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.

Beginner

72 Lessons

14h

Updated 1 week ago

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

AI-POWERED

Explanations

AI-POWERED

Explanations

This course includes

6 Projects

148 Playgrounds

26 Quizzes

This course includes

6 Projects

148 Playgrounds

26 Quizzes

Course Overview

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 pr...Show More

What You'll Learn

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

What You'll Learn

An understanding of fundamental machine learning algorithms and their use cases

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 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

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 Quiz on Clustering

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 Quiz on Generalized Linear Regression

Face Recognition Using Kernel Linear Discriminant

Project

Support Vector Machine

Explore support vector machines for classification, utilizing 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 Quiz on SVM

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

1 Lesson

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

How to Predict the Traffic Volume Using Machine Learning

Project

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