Europe's first EMBA with STEM focus

Machine Learning can be an incredibly beneficial tool to uncover hidden insights and predict future trends. This Machine Learning with Python course will give you all the tools you need to get started with supervised and unsupervised learning.

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This Machine Learning with Python course dives into the basics of machine learning using Python, 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.

We’ll explore many popular algorithms including Classification, Regression, Clustering, and Dimensional Reduction and popular models such as Train/Test Split, Root Mean Squared Error (RMSE), and Random Forests. Along the way, you’ll look at real-life examples of machine learning and see how it affects society in ways you may not have guessed!

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

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!

• Institution: IBM
• Subject:Data Analysis & Statistics
• Level: Introductory
• Prerequisites:Recommended: Python Basics for Data Science.
• Language: English
• Video Transcript: English

• The difference between the two main types of machine learning methods: supervised and unsupervised
• Supervised learning algorithms, including classification and regression
• Unsupervised learning algorithms, including Clustering and Dimensionality Reduction
• How statistical modeling relates to machine learning and how to compare them
• Real-life examples of the different ways machine learning affects society

Module 1 – Introduction to Machine Learning
Applications of Machine Learning
Supervised vs Unsupervised Learning
Python libraries suitable for Machine LearningModule 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

Unfortunately, learners residing in one or more of the following countries or regions will not be able to register for this course: Iran, Cuba and the Crimea region of Ukraine. While edX has sought licenses from the U.S. Office of Foreign Assets Control (OFAC) to offer our courses to learners in these countries and regions, the licenses we have received are not broad enough to allow us to offer this course in all locations. edX truly regrets that U.S. sanctions prevent us from offering all of our courses to everyone, no matter where they live.