Machine Learning with Python


Are the phrases ‘it is certain’, ‘yes you may rely on it’, ‘reply hazy try again’ common in your predictions? Make room on your shelf for your magic eight ball and take this opportunity to see how Machine Learning can be a beneficial tool for predicting future trends. All with a bit more than oil and blue die. All signs point to yes as an indicator you will benefit from this experience.

About This Course

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 Syllabus

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 for data science
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