Courses

Coursera's machine learning course, taught by Andrew Ng, provides a broad introduction to machine learning, data mining, and statistical pattern recognition.

This course is designed for anyone interested in machine learning, and is the first of two courses in the Microsoft Professional Program in Artificial Intelligence.

This course provides a broad introduction to machine learning, data mining, and statistical pattern recognition.

Fast.ai's deep learning course is designed to give coders an introduction to the practical applications of deep learning.

This course is designed to provide a broad introduction to machine learning, data mining, and statistical pattern recognition.

This course provides a broad introduction to machine learning, data mining, and statistical pattern recognition.

Kaggle's machine learning course provides a hands-on introduction to machine learning and data science.

This track provides an introduction to machine learning with Python, covering topics such as classification, regression, and clustering.

This series of tutorials provides an introduction to machine learning with Python, covering topics such as classification, regression, and clustering.

Microsoft Learn provides an introduction to machine learning, covering topics such as regression, classification, clustering, and deep learning.

Google's machine learning crash course provides a broad introduction to machine learning, data mining, and statistical pattern recognition.

Codecademy offers an interactive online course that teaches the basics of machine learning. The course covers the Python programming language and scikit-learn, a popular machine learning library for Python. Students will learn how to build models to make predictions from data, work with real-world data sets, and more. The course is self-paced and is designed to be completed in 10 weeks.

Hackr.io offers a comprehensive list of machine learning courses and tutorials. The courses on Hackr.io cover a range of topics, from basic concepts like regression and classification to advanced topics like neural networks and deep learning. Each course is rated by users, so you can see which courses are the most popular and well-regarded. In addition to paid courses, there are also many free courses and tutorials available on Hackr.io.

LearnPython.org offers a free, interactive course that teaches the basics of machine learning using the Python programming language. The course covers the basic concepts of supervised learning, unsupervised learning, and reinforcement learning, as well as common algorithms like decision trees and k-means clustering. The course is designed to be accessible to beginners and is self-paced, so you can learn at your own speed.

FreeCodeCamp offers a comprehensive guide to machine learning with Python. The guide covers the basic concepts of machine learning, as well as common algorithms like linear regression, decision trees, and k-nearest neighbors. The guide also includes tutorials on working with data in Python using libraries like NumPy, pandas, and Matplotlib. The guide is designed to be accessible to beginners and is free to access.

Codementor offers a range of machine learning courses and tutorials taught by industry professionals. The courses cover a range of topics, from basic concepts like linear regression to advanced topics like neural networks and deep learning. In addition to paid courses, there are also many free tutorials and resources available on Codementor. The platform also offers live online mentoring, so you can get help from an expert as you learn.

This YouTube playlist by Columbia University covers the basics of machine learning, including supervised and unsupervised learning, linear regression, and decision trees. The course is taught by Professor John Paisley and includes lecture videos, slides, and assignments. The course is designed for beginners and is free to access.

This is a course offered by Stanford University through Coursera, taught by Andrew Ng. It covers a broad introduction to machine learning, including topics such as linear regression, logistic regression, neural networks, SVMs, clustering, and dimensionality reduction.

This is an introductory course on machine learning, offered by IBM on the edX platform. It covers the basics of machine learning, including supervised and unsupervised learning, and introduces common algorithms like decision trees, k-nearest neighbors, and linear regression.

This is a free course offered by Udacity, designed to introduce students to machine learning. It covers topics such as regression, classification, clustering, and neural networks, and includes hands-on exercises using Python and scikit-learn.

This is a course offered by fast.ai, designed to teach deep learning to coders. It covers topics such as image classification, natural language processing, and recommendation systems, and includes hands-on coding exercises using the PyTorch library.

This is a course offered by Stanford University, taught by Andrew Ng. It covers a broad range of machine learning topics, including supervised and unsupervised learning, deep learning, and reinforcement learning. It includes lecture videos, notes, and assignments.

This is a course offered by MIT OpenCourseWare, designed to introduce students to machine learning. It covers topics such as supervised and unsupervised learning, decision trees, neural networks, and deep learning, and includes lecture videos, notes, and assignments.

This is a course offered by Kaggle, designed to teach the basics of machine learning using Python. It covers topics such as data cleaning, feature engineering, model selection, and cross-validation, and includes hands-on exercises using real-world datasets.

This is a track offered by DataCamp, designed to teach the basics of machine learning using Python. It covers topics such as regression, classification, clustering, and deep learning, and includes hands-on coding exercises using the scikit-learn library.

This is a series of articles and tutorials offered by IBM, designed to teach the basics of machine learning using Python..