9 Best MOOCs to learn Machine Learning for Free

Do you want to learn Machine Learning for free? We have selected the best MOOCs and free online courses from top institutions for you.

9 Best Free Machine Learning Courses

  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 at Coursera.
  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 at Coursera
  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 at Coursera
  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 at Google
  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 at Coursera
  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 at University of Michigan
  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 at Duke University
  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 at University of London
  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 at Coursera

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