Course Content

Probability Theory Basics

1. Basic Concepts of Probability Theory

Stochastic Experiment and Random Event Probability and It's Properties Geometrical Probability Challenge: Solving the Task Using Geometric Probability Independence and Incompatibility of Random Events Conditional Probability

2. Probability of Complex Events

Inclusion-Exclusion Principle Challenge: Solving the Task Using Inclusion-Exclusion Principle The Multiplication Rule of Probability Law of Total Probability Bayes' Theorem Challenge: Solving the Task Using Bayes' Theorem

3. Commonly Used Discrete Distributions

Binomial Distribution Challenge: Solving Task Using Binomial Distribution Multinomial Distribution Geometric Distribution Poisson Distribution Challenge: Solving Task Using Poisson Distribution

4. Commonly Used Continuous Distributions

Continuous Uniform Distribution Exponential Distribution Challenge: Solving Task Using Exponential Distribution Gaussian Distribution Challenge: Solving Task Using Gaussian Distribution

5. Covariance and Correlation

What is Covariance?What is Correlation?Challenge: Solving the Task Using Correlation

Challenge: Solving the Task Using Correlation

Task

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One of the most important tasks in machine learning is building a linear regression model (you can find more information in the Linear Regression with Python course).

Since we use a linear function in this model, we can use the correlation between features and target to indicate how significant a particular feature is for this model.

We will use the 'Heart Disease Dataset' now: it contains 14 features, including the predicted attribute, which refers to the presence of heart disease in the patient. Your task is to calculate attribute importance using correlation:

Calculate correlations between features and target.
Print these correlations in ascending order.

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Section 5. Chapter 3

Challenge: Solving the Task Using Correlation

Task

Swipe to show code editor

One of the most important tasks in machine learning is building a linear regression model (you can find more information in the Linear Regression with Python course).

Since we use a linear function in this model, we can use the correlation between features and target to indicate how significant a particular feature is for this model.

Calculate correlations between features and target.
Print these correlations in ascending order.

Switch to desktop for real-world practiceContinue from where you are using one of the options below

Everything was clear?

Thanks for your feedback!

Section 5. Chapter 3

Challenge: Solving the Task Using Correlation

Task

Swipe to show code editor

One of the most important tasks in machine learning is building a linear regression model (you can find more information in the Linear Regression with Python course).

Since we use a linear function in this model, we can use the correlation between features and target to indicate how significant a particular feature is for this model.

Calculate correlations between features and target.
Print these correlations in ascending order.

Switch to desktop for real-world practiceContinue from where you are using one of the options below

Everything was clear?

Thanks for your feedback!

Task

Swipe to show code editor

One of the most important tasks in machine learning is building a linear regression model (you can find more information in the Linear Regression with Python course).

Since we use a linear function in this model, we can use the correlation between features and target to indicate how significant a particular feature is for this model.

Calculate correlations between features and target.
Print these correlations in ascending order.

Switch to desktop for real-world practiceContinue from where you are using one of the options below

Section 5. Chapter 3

Switch to desktop for real-world practiceContinue from where you are using one of the options below