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If you're interested in a data-related career, it's important to become familiar with machine learning. Data analytics allows you to answer business questions by analyzing relevant historical data. But machine learning takes this a step further by building models that can predict future trends based on the data available.
To help you get started with machine learning, we've compiled a list of free courses from universities like MIT, Harvard, Stanford, University of Michigan, etc. We encourage you to have a quick look at the course content to get an idea of what is covered, and then you can choose to take one or more of these courses based on what you want to learn.
let's start!
1. Introduction to Machine Learning – MIT
MIT's Introduction to Machine Learning course covers a variety of ML topics in some depth, and the course content, including exercises and labs, is freely accessible through the MIT Open Learning Library.
From the basics of machine learning to ConvNets and recommendation systems, here is a list of topics covered in this course:
- Linear Classifier
- perceptron
- Maximizing Margins
- Regression
- neural network
- Convolutional Neural Networks
- State Machines and Markov Decision Processes
- Reinforcement learning
- Recommended Systems
- Decision trees and nearest neighbor methods
Link: Introduction to Machine Learning
2. Data Science: Machine Learning – Harvard
Data Science: Machine Learning is another course that will teach you the fundamentals of machine learning by working on practical applications such as movie recommendation systems.
This course covers the following topics:
- Machine Learning Basics
- Cross-validation and overfitting
- Machine Learning Algorithms
- Recommended Systems
- Normalization
Link: Data Science: Machine Learning
3. Applied Machine Learning with Python – University of Michigan
Applied Machine Learning in Python is offered by the University of Michigan on Coursera. You can sign up for Coursera for free and access the course content for free (Audit Track).
This is a comprehensive course focusing on popular machine learning algorithms and their scikit-learn implementations. You will work through simple programming exercises and projects using scikit-learn. Here is a list of topics covered in this course:
- Introduction to Machine Learning and scikit-learn
- Linear regression
- Linear Classifier
- Decision Tree
- Model evaluation and selection
- Naive Bayes, Random Forest, Gradient Boosting
- neural network
- Unsupervised learning
This course is part of the Applied Data Science with Python specialization offered by the University of Michigan on Coursera.
Link: Applied Machine Learning with Python
4. Machine Learning – Stanford
As a data scientist, you also need to be comfortable building predictive models, so it will be extremely helpful to learn how machine learning algorithms work and be able to implement them in Python.
CS229: Machine Learning from Stanford University is one of the highly recommended ML courses, which will teach you different learning paradigms such as supervised learning, unsupervised learning, and reinforcement learning. Additionally, you will learn techniques such as regularization to prevent overfitting and build models that are better for generalization.
Here's an overview of the topics we'll cover:
- Supervised learning
- Unsupervised learning
- Deep Learning
- Generalization and normalization
- Reinforcement Learning and Control
LinkMachine Learning
5. Statistical Learning with Python – Stanford
The Learning Statistics with Python course covers all the content of the ISL Python book. By taking the course and using the book as a supplement, you will learn the essential tools for data science and statistical modeling.
Here is a list of the main areas covered in this course:
- Linear regression
- Classification
- Resampling
- Choosing a Linear Model
- Tree-based methods
- Unsupervised learning
- Deep Learning
Link: Statistical learning with Python
summary
We hope you found this list of free machine learning courses from top universities useful. Whether you want to work as a machine learning engineer or explore machine learning research, these courses will help you master the fundamentals.
Here are some related resources that you may find useful:
Happy learning!
Bala Priya C Bala is a Developer and Technical Writer from India. He loves working at the intersection of Mathematics, Programming, Data Science, and Content Creation. His areas of interest and expertise are DevOps, Data Science, and Natural Language Processing. He loves reading, writing, coding, and coffee. Currently, he is committed to acquiring and sharing knowledge among the developer community by writing tutorials, how-to guides, opinion pieces, and more. Bala also creates engaging resource compendiums and coding tutorials.
