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Mastering machine studying (ML) could appear overwhelming, however with the suitable sources, it may be rather more manageable. GitHub, the extensively used code internet hosting platform, is house to quite a few useful repositories that may profit learners and practitioners in any respect ranges. On this article, we evaluation 10 important GitHub repositories that present a spread of sources, from beginner-friendly tutorials to superior machine studying instruments.
Repository: microsoft/ML-For-Inexperienced persons
This complete 12-week program gives 26 classes and 52 quizzes, making it a super 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 enrich the hands-on actions.
Repository: dair-ai/ML-YouTube-Programs
This GitHub repository serves as a curated index of high quality machine studying programs hosted on YouTube. By accumulating hyperlinks to numerous ML tutorials, lectures, and academic sequence into one centralized location from suppliers like Clatech, Stanford, and MIT, the repo makes it simpler for learners to seek out video-based ML content material that meets their wants.
It’s the solely repository you want if you’re making an attempt to be taught issues totally free 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 e-book “Arithmetic For Machine Studying.” The e-book motivates readers to be taught mathematical ideas wanted for machine studying. The authors purpose to supply the mandatory mathematical expertise to know superior machine studying methods, slightly than masking 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. Printed in 2016, the e-book offers 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 obtainable on-line, offering a useful contribution to the democratization of AI training.
The e-book 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. Preferrred for these severe about advancing their careers, this program guides college students by way of constructing real-world machine studying initiatives, masking elementary ideas like regression, classification, analysis metrics, deploying fashions, resolution timber, neural networks, Kubernetes, and TensorFlow Serving.
Over the course, contributors 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 show their newly-developed expertise.
Repository: ujjwalkarn/Machine-Studying-Tutorials
This repository is a group of tutorials, articles, and different sources on machine studying and deep studying. It covers a variety of matters equivalent to Quora, blogs, interviews, Kaggle competitions, cheat sheets, deep studying frameworks, pure language processing, pc imaginative and prescient, numerous machine studying algorithms, and ensembling methods.
The useful resource is designed to supply each theoretical and sensible data with code examples and use case descriptions. It’s a complete studying device that provides a multi-faceted strategy 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 good for these seeking to discover totally different instruments and applied sciences within the subject. It covers instruments throughout a spread of programming languages from C++ to Go which might be additional divided into numerous 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 fans, masking 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 very best match for his or her particular initiatives and objectives. Moreover, the repository stays up-to-date with the most recent and best machine studying software program throughout numerous programming languages, due to contributions from the neighborhood.
Repository: afshinea/stanford-cs-229-machine-learning
This repository offers condensed references and refreshers on machine studying ideas coated in Stanford’s CS 229 course. It goals to consolidate all of the essential notions into VIP cheat sheets spanning main matters like supervised studying, unsupervised studying, and deep studying. The repository additionally accommodates VIP refreshers that spotlight stipulations in chances, 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 objective is to assist learners totally grasp machine studying matters in CS 229. The cheat sheets allow summing up the important ideas from lectures and textbook supplies into condensed references for technical interview.
Repository: khangich/machine-learning-interview
It offers a complete research information and sources for making ready for machine studying engineering and information science interviews at main tech firms like Fb, Amazon, Apple, Google, Microsoft, and so forth.
Key matters coated:
- LeetCode questions categorized by kind (SQL, programming, statistics).
- ML fundamentals like logistic regression, KMeans, neural networks.
- Deep studying ideas from activation features to RNNs.
- ML techniques design together with papers on technical debt and guidelines of ML
- Traditional ML papers to learn.
- ML manufacturing challenges like scaling at Uber and DL in manufacturing
- Frequent ML system design interview questions e.g. video/feed advice, fraud detection.
- Instance options and architectures for YouTube, Instagram suggestions.
The information consolidates supplies from high specialists like Andrew Ng and contains actual interview questions requested at high firms. It goals to supply the research plan to ace ML interviews throughout numerous large tech corporations.
Repository: EthicalML/awesome-production-machine-learning
This repository offers a curated listing of open supply libraries to assist deploy, monitor, model, scale and safe machine studying fashions in manufacturing environments. It covers numerous facets of manufacturing machine studying together with:
- Explaining Predictions & Mannequin
- Privateness Preserving ML
- Mannequin & Knowledge Versioning
- Mannequin Coaching Orchestration
- Mannequin Serving & Monitoring
- AutoML
- Knowledge Pipeline
- Knowledge Labelling
- Metadata Administration
- Computation Distribution
- Mannequin Serialisation
- Optimized Computation
- Knowledge Stream Processing
- Outlier & Anomaly Detection
- Characteristic Retailer
- Adversarial Robustness
- Knowledge Storage Optimization
- Knowledge Science Pocket book
- Neural Search
- 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 expertise in machine studying. From foundational arithmetic to superior methods and sensible purposes, these repositories are important instruments for anybody severe about mastering machine studying.
Abid Ali Awan (@1abidaliawan) is an authorized information scientist skilled who loves constructing machine studying fashions. At present, 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 students combating psychological sickness.