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• Machine Learning Course by Andrew Ng (Stanford University) 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.

• Curated List of Machine Learning Resources Learn Machine Learning online from the best machine learning courses/tutorials submitted & voted by the programming community.

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• An Introduction to Statistical Learning A book by Gareth James which will explain about statistical learning.

• List of Machine Learning University Courses List of awesome university courses for learning Computer Science!.

• Machine Learning for Software Engineers A complete daily plan for studying to become a machine learning engineer.

• Dive into Machine Learning Dive into Machine Learning with Python Jupyter notebook and scikit-learn!

• A curated list of awesome Machine Learning frameworks, libraries and software A curated list of awesome Machine Learning frameworks, libraries and software.

• A curated list of awesome data visualization libraries and resources. A curated list of awesome data visualization libraries and resources.

• An awesome Data Science repository to learn and apply for real world problems An awesome Data Science repository to learn and apply for real world problems.

• The Open Source Data Science Masters The open-source curriculum for learning Data Science. Foundational in both theory and technologies, the OSDSM breaks down the core competencies necessary to making use of data.

• Machine Learning FAQs on Cross Validated Machine learning algorithms build a model of the training data.

• Machine Learning algorithms that you should always have a strong understanding of What are some machine learning algorithms that you should always have a strong understanding of, and why?

• Difference between Linearly Independent, Orthogonal, and Uncorrelated Variables​List of Machine Learning Concepts Machine Learning concepts explained from scratch.

• MIT Machine Learning Lecture Slides Machine Learning lecture slides from MIT.

• Comparison Supervised Learning Algorithms In the data science course that I instruct, we cover most of the data science pipeline but focus especially on machine learning.

• Learning Data Science Fundamentals This post is a collection of resources that I found particularly useful when I was learning the fundamentals of data science.

• Machine Learning mistakes to avoid New to Machine Learning? Avoid these three mistakes.

• Statistical Machine Learning Course Statistical Methods for Machine Learning which can be useful.

• TheAnalyticsEdge edX Notes and Codes Notes from the edX Course

• Have Fun With Machine Learning An absolute beginner's guide to Machine Learning and Image Classification with Neural Networks.

• Twitter's Most Shared #machineLearning Content From The Past 7 Days Highest ranked #machinelearning content from the past 7 Days