Do you want to learn Machine Learning for free? We have selected the best MOOCs and free online courses from top institutions for you.
1. Machine Learning
Stanford University, +3M students
This course provides a broad introduction to machine learning, datamining, and statistical pattern recognition. Topics include: (i) Supervised learning (parametric/non-parametric algorithms, support vector machines, kernels, neural networks). (ii) Unsupervised learning (clustering, dimensionality reduction, recommender systems, deep learning). (iii) Best practices in machine learning (bias/variance theory; innovation process in machine learning and AI). Take Course.
2. Machine Learning Foundations: A Case Study Approach
University of Washington, +300K students
In this course, you will get hands-on experience with machine learning from a series of practical case-studies. At the end of the first course you will have studied how to predict house prices based on house-level features, analyze sentiment from user reviews, retrieve documents of interest, recommend products, and search for images. Through hands-on practice with these use cases, you will be able to apply machine learning methods in a wide range of domains. Take Course
3. Mathematics for Machine Learning
Imperial College London, 170K enrolled
In this course, you will have the opportunity to be provided with the necessary mathematical background and skills in order to understand, design and implement modern statistical machine learning methodologies, as well as inference mechanisms, be provided with examples regarding the use of mathematical tools for the design of foundational machine learning and inference methodologies, such as Principal Component Analysis (PCA), Bayesian Linear Regression and Support Vector Machines. Take Course
4. Machine Learning Crash Course
Google, 150K enrolled
This course teaches the basics of machine learning through a series of lessons that include video lectures from researchers at Google, text written specifically for newcomers to ML, interactive visualizations of algorithms in action and real-world case studies. While learning new concepts, you’ll immediately put them into practice with coding exercises that walk you through implementing models in TensorFlow. Take Course
5. Machine Learning with Python
IBM, +130K students
This course dives into the basics of machine learning using an approachable, and well-known programming language, Python. In this course, we will be reviewing two main components:
First, you will be learning about the purpose of Machine Learning and where it applies to the real world. Second, you will get a general overview of Machine Learning topics such as supervised vs unsupervised learning, model evaluation, and Machine Learning algorithms. Take Course
6. Applied Machine Learning in Python
University of Michigan, +170K students
This course will introduce the learner to applied machine learning, focusing more on the techniques and methods than on the statistics behind these methods. The course will start with a discussion of how machine learning is different than descriptive statistics, and introduce the scikit learn toolkit through a tutorial. The issue of dimensionality of data will be discussed, and the task of clustering data, as well as evaluating those clusters, will be tackled. Take Course
7. Introduction to Machine Learning
Duke University, +25K enrolled
This course will provide you a foundational understanding of machine learning models (logistic regression, multilayer perceptrons, convolutional neural networks, natural language processing, etc.) as well as demonstrate how these models can solve complex problems in a variety of industries, from medical diagnostics to image recognition to text prediction. Take Course
8. Machine Learning for All
University of London, +25K enrolled
This course is for a lot of different people. It could be a good first step into a technical career but is also great if your role is non-technical. Or you might just be interested in finding out more about the hottest new technology of the moment. Whoever you are, we are looking forward to guiding you through your first machine learning project. Take Course
9. Fundamentals of Machine Learning in Finance
New York University, 13K students
The course aims at helping students to be able to solve practical ML-amenable problems that they may encounter in real life that include: (1) understanding where the problem one faces lands on a general landscape of available ML methods, (2) understanding which particular ML approach(es) would be most appropriate for resolving the problem, and (3) ability to successfully implement a solution, and assess its performance. Take Course.