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Fundamentals of Machine Learning: A Pythonic Introduction

Explore machine learning fundamentals by building algorithms from scratch and using scikit-learn, while mastering classic models and modern techniques through hands-on projects.

72 Lessons

8 Projects

14h

Updated 2 weeks ago

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LEARNING OBJECTIVES

An understanding of fundamental machine learning algorithms and their use cases
Strong problem-solving skills developed through hands-on machine learning projects
A working knowledge of applying machine learning algorithms to real-world datasets, including classification, regression, clustering, and dimensionality reduction
Hands-on experience implementing machine learning algorithms from scratch and with scikit-learn
The ability to assess, compare, and interpret the performance of machine learning models

Learning Roadmap

72 Lessons6 Projects29 Quizzes

Course Overview

Get familiar with foundational machine learning concepts, hands-on projects, and algorithm implementation.

Introduction to the Course

The Essentials of Machine Learning

Types of Machine Learning

Supervised Learning

Get started with supervised learning, focusing on regression, classifiers, validation, and sklearn.

Inputs, Features, and Targets

Parameters

Classifiers and Thresholds

Regression

Overfitting and Underfitting

Occam’s Razor

Regularization

Ridge and Lasso Regression

Validation

Supervised Learning with Sklearn

Quiz on Supervised Learning

Detect Cyber Intrusion Using Machine Learning

Project

Clustering

10 Lessons

Examine clustering techniques including k-means, DBSCAN, agglomerative clustering, and their practical applications.

Project: Bag of Visual Words

Project

Generalized Linear Regression

9 Lessons

Grasp the fundamentals of generalized linear regression, kernel methods, and feature transformations.

Face Recognition Using Kernel Linear Discriminant

Project

Support Vector Machine

9 Lessons

Explore support vector machines for classification, utilizing hyperplanes, kernels, and optimization techniques.

Logistic Regression

8 Lessons

Investigate logistic regression, BCE optimization, kernel methods, multiclass extension, and neural network transition.

Ensemble Learning

9 Lessons

Master the fundamentals of ensemble learning and explore techniques to enhance predictive accuracy.

Early Stage Diabetes Prediction Using Ensemble Learning

Project

Decoding Dimensions: PCA and Autoencoders

6 Lessons

Solve problems in dimensionality reduction using PCA, autoencoders, and VAEs.

Image Reconstruction Using PCA

Project

Image Colorization using Autoencoders

Project

Colorful Face Generation with VAEs

Project

Appendix

6 Lessons

Get started with CVXPY, mathematical and convex optimization, gradient descent, and Lagrangian duality.

10.

Wrapping Up

Examine the comprehensive introduction to machine learning using Python and practical applications.

Conclusion

How to Predict the Traffic Volume Using Machine Learning

Project

Certificate of Completion

Showcase your accomplishment by sharing your certificate of completion.

Developed by MAANG Engineers

ABOUT THIS COURSE

Machine learning is a core skill for modern developers, powering applications such as data analysis, computer vision, recommendation systems, and automation. In this course, you’ll learn essential machine learning concepts, key algorithms, and practical techniques using Python, combining theory with hands-on implementation and comparison to scikit-learn models. You’ll begin with machine learning fundamentals and real-world use cases, then explore supervised learning and clustering. The course includes a practical bag-of-visual-words project and covers topics such as linear and logistic regression, support vector machines, ensemble methods, and principal component analysis. It concludes with modern representation learning techniques, including autoencoders and variational autoencoders. By the end, you will be able to apply core machine learning algorithms to real datasets, evaluate model performance, and confidently use machine learning in real-world projects.

ABOUT THE AUTHOR

Khayyam Hashmi

Computer scientist and Generative AI and Machine Learning specialist. VP of Technical Content @ educative.io.

Learn more about Khayyam

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