Fundamentals of Machine Learning: A Pythonic Introduction

INTERACTIVE COURSE

Fundamentals of Machine Learning: A Pythonic Introduction

Beginner

65 Lessons

14h

Certificate of Completion

AI Explanations

AI Explanations

Fundamentals of Machine Learning: A Pythonic Introduction

6 Projects

148 Playgrounds

23 Quizzes

138 Illustrations

Takeaway Skills

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

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

Course Content

1

Course Overview

Introduction to the Course

The Essentials of Machine Learning

2

Supervised Learning

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

3

Clustering

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

4

Generalized Linear Regression

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

5

Support Vector Machine

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

6

Logistic Regression

8 Lessons

7

Ensemble Learning

8 Lessons

Early Stage Diabetes Prediction Using Ensemble Learning

Project

8

Decoding Dimensions: PCA and Autoencoders

5 Lessons

Image Reconstruction Using PCA

Project

Image Colorization using Autoencoders

Project

Colorful Face Generation with VAEs

Project

9

Appendix

6 Lessons

10

Wrapping Up

1 Lesson

COURSE AUTHOR

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