Conteúdo do Curso

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

Tarefa

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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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Seção 5. Capítulo 3

Challenge: Solving the Task Using Correlation

Tarefa

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.

Mude para o desktop para praticar no mundo realContinue de onde você está usando uma das opções abaixo

Tudo estava claro?

Obrigado pelo seu feedback!

Seção 5. Capítulo 3

Mude para o desktop para praticar no mundo realContinue de onde você está usando uma das opções abaixo