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Course Details

 Course Code: ML0101ENv3

 Audience: Anyone

 Course Level: Intermediate

 Time to Complete: 12 hours

 Language: English

About the Course

This course dives into the basic of machine learning using Python, an approachable and well-known programming language. You will learn about supervised and unsupervised learning, analyze the relation between statistical modelling and machine learning through comparisons.

You will also look into real-life examples to look at the various ways that machine learning affects the society. You will also explore many algorithms and models –

  • Popular algorithms: Classification, Regression, Clustering, Dimensional Reduction
  • Popular models: Train/Test Split, Root Mean Squared Error, Random Forests

Most importantly, this course will transform your theoretical knowledge into practical skills through experience at many hands-on labs.

Course Syllabus

  • Applications of Machine Learning
  • Supervised vs Unsupervised Learning
  • Python libraries suitable for Machine Learning
  • Linear Regression
  • Non-linear Regression
  • Model evaluation methods
  • K-Nearest Neighbour
  • Decision Trees
  • Logistic Regression
  • Support Vector Machines
  • Model Evaluation
  • K-Means Clustering
  • Hierarchical Clustering
  • Density-Based Clustering
  • Content-based recommender systems
  • Collaborative Filtering

Prerequisites for this Course

  • Python for data science

Recommended Existing Skills

  • JupyterLab. You can enrol for our free course for JupyterLab here.
  • Data Analysis with Python. You can take the course here.

Course Staff


Saeed Aghabozorgi, PhD.

Saeed Aghabozorgi, PhD. He is a Sr. Data Scientist at IBM with a track record of developing enterprise-level applications that substantially increase clients’ ability to turn data into actionable knowledge. He is a data mining field researcher and expert in developing advanced analytic methods like machine learning and statistical modelling on large datasets.

Other Contributors

Agatha Colangelo also contributed.