HomeCoursesFundamentals of Machine Learning: A Pythonic Introduction

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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.

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Overview

Content

Reviews

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, followed by generalized linear regression, support vector machines, logistic regression, ensemble learning, and principal component analysis. You’ll also learn about autoencoders and variational autoencoders and end with three exciting projects. By the end, you’ll have a solid understanding of machine learning and its algorithms, hands-on experience implementing such algorithms and applying them to different problems, and an understanding of how each algorithm works with the provided examples.

This course focuses on core concepts, algorithms, and machine learning techniques. It explores the fundamentals, implements algo...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

An understanding of the fundamental machine learning algorithms

Content

65 Lessons6 Projects23 Quizzes

Course Overview

2 Lessons

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

Introduction to the Course

The Essentials of Machine Learning

Supervised Learning

10 Lessons

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

9 Lessons

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

8 Lessons

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

8 Lessons

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.

Overview of Probabilistic Models

Introduction to Logistic Regression

Optimizing BCE Loss

Kernel Logistic Regression

Multiclass Formulation

From Logistic Regression to Neural Networks

Implementation of a Neural Network

Quiz on Logistic Regression

Ensemble Learning

8 Lessons

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

Introduction to Ensemble Learning

Types of Ensemble Learning

Bagging

Decision Trees

Random Forest

Gradient Boosting: Implementation from Scratch

Gradient Boosting: Implementation Using Scikit-learn

AdaBoost

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.

Principal Component Analysis

Autoencoders

Autoencoders in Action

Variational Autoencoders

VAEs in Action

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.

Introduction to CVXPY

Mathematical Optimization

Convex Optimization

Convex Optimization Problems

Lagrangian Duality

Gradient Descent

10.

Wrapping Up

1 Lessons

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.

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