10 GitHub Repositories to Grasp Machine Studying

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Mastering machine studying (ML) could seem overwhelming, however with the precise sources, it may be way more manageable. GitHub, the broadly used code internet hosting platform, is dwelling to quite a few invaluable repositories that may profit learners and practitioners in any respect ranges. On this article, we assessment 10 important GitHub repositories that present a variety of sources, from beginner-friendly tutorials to superior machine studying instruments.

Repository: microsoft/ML-For-Beginners

This complete 12-week program gives 26 classes and 52 quizzes, making it a perfect start line for newcomers. It serves as a place to begin for these with no prior expertise with machine studying and appears to construct core competencies utilizing Scikit-learn and Python.

Every lesson options supplemental supplies together with pre- and post-quizzes, written directions, options, assignments, and different sources to enhance the hands-on actions.

Repository: dair-ai/ML-YouTube-Courses

This GitHub repository serves as a curated index of high quality machine studying programs hosted on YouTube. By gathering hyperlinks to varied ML tutorials, lectures, and academic collection into one centralized location from suppliers like Clatech, Stanford, and MIT, the repo makes it simpler for learners to search out video-based ML content material that meets their wants. 

It’s the solely repository you want if you’re attempting to study issues without cost and at your individual time.

Repository: mml-book/mml-book.github.io

Arithmetic is the spine of machine studying, and this repository serves because the companion webpage to the guide “Arithmetic For Machine Studying.” The guide motivates readers to study mathematical ideas wanted for machine studying. The authors purpose to supply the mandatory mathematical abilities to know superior machine studying methods, reasonably than overlaying the methods themselves.

It covers linear algebra, analytic geometry, matrix decompositions, vector calculus, likelihood, distribution, steady optimization, linear regression, PCA, Gaussian combination fashions, and SVMs.

Repository: janishar/mit-deep-learning-book-pdf

The Deep Studying textbook is a complete useful resource meant to assist college students and practitioners enter the sector of machine studying, particularly deep studying. Revealed in 2016, the guide gives a theoretical and sensible basis within the machine studying methods which have pushed current advances in synthetic intelligence. 

The web model of the MIT Deep Studying E-book is now full and can stay freely accessible on-line, offering a invaluable contribution to the democratization of AI schooling. 

The guide covers a variety of matters in depth, together with deep feedforward networks, regularization, optimization algorithms, convolutional networks, sequence modeling, and sensible methodology.

Repository: DataTalksClub/machine-learning-zoomcamp

Machine Studying ZoomCamp is a free four-month on-line bootcamp that gives a complete introduction to machine studying engineering. Perfect for these critical about advancing their careers, this program guides college students via constructing real-world machine studying initiatives, overlaying elementary ideas like regression, classification, analysis metrics, deploying fashions, determination bushes, neural networks, Kubernetes, and TensorFlow Serving.

Over the course, members will achieve sensible expertise in areas like deep studying, serverless mannequin deployment, and ensemble methods. The curriculum culminates in two capstone initiatives that allow college students to reveal their newly-developed abilities. 

Repository: ujjwalkarn/Machine-Learning-Tutorials

This repository is a group of tutorials, articles, and different sources on machine studying and deep studying. It covers a variety of matters similar to Quora, blogs, interviews, Kaggle competitions, cheat sheets, deep studying frameworks, pure language processing, pc imaginative and prescient, varied machine studying algorithms, and ensembling methods. 

The useful resource is designed to supply each theoretical and sensible information with code examples and use case descriptions. It’s a complete studying instrument that gives a multi-faceted method to gaining publicity to the machine studying panorama.

Repository: josephmisiti/awesome-machine-learning

Superior Machine Studying is a curated listing of superior machine studying frameworks, libraries, and software program that’s excellent for these seeking to discover totally different instruments and applied sciences within the area. It covers instruments throughout a variety of programming languages from C++ to Go which can be additional divided into varied machine studying classes together with pc imaginative and prescient, reinforcement studying, neural networks, and general-purpose machine studying.

Superior Machine Studying is a complete useful resource for machine studying practitioners and lovers, overlaying all the things from information processing and modeling to mannequin deployment and productionization. The platform facilitates simple comparability of various choices to assist customers discover the most effective match for his or her particular initiatives and targets. Moreover, the repository stays up-to-date with the newest and best machine studying software program throughout varied programming languages, because of contributions from the neighborhood.

Repository: afshinea/stanford-cs-229-machine-learning

This repository gives condensed references and refreshers on machine studying ideas lined in Stanford’s CS 229 course. It goals to consolidate all of the vital notions into VIP cheat sheets spanning main matters like supervised studying, unsupervised studying, and deep studying. The repository additionally accommodates VIP refreshers that spotlight conditions in possibilities, statistics, algebra and calculus. Moreover, there’s a tremendous VIP cheatsheet that compiles all these ideas into one final reference that learners can readily have readily available.

By bringing collectively these key factors, definitions, and technical ideas, the aim is to assist learners totally grasp machine studying matters in CS 229. The cheat sheets allow summing up the very important ideas from lectures and textbook supplies into condensed references for technical interview.

Repository: khangich/machine-learning-interview

It gives a complete examine information and sources for getting ready for machine studying engineering and information science interviews at main tech corporations like Fb, Amazon, Apple, Google, Microsoft, and many others.

Key matters lined:

• LeetCode questions categorized by kind (SQL, programming, statistics).
• ML fundamentals like logistic regression, KMeans, neural networks.
• Deep studying ideas from activation capabilities to RNNs.
• ML techniques design together with papers on technical debt and guidelines of ML
• Basic ML papers to learn.
• ML manufacturing challenges like scaling at Uber and DL in manufacturing
• Widespread ML system design interview questions e.g. video/feed suggestion, fraud detection.
• Instance options and architectures for YouTube, Instagram suggestions.

The information consolidates supplies from prime specialists like Andrew Ng and contains actual interview questions requested at prime corporations. It goals to supply the examine plan to ace ML interviews throughout varied massive tech corporations.

Repository: EthicalML/awesome-production-machine-learning

This repository gives a curated listing of open supply libraries to assist deploy, monitor, model, scale and safe machine studying fashions in manufacturing environments. It covers varied elements of manufacturing machine studying together with:

1. Explaining Predictions & Mannequin
2. Privateness Preserving ML
3.  Mannequin & Knowledge Versioning
4. Mannequin Coaching Orchestration
5. Mannequin Serving & Monitoring
6. AutoML
7. Knowledge Pipeline
8. Knowledge Labelling
9. Metadata Administration
10. Computation Distribution
11. Mannequin Serialisation
12. Optimized Computation
13. Knowledge Stream Processing
14. Outlier & Anomaly Detection
15. Characteristic Retailer
16. Adversarial Robustness
17. Knowledge Storage Optimization
18. Knowledge Science Pocket book
19. Neural Search
20. And Extra.

Whether or not you are a newbie or an skilled ML practitioner, these GitHub repositories present a wealth of data and sources to deepen your understanding and abilities in machine studying. From foundational arithmetic to superior methods and sensible purposes, these repositories are important instruments for anybody critical about mastering machine studying.
 
 

Abid Ali Awan (@1abidaliawan) is an authorized information scientist skilled who loves constructing machine studying fashions. Presently, he’s specializing in content material creation and writing technical blogs on machine studying and information science applied sciences. Abid holds a Grasp’s diploma in Expertise Administration and a bachelor’s diploma in Telecommunication Engineering. His imaginative and prescient is to construct an AI product utilizing a graph neural community for college kids fighting psychological sickness.