A Practical Guide to Machine Learning with Python

The goal is to introduce the audience to some fundamental machine learning algorithms with implementation.

Beginner

57 Lessons

72h 30min

Certificate of Completion

The goal is to introduce the audience to some fundamental machine learning algorithms with implementation.

AI-POWERED

Explanations

AI-POWERED

Explanations

This course includes

108 Playgrounds

12 Quizzes

This course includes

108 Playgrounds

12 Quizzes

Course Overview

This course teaches you how to code basic machine learning models. The content is designed for beginners with general knowledge of machine learning, including common algorithms such as linear regression, logistic regression, SVM, KNN, decision trees, and more. If you need a refresher, we have summarized key concepts from machine learning, and there are overviews of specific algorithms dispersed throughout the course.

What You'll Learn

Learn fundamental principles and techniques of machine learning.

Understand the benefits and drawbacks of a variety of common machine learning methods.

The key premise of the course is to teach you how to code basic machine learning models.

Develop skills with using machine learning tools to solve real-world issues.

Learn the fundamentals of different learning paradigms (supervised, unsupervised, etc.).

What You'll Learn

Learn fundamental principles and techniques of machine learning.

Course Content

Introduction to Course

Getting Started

Why Learn Machine Learning

Introduction to Machine Learning

Types of Self Learning

Introduction to Datasets

Machine Learning Libraries

Quiz

Exploratory Data Analysis

Import Dataset

Data Frame Functions

Quiz

Data Scrubbing

Introduction to Data Scrubbing

Data Scrubbing Operation: Removing Variables

Data Scrubbing Operation: One-Hot Encoding

Data Scrubbing Operation: Drop Missing Values

Data Scrubbing Operation: Dimension Reduction

Quiz

Pre-Model Algorithms

PCA Implementation Steps: 1 to 3

PCA Implementation Steps: 4 to 6

K-Means Clustering Implementation Steps: 1 to 3

K-Means Clustering Implementation Steps: 4 to 6

Quiz

Split Validation

2 Lessons

Model Design

4 Lessons

Linear Regression

5 Lessons

Logistic Regression

5 Lessons

10.

Support Vector Machines

4 Lessons

11.

K-Nearest Neighbors

4 Lessons

12.

Tree-Based Methods

10 Lessons

13.

Conclusion

1 Lesson

14.

Appendix

2 Lessons

Course Author

Trusted by 1.4 million developers working at companies

Anthony Walker

@_webarchitect_

Evan Dunbar

ML Engineer

Carlos Matias La Borde

Software Developer

Souvik Kundu

Front-end Developer

Vinay Krishnaiah

Software Developer

Eric Downs

Musician/Entrepeneur

Kenan Eyvazov

DevOps Engineer

Souvik Kundu

Front-end Developer

Eric Downs

Musician/Entrepeneur

Anthony Walker

@_webarchitect_

Evan Dunbar

ML Engineer

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