## Description

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**

Topics

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**

Topics

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**

Topics

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**

Topics

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**

Topics

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**

Topics

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

Topics

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**

Topics

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**

Topics

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**

Topics

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**

Topics

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.

### Pictures

### Sample video

### Installation guide

After Extract, view with your favorite Player.

Subtitle: None

Quality: 720p

### download link

##### Password file(s): www.downloadly.ir

### Size

14.6 GB

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