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IBM - Machine Learning with Python

IBM: Machine Learning with Python

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IBM INSTRUCTORS

Instructors: Saeed Aghabozorgi

Course Description

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!

Topics include

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

This course is part of the 'IBM Data Science Professional Certificate' IBM