Machine Learning with Python: A Practical Introduction (edX)

This Machine Learning with Python course dives into the basics ofMachine LearningusingPython, 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.

You'll look at real-life examples of Machine Learning and how it affects society in ways you may not have guessed!

We'll explore many popular algorithms including Classification, Regression, Clustering, and Dimensional Reduction and popular models such asTrain/Test Split, Root Mean Squared Error and Random Forests.

Mostimportantly, you will transform your theoretical knowledge into practical skill using hands-on labs. Get ready to do more learning than your machine!

This course is part of the Python Data Science Professional Certificate and IBM Data Science Professional Certificate.

What you'll learn

- Supervised vs Unsupervised Machine Learning

- How Statistical Modeling relates to Machine Learning, andhow to do a comparison of each.

- Different waysmachinelearning affects society

Syllabus

Module 1 - Introduction to Machine Learning

- Applications of Machine Learning

- Supervised vs Unsupervised Learning

- Python libraries suitable for Machine Learning

Module 2 - Regression

- Linear Regression

- Non-linear Regression

- Model evaluation methods

Module 3 - Classification

- K-Nearest Neighbour

- Decision Trees

- Logistic Regression

- Support Vector Machines

- Model Evaluation

Module 4 - Unsupervised Learning

- K-Means Clustering

- Hierarchical Clustering

- Density-Based Clustering

Module 5 - Recommender Systems

- Content-based recommender systems

- Collaborative Filtering

Prerequisites

Recommended: Python Basics for Data Science