Top 5 Free University Courses To Master Machine Learning And Data Science – News18

Last Updated:January 22, 2025, 08:44 IST

Explore 5 free university courses on machine learning from MIT, Harvard, Stanford and the University of Michigan.

Machine learning, a branch of artificial intelligence (AI), enables computers to learn from past data and independently enhance their performance over time without needing explicit programming. Unlike traditional systems that follow predefined rules, machine learning algorithms are designed to identify patterns, correlations, and trends within data. Using these insights, the system can make predictions, decisions, or take actions on its own. As these algorithms process more data, they become increasingly accurate and effective, continuously improving their ability to make better decisions in the future.

For those pursuing a career in data, gaining knowledge of machine learning is essential. While data analysis allows you to examine past data to answer business questions, machine learning goes further by developing models that can forecast future trends based on the available data.

Here are 5 free university courses to learn Machine Learning:

1. Introduction to Machine Learning – MIT

The “Introduction to Machine Learning” course from MIT explores a variety of machine learning topics in great detail. You can freely access the course materials, including exercises and practice labs, through the MIT Open Learning Library.

This course covers a broad range of subjects, from foundational machine learning concepts to more advanced topics like ConvNets and recommendation systems. Key areas of the curriculum include:

• Linear classifiers
• Perceptrons
• Margin maximisation
• Regression analysis
• Neural networks
• Convolutional neural networks (ConvNets)
• State machines and Markov Decision Processes
• Reinforcement learning
• Recommendation systems
• Decision trees and nearest neighbours

2. Data Science: Machine Learning – Harvard

The “Data Science: Machine Learning” course offers an introduction to machine learning principles through hands-on projects, such as building movie recommendation systems. The course covers a variety of key topics, including:

• Basics of machine learning
• Cross-validation and overfitting
• Machine learning algorithms
• Recommendation systems
• Regularisation

3. Applied Machine Learning with Python – University of Michigan

The University of Michigan offers the “Applied Machine Learning in Python” course on Coursera, available for free through the audit track. This in-depth course focuses on widely used machine learning algorithms and their implementation using scikit-learn. Throughout the course, you’ll engage in hands-on programming exercises and projects using scikit-learn. Topics covered include:

• Introduction to machine learning and scikit-learn
• Linear regression
• Linear classifiers
• Decision trees
• Model evaluation and selection
• Naive Bayes, Random forest, Gradient boosting
• Neural networks
• Unsupervised learning

This course is part of the Applied Data Science with Python Specialisation provided by the University of Michigan on Coursera.

ALSO READ: 5 Free Online Data Science Courses By Stanford University

4. Machine Learning – Stanford

As a data scientist, being able to build predictive models is essential. Understanding how machine learning algorithms function and having the ability to implement them in Python is incredibly valuable. One of the top recommended courses in machine learning is CS229: Machine Learning at Stanford University. This course offers an in-depth look at various learning approaches, including supervised, unsupervised, and reinforcement learning. You’ll also explore techniques such as regularisation to avoid overfitting and create models that perform well across different datasets.

Topics covered include:

• Supervised learning
• Unsupervised learning
• Deep learning
• Generalisation and regularisation
• Reinforcement learning and control

5. Statistical Learning with Python – Stanford

The Statistical Learning with Python course covers the entire content of the ISL with Python book. By following the course and using the book as a guide, you’ll gain important skills in data science and statistical modelling.

Main topics include:

• Linear regression
• Classification
• Resampling
• Linear model selection
• Tree-based methods
• Unsupervised learning
• Deep learning