6 Free Courses for Machine Learning and Data Science

This is an accumulation of free machine learning and #datascience courses to commence your winter learning season. Courses range from introductory #machinelearning to #deeplearning to natural language processing and past.

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Check out a collection of free machine learning and data science courses to kick off your winter learning season.

This is an accumulation of free machine learning and data science courses to commence your winter learning season. Courses range from introductory machine learning to deep learning to natural language processing and past.

On the off chance that, after reading this rundown, you wind up needing more free quality, curated learning materials, look at the related posts further underneath.



1. Introduction to Computational Thinking and Data Science

MIT

6.0002 is the continuation of 6.0001 Introduction to Computer Science and Programming in Python and is planned for students with practically zero programming experience. It means to provide students with an understanding of the role calculation can play in taking care of problems and to encourage understudies, regardless of their major, feel reasonably certain of their capacity to write little programs that enable them to achieve helpful objectives. The class utilizes the Python 3.5 programming language.

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2. Machine Learning



Stanford

This course provides a broad introduction to machine learning and statistical pattern recognition. Topics include: supervised learning (generative/discriminative learning, parametric/non-parametric learning, neural networks, support vector machines); unsupervised learning (clustering, dimensionality reduction, kernel strategies); learning theory (predisposition/variance tradeoffs; VC theory; large margins); reinforcement learning and versatile control. The course will likewise talk about recent utilizations of machine learning, for example, to robotic control, data mining, self-ruling route, bioinformatics, discourse recognition, and content and web data processing.

3. Introduction to Machine Learning for Coders!

fast.ai

There are around 24 hours of exercises, and you should plan to spend around 8 hours per week for 12 weeks to finish the material. The course depends on exercises recorded at the University of San Francisco for the Masters of Science in Data Science program. We expect that you have no less than one year of coding experience, and either remember what you learned in secondary school math, or are prepared to do some free investigation to refresh your insight.

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4. Machine Learning Crash Course

Google

Ready to start practicing machine learning? Learn and apply major machine learning ideas with the Crash Course, get real-world experience with the partner Kaggle rivalry, or visit Learn with Google AI to explore the full library of training resources.

5. Introduction to Deep Learning

MIT

An introductory course on deep learning techniques with applications to machine translation, picture recognition, diversion playing, picture generation and the sky is the limit from there. A collaborative course incorporating labs in TensorFlow and peer brainstorming alongside lectures. Course finishes up with project proposals with criticism from staff and board of industry sponsors.

6. Practical Deep Learning For Coders, Part 1



fast.ai

Welcome to the 2018 release of fast.ai’s 7 week course, Practical Deep Learning For Coders, Part 1, instructed by Jeremy Howard (Kaggle’s #1 competitor 2 years running, and founder of Enlitic). Learn how to assemble best in class models without requiring graduate-level math—yet in addition without dumbing anything down. Goodness one other thing… it’s thoroughly free! Also, there’s an entire network of thousands of other learners ready to assist you with your journey—simply go to forums.fast.ai on the off chance that you require any assistance, or simply need to talk to other deep learning learners

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