Fundamentals of Machine Learning: A Pythonic Introduction

Learn machine learning with scikit-learn, covering supervised learning, clustering, regression, SVMs, autoencoders, and ensemble methods through practical Python projects.

Beginner

65 Lessons

14h

Updated 3 months ago

Learn machine learning with scikit-learn, covering supervised learning, clustering, regression, SVMs, autoencoders, and ensemble methods through practical Python projects.

AI-POWERED

Explanations

AI-POWERED

Explanations

This course includes

6 Projects

148 Playgrounds

23 Quizzes

This course includes

6 Projects

148 Playgrounds

23 Quizzes

Course Overview

This course focuses on core concepts, algorithms, and machine learning techniques. It explores the fundamentals, implements algorithms from scratch, and compares the results with scikit-learn, the Python machine learning library. This course contains examples, theoretical knowledge, and codes for various ML algorithms. You’ll start by learning the essentials of machine learning and its applications. Then, you’ll learn about supervised learning, clustering, and constructing a bag of visual words project, fo...Show More

What You'll Learn

An understanding of the fundamental machine learning algorithms

Proficiency in strong problem-solving skills through hands-on projects

Working knowledge of applying machine learning algorithms to real-world datasets, addressing classification, regression, clustering, and dimensionality reduction tasks

Hands-on experience assessing and comparing the performance of machine learning models

What You'll Learn

An understanding of the fundamental machine learning algorithms

Course Content

Course Overview

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

Introduction to the Course

The Essentials of Machine Learning

Supervised Learning

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

Inputs, Features, and Targets Classifiers and Thresholds Parameters Regression Overfitting and Underfitting Occam’s Razor Regularization Ridge and Lasso Regression Validation Supervised Learning with Sklearn

Detect Cyber Intrusion Using Machine Learning

Project

Clustering

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

Introduction to Clustering K-Means Clustering

K-Means Walk-Through Example DBSCAN DBSCAN Walk-Through Example Agglomerative Clustering Agglomerative Clustering Walk-Through Example Clustering Algorithms Comparison Similarity and Dissimilarity Measures

Project: Bag of Visual Words

Project

Generalized Linear Regression

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

Feature Space Generalized Linear Models Generalized Linear Regression Generalized Linear Regression for Multiple Targets Kernels Kernel Linear Regression Kernel Linear Discriminant Kernel Trick Can Be Dangerous

Face Recognition Using Kernel Linear Discriminant

Project

Support Vector Machine

Dig into Support Vector Machines for classification, leveraging hyperplanes, kernels, and optimization techniques.

Introduction to SVM Hard-Margin SVM Identifying Support Vectors Linear Separability Soft-Margin SVM Dual Formulation Kernel SVM and Sparsity SVM in Sklearn

Logistic Regression

8 Lessons

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

Ensemble Learning

8 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

5 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

1 Lesson

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

How to Predict the Traffic Volume Using Machine Learning

Project

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

Hands-on Learning Powered by AI

See how Educative uses AI to make your learning more immersive than ever before.

Instant Code Feedback

Evaluate and debug your code with the click of a button. Get real-time feedback on test cases, including time and space complexity of your solutions.

AI-Powered Mock Interviews

Put your skills to the test in a simulated interview setting. Receive personalized feedback based on your performance. Available in Premium & Premium Plus plans.

Adaptive Learning

At various checkpoints throughout Educative courses, you will be prompted to take a quick assessment. Receive a condensed curriculum tailored to your strengths and skill gaps.

Explain with AI

Select any text within any Educative course, and get an instant explanation — without ever leaving your browser.

AI Code Mentor

AI Code Mentor helps you quickly identify errors in your code, learn from your mistakes, and nudge you in the right direction — just like a 1:1 tutor!