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Learning how AI works is becoming increasingly crucial in today’s era. And one of the most popular areas of study is machine learning algorithms. Currently, machine learning is among the most influential technologies across industries, from healthcare and finance to manufacturing and entertainment.
Machine learning algorithms are essentially a set of instructions that allow AI to perform various tasks. They provide insights into how machines can learn from data, make predictions, and even improve their capabilities over time.
Commonly, machine learning algorithms are applied in linear regression to predict continuous values based on the relationship between independent and dependent variables. Decision trees for classification and regression with rule-based approaches. Artificial neural networks (ANN) that mimic how the human brain works, and logistic regression, often used for binary classification.
For those working in AI, data science, or technology-driven industries, understanding machine learning algorithms is essential. As they form the foundation of modern technology. To better understand how machine learning algorithms work, there are several free courses available online. Here are some of the most recommended ones:
Machine Learning by Stanford University – Coursera
Taught by Andrew Ng, this course is one of the most recommended introductions to machine learning algorithms. It is ideal for beginners who want to take machine learning seriously.
Andrew Ng guides learners through the core concepts of machine learning, data mining, and statistical pattern recognition. The course also introduces key techniques such as linear regression, logistic regression, and neural networks.
Additionally, the course includes programming exercises using Octave/MATLAB, helping learners practice the real application of algorithms. The curriculum is structured from the basics to more advanced topics such as support vector machines (SVM). Clustering with K-means, and recommender systems.
With Andrew Ng’s clear and approachable teaching style, this course has been taken by millions of learners worldwide via Coursera. And is often considered the best entry point before moving into more advanced areas like deep learning or AI.
Applied Machine Learning with Python – University of Michigan (Coursera)
This course, taught by Kevyn Collins-Thompson, is part of the Applied Data Science with Python Specialization on Coursera.
It is designed for those who already have a basic understanding of Python and now want to focus on ML algorithms. Learners will study both supervised and unsupervised methods, including decision trees, random forests, k-nearest neighbors (KNN), and support vector machines (SVM).
The course emphasizes hands-on practice using Python to build models, test performance, and understand how algorithms work in real-world scenarios.
With an average duration of 4 weeks (6–8 hours per week), this intermediate-level course is best suited for learners who have prior knowledge of Python programming, linear algebra, and probability. By the end, participants are expected to have a solid understanding of applying M in practical contexts.
Machine Learning for Coders – fast.ai
The Introduction to Machine Learning (ML)for Coders course, taught by Jeremy Howard and Sylvain Gugger, is tailored for those who already have coding experience with Python.
This course focuses on hands-on coding and quickly moves into applying ML algorithms without overwhelming learners with unnecessary theory. At the start, learners will build an advanced image classifier using deep learning. Then dive into the algorithms behind it.
The course also covers essential algorithms such as random forests, gradient boosting, logistic regression, and support vector machines (SVM). Participants will also learn about model evaluation, data processing, and best practices in building ML systems.
Available for free on the fast.ai platform. This course is part of the fast.ai mission to make AI education more accessible. With its code-first approach, it is ideal for learners who prefer to dive straight into building and experimenting with models before delving deeper into theory.
