https://github.com/nishitpatel01/Data-Science-and-Machine-Learning-Resources

List of Data Science and Machine Learning Resource that I frequently use
https://github.com/nishitpatel01/Data-Science-and-Machine-Learning-Resources

List: Data-Science-and-Machine-Learning-Resources

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List of Data Science and Machine Learning Resource that I frequently use

• Host: GitHub
• URL: https://github.com/nishitpatel01/Data-Science-and-Machine-Learning-Resources
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README

        
# [![Awesome](https://awesome.re/badge-flat.svg)](https://awesome.re) Data-Science-and-Machine-Learning-Resources

List of Data Science and Machine Learning Resource that I frequently use

### Large Language Models (LLMs) & Generative AI

 - [Awesome LLM](https://github.com/Hannibal046/Awesome-LLM#tutorials-about-llm)

   

#### Basic Resources & Transformers

- [The Illustrated Transformer](http://jalammar.github.io/illustrated-transformer/)

- [Transformer Explained](https://daleonai.com/transformers-explained)

- [Visual Guide to Transformer Neural Networks](https://www.youtube.com/watch?v=dichIcUZfOw)

- [Understanding Large Language Models](https://magazine.sebastianraschka.com/p/understanding-large-language-models)

- [Transformer models: an introduction and catalog — 2023 Edition](https://amatriain.net/blog/transformer-models-an-introduction-and-catalog-2d1e9039f376/)

#### Advanced concepts

- [Fine-tuning LLMs](https://magazine.sebastianraschka.com/p/finetuning-large-language-models)

#### Research Papers

##### LLM Architectures

- [Attention is all you need](https://arxiv.org/pdf/1706.03762.pdf)

- [BERTL Pre-training of Deep Biirectional transformers for Language Understanding](https://arxiv.org/abs/1810.04805)

- [LLaMA-Adapter: Efficient Fine-tuning of Language Models with Zero-init Attension](https://daleonai.com/transformers-explained)

- [Paramter-Efficient Transfer Learning for NLP](http://proceedings.mlr.press/v97/houlsby19a/houlsby19a.pdf)

- [Distillation - step by step](https://arxiv.org/pdf/2305.02301.pdf)

- [SQL-PaLM: IMPROVED LARGE LANGUAGE MODEL ADAPTATION FOR TEXT-TO-SQL](https://arxiv.org/pdf/2306.00739.pdf)

- [Language Models are Few-Shot Learners](https://arxiv.org/pdf/2005.14165.pdf)

- [FINETUNED LANGUAGE MODELS ARE ZERO-SHOT LEARNERS](https://arxiv.org/pdf/2109.01652.pdf)

- [Large Language Models are Zero-Shot Reasoners](https://arxiv.org/pdf/2205.11916.pdf)

- [Scaling Laws for Neural Language Models](https://arxiv.org/pdf/2001.08361.pdf)

- [REACT: SYNERGIZING REASONING AND ACTING INLANGUAGE MODELS](https://arxiv.org/pdf/2210.03629.pdf)

- [Chain-of-Thought Prompting Elicits Reasoning in Large Language Models](https://arxiv.org/pdf/2201.11903.pdf)

##### Retrieval augmented LLMs

### Data Science

- [Data Science Central](https://www.datasciencecentral.com/)

- [Towards Data Science](https://towardsdatascience.com/)

- [Analytics Vidhya](http://analyticsvidhya.com/)

- [Data Science 101](http://101.datascience.community/)

- [Data Science News](https://www.datacamp.com/community)

- [Data Science Plus](https://datascienceplus.com/)

- [Listen Data](https://www.listendata.com/)

- [Data Science Specialization Course Notes](http://sux13.github.io/DataScienceSpCourseNotes/)

- [Various Data Science Tutorials](https://data-flair.training/blogs/data-science-tutorials-home/)

- [Probabilistic Programming & Bayesian Methods for Hackers](http://camdavidsonpilon.github.io/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers/)

- [Unofficial Google Data Science Blog](http://www.unofficialgoogledatascience.com/)

- [Data Science Cheat Sheet](https://medium.com/@anushkhabajpai/top-data-science-cheat-sheets-ml-dl-python-r-sql-maths-statistics-5239d4568225)

### R 

- [R Cookbook](http://www.cookbook-r.com/)

- [R Blogdown](https://bookdown.org/yihui/blogdown/)

- [ggplot2](http://ggplot2.tidyverse.org/reference/)

- [Headley Wickham](http://hadley.nz/)

- [Advance R](http://adv-r.had.co.nz/)

- [R Package Documentation](https://rdrr.io/)

- [Parallel Processing in R](http://dept.stat.lsa.umich.edu/~jerrick/courses/stat701/notes/parallel.html)

- [Geo Computation with R](https://geocompr.robinlovelace.net/)

### Python

- [Learn Python Org](http://www.learnpython.org/en/Hello%2C_World%21)

- [Python Graph Gallery](https://python-graph-gallery.com/)

- [Collection of Jupyter Notebooks](https://github.com/donnemartin/data-science-ipython-notebooks)

- [Streamlit library for ML visuals](https://github.com/streamlit/streamlit/)

- [Python Machine learning Notebooks](https://github.com/rasbt/python-machine-learning-book-3rd-edition)

- [Automate Stuff with Python](https://automatetheboringstuff.com/)

- [Python from NSA](https://nsa.sfo2.digitaloceanspaces.com/comp3321.pdf)

- [Awesome Python](https://awesome-python.com/)

- [Awesome Python Github](https://github.com/vinta/awesome-python#readme)

- [Comprehensice python cheatsheet](https://gto76.github.io/python-cheatsheet)

- [Real Python](https://realpython.com/)

- [Function Decorators](https://stackoverflow.com/questions/739654/how-to-make-function-decorators-and-chain-them-together)

### Machine Learning

- [Google AI Blog](https://ai.googleblog.com/)

- [kdnuggets](https://www.kdnuggets.com/)

- [Kaggle](https://www.kaggle.com/)

- [Math Works](https://www.mathworks.com/help/stats/index.html)

- [In depth introduction to machine learning - Hastie & Tibshirani](https://www.dataschool.io/15-hours-of-expert-machine-learning-videos/)

- [UC Business Analytics R programming guide](http://uc-r.github.io/)

- [Machine Learning from CMU](https://alex.smola.org/teaching/cmu2013-10-701/index.html)

- [ML Cheatsheet - Stanford CS229](https://stanford.edu/~shervine/teaching/cs-229/cheatsheet-supervised-learning)

- [Learning from Data](http://work.caltech.edu/telecourse.html)

- [The Learning Machine](https://www.thelearningmachine.ai/)

- [Machine Learning Plus](https://www.machinelearningplus.com/)

- [Machine Learning Resources from Sebastian Raschka](https://sebastianraschka.com/resources/)

- [Machine Learning Notebooks](https://github.com/ethen8181/machine-learning)

- [Machine Learning for beginners](https://mlcourse.ai/)

- [Curated Machine Learning Resources](https://madewithml.com/)

- [Machine Learning Toolbox](https://amitness.com/toolbox/)

- [Rules of Machine Learning: Best Practices for ML Engineering from Google](https://developers.google.com/machine-learning/guides/rules-of-ml)

- [Machine Learning Crash Course](https://developers.google.com/machine-learning/crash-course/ml-intro)

- [Machine Learning Interviews](https://github.com/khangich/machine-learning-interview)

- [Applied ML - Curated list of papers, articles, and blogs on data science & machine learning in production](https://github.com/eugeneyan/applied-ml)

- [Best of Machine Learning - Python](https://github.com/ml-tooling/best-of-ml-python)

- [Machine Learning Glossary](https://ml-cheatsheet.readthedocs.io/en/latest/index.html)

- [Awesome Machine Learning](https://github.com/josephmisiti/awesome-machine-learning)

- [Explanable AI](https://christophm.github.io/interpretable-ml-book/)

- [Fairness and Machine Learning](https://fairmlbook.org/)

- [Google Reseatch 2021: Themes and beyond](https://ai.googleblog.com/2022/01/google-research-themes-from-2021-and.html)

- [Machine Learning Complete - Notebooks & demos](https://github.com/Nyandwi/machine_learning_complete)

- [Awesome AI: A curated list of Artificial Intelligence (AI) courses, books, video lectures and papers](https://project-awesome.org/owainlewis/awesome-artificial-intelligence?utm_campaign=Artificial%2BIntelligence%2BWeekly&utm_medium=web&utm_source=Artificial_Intelligence_Weekly_277)

### MLOps

- [MLOps References - Curated list of MLOps resources](https://ml-ops.org/content/references.html)

- [Industrialization of ML Model](https://medium.com/swlh/industrialization-of-a-ml-model-using-airflow-and-apache-beam-5a5338f20184)

- [Awesome MLOps](https://github.com/visenger/awesome-mlops)

### Statistics & Probability

- [Seeing Theory](http://students.brown.edu/seeing-theory/index.html)

- [Applied Modern Statistical Learning Techniques](https://www.alsharif.info/iom530)

- [Probability Theory & Mathematical Statistics](https://onlinecourses.science.psu.edu/stat414/node/287/)

- [Probability Distributions Overview](http://blog.cloudera.com/blog/2015/12/common-probability-distributions-the-data-scientists-crib-sheet/)

- [Applied Data Mining and Statistical Learning (PSU)](https://onlinecourses.science.psu.edu/stat857/)

- [Intro to Statistics - Distributions, Power, Sample size, Effective trial design and mixed effect models](https://michael-bar.github.io/Introduction-to-statistics/)

- [Statistics How To](https://www.statisticshowto.datasciencecentral.com/)

- [Probability Distributions in R](http://www.r-tutor.com/elementary-statistics/probability-distributions)

- [Mathematical Challenges](https://brilliant.org)

- [Statistics Basics & Inference](https://support.minitab.com/en-us/minitab-express/1/help-and-how-to/basic-statistics/inference/supporting-topics/basics/what-is-a-hypothesis-test/)

### Linear Algebra

- [Numerical Analysis](https://relate.cs.illinois.edu/course/cs450-f18/)

- [Introduction fo Linear Algebra for Applied Machine Learnign with Python](https://pabloinsente.github.io/intro-linear-algebra)

### Deep Learning

- [Deep Learning Papers and read](https://www.kdnuggets.com/2018/03/top-20-deep-learning-papers-2018.html)

- [Convolutional Neural Network](https://deeplearning4j.org/convolutionalnetwork)

- [Convolutional Neural Network for Visual Recognition](https://cs231n.github.io/)

- [A simple introduction of ANN](https://ujjwalkarn.me/2016/08/09/quick-intro-neural-networks/)

- [How backpropagation works](http://home.agh.edu.pl/~vlsi/AI/backp_t_en/backprop.html)

- [UFLDL DeepLearning Tutorials](http://ufldl.stanford.edu/tutorial/)

- [Classification Results using Deep Learing](http://rodrigob.github.io/are_we_there_yet/build/classification_datasets_results.html)

- [VGGNet Architecture on Imagenet](https://www.pyimagesearch.com/2017/03/20/imagenet-vggnet-resnet-inception-xception-keras/)

- [Deep Learning Book](https://www.deeplearningbook.org/lecture_slides.html)

- [Andrej Karpathy](https://karpathy.ai/)

- [Dive into Deep Learning](http://d2l.ai/index.html)

- [Deep Learning Examples in PyTorch by Nvidia](https://github.com/NVIDIA/DeepLearningExamples/tree/master/PyTorch)

- [Deep Learning Examples in TensorFlow by Nvidia](https://github.com/NVIDIA/DeepLearningExamples/tree/master/TensorFlow)

- [Curve Detectors](https://distill.pub/2020/circuits/curve-detectors/)

- [Deep Learning Drizzle](https://deep-learning-drizzle.github.io/)

- [Full Stack Deep Learning - training machine learning models to deploying AI systems in the real world](https://course.fullstackdeeplearning.com/)

- [Practical Deep Learning by Fasi.ai](https://www.fast.ai/)

- [Transformers from Scratch](https://e2eml.school/transformers.html)

### Time Series

- [Forecasting Principles and Practice](https://otexts.org/fpp2/)

- [How To Identify Patterns in Time Series Data](http://www.statsoft.com/textbook/time-series-analysis)

- [Applied Time Series Characteristics](https://newonlinecourses.science.psu.edu/stat510/node/47/)

- [CausalImpact using Baysian structure time series](https://google.github.io/CausalImpact/CausalImpact.html)

- [Time Series Notes (Oregon State University)](http://stat565.cwick.co.nz/)

- [Extracting Seasonality and Trend from Data: Decomposition using R](https://anomaly.io/seasonal-trend-decomposition-in-r/)

### Text Analysis/NLP

- [Text Processing - Steps, Tools & Examples](https://medium.com/@datamonsters/text-preprocessing-in-python-steps-tools-and-examples-bf025f872908)

- [Document Classification: 7 pragmatic approaches for small datasets](https://neptune.ai/blog/document-classification-small-datasets?utm_source=slack&utm_medium=post&utm_campaign=blog-document-classification-small-datasets)

- [Collection of Colab notebook based on deep learning & transformer models](https://notebooks.quantumstat.com/)

- [NLP on Spark](https://nlp.johnsnowlabs.com/)

- [NLP Index](https://index.quantumstat.com/)

### Data Visualization

- [Flowing Data](http://flowingdata.com/)

- [Seaborn pair plots](https://seaborn.pydata.org/generated/seaborn.pairplot.html)

- [D3 js examples](http://biovisualize.github.io/d3visualization/)

- [D3 js examples newer version](http://christopheviau.com/d3list/gallery.html)

- [Data Visualization Society](https://www.datavisualizationsociety.com/)

- [A Comprehensive guide to data exploration](https://www.analyticsvidhya.com/blog/2016/01/guide-data-exploration/)

- [Dash](https://dash.plot.ly/)

### Algorithms

- [Regression (Glm)](https://web.stanford.edu/~hastie/glmnet/glmnet_alpha.html)

- [Forecasting using Time Series](http://a-little-book-of-r-for-time-series.readthedocs.io/en/latest/src/timeseries.html)

- [Types of Regressions](https://www.listendata.com/2018/03/regression-analysis.html)

- [Practice Algorithms](https://algorithms.tutorialhorizon.com/)

- [Hidden Markov Models](https://s3-ap-southeast-1.amazonaws.com/erbuc/files/c893615b-5f1e-422e-833c-10ac70bd39c9.pdf)

- [HMM Example: Dishonest Casino](https://www.jennyleestat.com/post/hmm-algorithms/)

- [Hidden Markov Model Notes](http://www.cs.cmu.edu/~roni/10601-slides/hmm-for-asr-whw.pdf)

- [Kernals Trick(SVM)](http://www.eric-kim.net/eric-kim-net/posts/1/kernel_trick_blog_ekim_12_20_2017.pdf)

- [Boosting](https://www.r-bloggers.com/machine-learning-basics-gradient-boosting-xgboost/)

### Big Data

- [Big Data - Cognitive AI](https://cognitiveclass.ai/)

- [Hadoop Ecosystem Table](https://hadoopecosystemtable.github.io/)

### Spark

- [Data Scientist's guide to Apache Spark](https://pages.databricks.com/rs/094-YMS-629/images/data-scientists-guide-apache-spark-NEW-BRAND-051520-v4.pdf?mkt_tok=eyJpIjoiT0RKa01ESTJaV1UxTkRkaiIsInQiOiI1Z2NTbXk1RThENm9YbEliNlwvbTc2VjNVVkYyRXB1ZnJoV0hITm9DK1A2clBwXC8reE1LRWU4TndSXC9WWmwzb3lrWVQ2eUlqcW9lRU5nZlNidElha0UzOENrYlQ1WUI5NENtamZEbXhRM01iVSs3OGhWUlpDUEpLK0NyS210MVRNNyJ9)

### Frequently visiting blogs

- [Chris Albon](https://chrisalbon.com/)

- [DS Lore](http://nadbordrozd.github.io/)

- [Zack Stewart](http://zacstewart.com/)

- [David Robinson](http://varianceexplained.org/)

- [Simply Statistics](https://simplystatistics.org/)

- [Citizen Statistics](http://citizen-statistician.org/)

- [Civil Statistian](http://civilstat.com/)

- [R Studio Blog](https://blog.rstudio.com/)

- [Data Science Plus](https://datascienceplus.com/)

- [R Weekly Org](https://rweekly.org/)

- [Andrew Gelman](http://andrewgelman.com/)

- [Edwin Chen's Blog](http://blog.echen.me/)

- [R Statistcis co](http://r-statistics.co/Linear-Regression.html)

- [Datacamp Community News](https://www.datacamp.com/community)

- [Data Science and Robots - Brandon Rohrer](http://brohrer.github.io/blog.html)

- [Lavanya.ai](https://www.notion.so/Lavanya-ai-d43ba856316e47ab98969ab4a613c629)

- [Data Flair](https://data-flair.training/blogs/)

- [Fast.ai Blog](https://www.fast.ai)

- [Domino Blog for Code, ML and Data Science](https://blog.dominodatalab.com/)

- [Data36](https://data36.com/)

- [AI Show](https://channel9.msdn.com/Shows/AI-Show/An-Intuitive-Approach-to-Machine-Learning-Models-Part-1-of-4)

- [Distill.pub](https://distill.pub/)

- [Jay Alammar - Blog on NLP and Deep Learning](http://jalammar.github.io/)

- [Open AI Blog](https://openai.com/blog/)

### Tech blogs from various organizations

- [Netflix Tech Blog for Data Science](https://netflixtechblog.com/tagged/data-science)

- [Google AI Blog](https://ai.googleblog.com/)

- [AirBnb Engineering & Data Science](https://airbnb.io/)

- [Facebook Research](https://research.fb.com/blog/?cat=6)

- [The Yhat Blog](http://blog.yhat.com/)

- [Uber Engineering](https://eng.uber.com/category/articles/ai/)

### Classes from different universities

- [CS 229 ― Machine Learning](http://cs229.stanford.edu/syllabus.html)

- [Stat202 - Data Mining and analysis](http://web.stanford.edu/class/stats202/content/lectures.html)

- [Columbia University Applied Machine Learning by Andreas Muller](http://www.cs.columbia.edu/~amueller/comsw4995s18/schedule/)

### Public Datasets

- [Fig Share](https://figshare.com/browse)

- [Quandl](https://www.quandl.com/)

- [Quora](https://www.quora.com/Where-can-I-find-large-datasets-open-to-the-public)

- [Public Data Sources](http://www.jenunderwood.com/2016/01/14/my-favorite-public-data-sources/)

- [US Gov](https://www.data.gov/)

- [Our World Data](https://ourworldindata.org/)

- [UCI Machine Learning Repository](http://archive.ics.uci.edu/ml/index.php)

- [KDNuggets datasets](https://www.kdnuggets.com/datasets/index.html)

- [Jerry Smith - Data Science Insights](https://datascientistinsights.com/2013/02/02/data-monetization-road-paved-on-top-of-data-sets/)

- [Data Quest](https://www.dataquest.io/blog/free-datasets-for-projects/)

- [Amazon Product Data](http://jmcauley.ucsd.edu/data/amazon/)

- [Sentiment Analysis Datasets](https://lionbridge.ai/datasets/15-free-sentiment-analysis-datasets-for-machine-learning/)

- [Machine Learning A-Z: Download Practice Datasets](https://www.superdatascience.com/pages/machine-learning)

- [Microsoft Research Open Data](https://msropendata.com/)

- [Data Hub](https://datahub.io/)

- [Collection of NLP datasets](https://quantumstat.com/dataset/dataset.html)

- [John Snow Labs NLP & Healthcare datasets](https://www.johnsnowlabs.com/welcome-to-the-healthcare-data-library/)

- [Open Source Audio datasets](https://towardsdatascience.com/40-open-source-audio-datasets-for-ml-59dc39d48f06)

### Videos on Data

- [Ted Talks - Making sense of too much data](https://www.ted.com/playlists/56/making_sense_of_too_much_data)

- [Andrew Ng's ML Lectures - Stanford](https://www.youtube.com/playlist?list=PLoROMvodv4rMiGQp3WXShtMGgzqpfVfbU)

### Free Ebooks

- [Green Tea Press](http://greenteapress.com/wp/)

- [Machine learning and Data Science Books](https://www.kdnuggets.com/2018/05/10-more-free-must-read-books-for-machine-learning-and-data-science.html)

- [Time Series Analysis using R](https://www.stat.pitt.edu/stoffer/tsa4/tsaEZ.pdf)

- [Free programming ebooks](https://goalkicker.com/)

- [Machine Learning](https://livebook.manning.com/book/machine-learning-bookcamp/welcome/v-1/)

- [65 Free machine learnign and data books](https://towardsdatascience.com/springer-has-released-65-machine-learning-and-data-books-for-free-961f8181f189)

- [Free Programming and ML pdf books](https://www.pythonstacks.com/free-books/)

- [Approaching any machine learning problem](https://github.com/abhishekkrthakur/approachingalmost/blob/master/AAAMLP.pdf)

### Misc

- [Machine Learning Cheat Sheet in R](https://i2.wp.com/www.thertrader.com/wp-content/uploads/2018/03/Picture3.jpg)

- [Which algorithn should one use?](https://blogs.sas.com/content/subconsciousmusings/2017/04/12/machine-learning-algorithm-use/#prettyPhoto)

- [Papers with code](https://github.com/zziz/pwc)

- [Browse State of the art](https://paperswithcode.com/sota)

- [Data Science Projects](https://www.analyticsvidhya.com/blog/2018/05/24-ultimate-data-science-projects-to-boost-your-knowledge-and-skills/)

- [Churn Prediction & Survival Analysis](https://carldawson.net/churn-prediction-python/)

- [Stanford Machine Learning Projects](http://cs229.stanford.edu/proj2018/?source=post_page-----11ee8f95fc96----------------------)

- [Amazon Science Reasearch and blog](https://www.amazon.science/)

- [Machine Learning Questions](https://sebastianraschka.com/faq/index.html)

- [Graph database for beginners](https://go.neo4j.com/rs/710-RRC-335/images/Graph_Databases_for_Beginners.pdf?_ga=2.210920725.1607138034.1595889787-1206924530.1595889787)

- [Top Github Repos](https://towardsdatascience.com/top-10-github-repos-to-bookmark-right-now-b0bc62436ffc)

### Jupyter Notebooks

- [Survival Regression with Sci-kit learn](https://nbviewer.jupyter.org/github/sebp/scikit-survival/blob/master/examples/00-introduction.ipynb)

- [Evaluating Survival Regression](https://nbviewer.jupyter.org/github/sebp/scikit-survival/blob/master/examples/evaluating-survival-models.ipynb)

- [Jupyter Notebook by Domain](https://github.com/jupyter/jupyter/wiki/A-gallery-of-interesting-Jupyter-Notebooks)

- [Jupyter Notebooks - DS,ML,TF,AWS,Python](https://github.com/donnemartin/data-science-ipython-notebooks)

### Data Science Interview  Prep

- [Data Science Interview Questions  - Springboard](https://www.springboard.com/blog/data-science-interview-questions/)

- [Data Science Interviews by Category](https://github.com/alexeygrigorev/data-science-interviews)

- [120 Data Science Interview Questions](https://github.com/kojino/120-Data-Science-Interview-Questions)

- [Facebook Interview Prep](https://github.com/Christopher-Hsieh/facebook-interview-prep)

- [Software/ML Engineer Interview Prep](https://engineerseekingfire.com/how-to-prepare-for-software-engineering-interviews/)

- [Tech Interview Handbook](https://yangshun.github.io/tech-interview-handbook/introduction)

- [DS Interview Questions-Answers](https://towardsdatascience.com/120-data-scientist-interview-questions-and-answers-you-should-know-in-2021-b2faf7de8f3e)

- [Interview Query](https://www.interviewquery.com/)

### Data Science & Machine Learning Podcasts

- [Dataframed by Datacamp](https://www.datacamp.com/community/podcast)

- [Data Skeptic by Kyle Polich](https://dataskeptic.com/)

- [Linear Diagressions](http://lineardigressions.com/)

- [Gradient Dissent](https://www.wandb.com/podcast)

### Data Structure & Algorithms

- [Geeks for Geeks](https://www.geeksforgeeks.org/)

- [Program Creek](https://www.programcreek.com/)

- [Career Cup](https://www.careercup.com/)

- [A Gentle Introduction to Algorithm Complexity Analysis](http://discrete.gr/complexity/)

- [Always be Coding](https://medium.com/always-be-coding/abc-always-be-coding-d5f8051afce2#.4heg8zvm4)

- [Competitive Programming Tutorials](https://www.topcoder.com/community/competitive-programming/tutorials/)

- [Python for Algorithms & Data Structure - Interview](https://nbviewer.jupyter.org/github/jmportilla/Python-for-Algorithms--Data-Structures--and-Interviews/tree/master/)

- [Skilled.dev](https://skilled.dev/course)

- [Big O Cheatsheet](https://www.bigocheatsheet.com/)

- [The Algorithms Repo](https://github.com/TheAlgorithms)

- [Interview Cake (Glossary)](https://www.interviewcake.com/)

- [Algorithm & Coding Interviews](https://github.com/liyin2015/Algorithms-and-Coding-Interviews/tree/master/)

- [SDE Skills](https://beta.sdeskills.com/)

- [Tech Interview Handbook](https://techinterviewhandbook.org/algorithms/algorithms-introduction/)

### System Designs & Distributed Systems

 - [SYSTEM DESIGN INTERVIEW- AN INSIDER'S GUIDE](https://systeminterview.com/)

### Git 

- [Git Explorer](https://gitexplorer.com/)

- [Interactive git tutorial for beginners](https://rogerdudler.github.io/git-guide/)

- [How to Write a Git Commit Message](https://chris.beams.io/posts/git-commit/)

- [Awesome Git](https://github.com/dictcp/awesome-git)

- [Git Cheatsheet](http://ndpsoftware.com/git-cheatsheet.html#loc=workspace;)