Extra
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.
In-depth introduction to machine learning in 15 hours of expert videos Introduction in-depth to machine learning in 15 hours of expert videos by experts.
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
Last updated