A Practical Guide to Machine Learning with Python

Explore practical coding of basic machine learning models using Python. Gain insights into algorithms like linear regression, logistic regression, SVM, KNN, and decision trees.

Beginner

57 Lessons

72h 30min

Certificate of Completion

Explore practical coding of basic machine learning models using Python. Gain insights into algorithms like linear regression, logistic regression, SVM, KNN, and decision trees.

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Explanations

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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

Get familiar with coding basic machine learning models using Python and its historical importance.

Getting Started

Why Learn Machine Learning

Introduction to Machine Learning

Look at the essentials of machine learning types, key datasets, and core libraries.

Types of Self Learning

Introduction to Datasets Machine Learning Libraries Quiz

Exploratory Data Analysis

Break apart Exploratory Data Analysis techniques for importing datasets, using data frame functions, and practical quizzes.

Import Dataset Data Frame Functions Quiz

Data Scrubbing

Break down complex ideas in data scrubbing, variable removal, one-hot encoding, and dimension reduction.

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

Solve problems in PCA and K-means clustering for dimensionality reduction and data simplification.

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

Customer Segmentation with K-Means Clustering

Project

Split Validation

2 Lessons

Investigate how split validation partitions data, optimizes models, and ensures unbiased assessments.

Model Design

4 Lessons

Master the steps to design, implement, evaluate, and optimize machine learning models effectively.

Linear Regression

5 Lessons

Get familiar with implementing linear regression, handling data, and evaluating prediction accuracy.

Logistic Regression

5 Lessons

Get started with logistic regression for classification, handling data, and evaluating predictions.

10.

Support Vector Machines

4 Lessons

Go hands-on with implementing and optimizing Support Vector Machines for robust classification.

11.

K-Nearest Neighbors

4 Lessons

Apply your skills to implement and optimize k-NN models using Python for classification tasks.

12.

Tree-Based Methods

10 Lessons

Dig into core tree-based methods, including decision trees, random forests, and gradient boosting.

Cardiovascular Disease Risk Prediction with Random Forest

Project

13.

Conclusion

1 Lesson

Investigate future growth opportunities in machine learning and stay motivated for continuous learning.

14.

Appendix

2 Lessons

Master Python basics and set up Jupyter Notebook for effective machine learning practice.

Course Author

Trusted by 1.4 million developers working at companies

Anthony Walker

@_webarchitect_

Emma Bostian 🐞

@EmmaBostian

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

Anthony Walker

@_webarchitect_

Emma Bostian 🐞

@EmmaBostian

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