This Machine Learning with Python course dives into the basics of machine learning using an approachable, and well-known, programming language. You’ll learn about Supervised vs Unsupervised Learning, look into how Statistical Modeling relates to Machine Learning, and do a comparison of each.
Look at real-life examples of Machine learning and how it affects society in ways you may not have guessed!
Explore many algorithms and models:
Popular algorithms: Classification, Regression, Clustering, and Dimensional Reduction.
Popular models: Train/Test Split, Root Mean Squared Error, and Random Forests.
Get ready to do more learning than your machine!

Course Overview

Module 1 – Supervised vs Unsupervised Learning

  • Machine Learning vs Statistical
  • Modelling

  • Supervised vs Unsupervised Learning
  • Supervised Learning Classification
  • Unsupervised Learning

Module 2 – Supervised Learning I

  • K-Nearest Neighbors
  • Decision Trees
  • Random Forests
  • Reliability of Random Forests
  • Advantages & Disadvantages of Decision Trees

Module 3 – Supervised Learning II

  • Regression Algorithms
  • Model Evaluation
  • Model Evaluation: Overfitting & Underfitting
  • Understanding Different Evaluation Models

Module 4 – Unsupervised Learning

  • K-Means Clustering plus Advantages & Disadvantages
  • Hierarchical Clustering plus Advantages & Disadvantages
  • Measuring the Distances Between Clusters – Single Linkage Clustering
  • Measuring the Distances Between Clusters – Algorithms for Hierarchy Clustering
  • Density-Based Clustering

Module 5 – Dimensionality Reduction & Collaborative Filtering

  • Dimensionality Reduction: Feature Extraction & Selection
  • Collaborative Filtering & Its Challenges
  • Python