Description

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!

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