probability
1.1 Orientation to the Machine Learning Foundations Series
1.2 What Probability Theory Is
1.3 Events and Sample Spaces
1.4 Multiple Observations
1.5 Factorials and Combinatorics
1.6 Exercises
1.7 The Law of Large Numbers and the Gambler’s Fallacy
1.8 Probability Distributions in Statistics
1.9 Bayesian versus Frequentist Statistics
1.10 Applications of Probability to Machine Learning

Lesson 2: Random Variables
2.1 Discrete and Continuous Variables
2.2 Probability Mass Functions
2.3 Probability Density Functions
2.4 Exercises on Probability Functions
2.5 Expected Value
2.6 Exercises on Expected Value

Lesson 3: Describing Distributions
3.1 The Mean, a Measure of Central Tendency
3.2 Medians
3.3 Modes
3.4 Quantiles: Percentiles, Quartiles, and Deciles
3.5 Box-and-Whisker Plots
3.6 Variance, a Measure of Dispersion
3.7 Standard Deviation
3.8 Standard Error
3.9 Covariance, a Measure of Relatedness
3.10 Correlation

Lesson 4: Relationships Between Probabilities
4.1 Joint Probability Distribution
4.2 Marginal Probability
4.3 Conditional Probability
4.4 Exercises
4.5 Chain Rule of Probabilities
4.6 Independent Random Variables
4.7 Conditional Independence

Lesson 5: Distributions in Machine Learning
5.1 Uniform
5.2 Gaussian: Normal and Standard Normal
5.3 The Central Limit Theorem
5.4 Log-Normal
5.5 Exponential and Laplace
5.6 Binomial and Multinomial
5.7 Poisson
5.8 Mixture Distributions
5.9 Preprocessing Data for Model Input
5.10 Exercises

Lesson 6: Information Theory
6.1 What Information Theory Is
6.2 Self-Information, Nats, and Bits
6.3 Shannon and Differential Entropy
6.4 Kullback-Leibler Divergence and Cross-Entropy

Lesson 7: Introduction to Statistics

7.1 Applications of Statistics to Machine Learning
7.2 Review of Essential Probability Theory
7.3 z-scores and outliers
7.4 Exercises on z-scores
7.5 p-values
7.6 Exercises on p-values

Lesson 8: Comparing Means
8.1 Single-Sample t-Tests and Degrees of Freedom
8.2 Independent t-Tests
8.3 Paired t-Tests
8.4 Applications to Machine Learning
8.5 Exercises
8.6 Confidence Intervals
8.7 ANOVA: Analysis of Variance

Lesson 9: Correlation
9.1 The Pearson Correlation Coefficient
9.2 R-Squared Coefficient of Determination
9.3 Correlation versus Causation
9.4 Correcting for Multiple Comparisons

Lesson 10: Regression
10.1 Independent versus Dependent Variables
10.2 Linear Regression to Predict Continuous Values
10.3 Fitting a Line to Points on a Cartesian Plane
10.4 Linear Least Squares Exercise
10.5 Ordinary Least Squares
10.6 Categorical “Dummy” Features
10.7 Logistic Regression to Predict Categories
10.8 Open-Ended Exercises

Lesson 11: Bayesian Statistics
11.1 Machine Learning versus Frequentist Statistics
11.2 When to use Bayesian Statistics
11.3 Prior Probabilities
11.4 Bayes’ Theorem
11.5 Resources for Further Study of Probability and Statistics

Summary of Probability and Statistics for Machine Learning (Machine Learning Foundations) LiveLessons

Course prerequisites:

Mathematics: Familiarity with secondary school-level mathematics will make it easier for you to follow along with the class. If you are comfortable dealing with quantitative information–such as understanding charts and rearranging simple equations–then you should be well-prepared to follow along with all of the mathematics.
Programming: All code demos will be in Python so experience with it or another object-oriented programming language would be helpful for following along with the hands-on examples.


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