Machine Learning Goodness with various repositories or notebooks , ML/DL projects and AGI/AI tips/cheats.
Overview & Next Move
With the start of 100DaysOfMLCode challenge this Machine Learning Goodness repository is updated daily with either the completed Jupyter notebooks, Python codes, ML projects, useful ML/DL/NN libraries, repositories, cheat codes of ML/DL/NN/AI, useful information such as websites, beneficial learning materials, tips and whatnot not to mention some basic and advanced Python coding.
As the challenge is over the repo still grows. New beneficial material or materials in the world of Machine Learning when found is/are added to books, tools or repositories as well as updated in FinishYearWithML challenge and tweeted through my Twitter account and on Linkedin as well as sometimes on Facebook, Instagram.
- Table of contents
- Worthy Books
- Worthy Tools
- Worthy Repositories
- Notebooks
- Notes
- 100DaysOfMLCode
- FinishYearWithML
- Public
- Jupyter in Browser
- Logo
- License
Worthy books to hone expertise of ML/DL/NN/AGI, Python Programming, CS fundamentals needed for AI analysis and any useful book for a Developer or ML Engineer.
| Number | Title | Description | Link |
|---|---|---|---|
| 1 | Grokking Algorithms: An illustrated guide for programmers and other curious people | Visualisation of most popular algorithms used in Machine Learning and programming to solve problems | Grokking Algorithms |
| 2 | Algorithm Design Manual | Introduction to mathematical analysis of a variety of computer algorithms | Algorithm Design Manual |
| 3 | Category Theory for Programmers | Book about Category Theory written on posts from Milewski's programming cafe | Category Theory for Programmers |
| 4 | Automated Machine Learning | Book includes overviews of the bread-and-butter techniques we need in AutoML, provides in-depth discussions of existing AutoML systems, and evaluates the state of the art in AutoML | Automated Machine Learning |
| 5 | Mathematics for Computer Science | Book by MIT on Mathematics for Computer Science | Mathematics for Computer Science |
| 6 | Mathematics for Machine Learning | Book by University of California on Mathematics for Machine Learning | Mathematics for Machine Learning |
| 7 | Applied Artificial Intelligence | Book on engineering AI applications | Applied Artificial Intelligence |
| 8 | Automating Machine Learning Pipeline | Book-overview of automating ML lifecycle with Databricks Lakehouse platform | Automating Machine Learning Pipeline |
| 9 | Machine Learning Yearning | The book for AI Engineers win the era of Deep Learning | Machine Learning Yearning |
| 10 | Think Bayes | An introduytion to Bayesian statistics with Python implementation and Jupyter Notebooks | Think Bayes |
| 11 | The Ultimate ChatGPT Guide | The book that provides 100 resources to enhance your life with ChatGPT | The Ultimate ChatGPT Guide |
| 12 | The Art of ChatGPT Prompting: A Guide to Crafting Clear and Effective Prompts | The book to learn strategies for crafting compelling ChatGPT prompts that drive engaging and informative conversations | The Art of ChatGPT Prompting: A Guide to Crafting Clear and Effective Prompts |
| 13 | 10 ChatGPT prompts for Software Engineers | The book to learn how to prompt for software engineering tasks | 10 ChatGPT prompts for Software Engineers |
| 14 | How to Build Your Career in AI | Andrew Ng's insights about learning foundational skills, working on projects, finding jobs, and community in machine | How to Build Your Career in AI |
| 15 | Machine Learning Q and AI | Th book on popular wuestions asked in interviews on ML and advanced information to those questions | Machine Learning Q and AI |
| 16 | A comprehensive guide to Machine Learning | A free book of comprehensive guide to ML | A comprehensive guide to Machine Learning |
| 17 | Math for Deep Learning: What You Need to Know to Understand Neural Networks | A book of Mathematics for Machine Learning and Artificial Intelligence that goes into the Mathematics & Statistics Foundations of for Data Science | Math for Deep Learning: What You Need to Know to Understand Neural Networks |
Worthy websites and tools that include cheat codes for Python, Machine Learning, Deep Learning, Neural Networks and what not apart from other worthy tools while you are learning or honing your skills can be found here. Updated constantly when a worthy material is found to be shared on the repository.
| Number | Title | Description | Link |
|---|---|---|---|
| 1 | Python Cheatsheet | The Python Cheatsheet based on the book "Automate the Boring Stuff with Python" and many other sources | Python Cheatsheet |
| 2 | Machine Learning Algorithms Cheatsheet | The Machine Learning Cheatsheet explaining various models briefly | ML Algorithms Cheatsheet |
| 3 | Awesome AI Datasets & Tools | Links to popular open-source and public datasets, data visualizations, data analytics resources, and data lakes | Awesome AI Datasets & Tools |
| 4 | Machine Learning Cheatsheet | This Cheatsheet contains many classical equations and diagrams on Machine Learning to quickly recall knowledge and ideas on Machine Learning | Machine Learning Cheatsheet |
| 5 | Universal Intelligence: A Definition of Machine Intelligence | The publication on definitions of intelligence | Universal Intelligence |
| 6 | Logistic Regression | Detailed Overview of Logistic Regression | Logistic Regression |
| 7 | BCI Overview | Simple Overview of Brain-Computer Interface (BCI) | BCI Overview |
| 8 | BCI Research | Fascinating research of Brain-Computer Interface (BCI) | BCI Research |
| 9 | AI in Chemical Discovery | How AI is changing Chemical Diccovery? | AI in Chemical Discovery |
| 10 | Machine Learning for Chemistry | Best practices in Machine Learning for Chemistry | Machine Learning for Chemistry |
| 11 | AI tools for drug discovery | 5 cool AI-powered Drug Discovery tools | AI tools for drug discovery |
| 12 | Quantum Chemistry and Deep Learning | The application of Deep Learning and Neural Networks on Quantum Chemisty | Quantum Chemistry and Deep Learning |
| 13 | Computing Machinery and Intelligence | First paper on AI by Alan Turing | Computing Machinery and Intelligence |
| 14 | The blog on the take of Alan Turing | The analysis of Alan Turing's paper on AI (13 in the list) and the blog post on the life of him | Blog on Alan Turing |
| 15 | Minds, Brains and Programs | Paper that objects 'Turing Test' by John Searle | Minds, Brains and Programs |
| 16 | The blog on the take Of John Searle & Alan Turing | The blog post on the take Of John Searle paper (15 in the list) and ideas about AI and Alan Turing | John Searle & Alan Turing |
| 17 | The Youtube channel on Deep Learning's Neural Networks | An amazing youtube channel explaining what is Neural Network with simple and easy to follow descriptions | Deep Learning's Neural Networks |
| 18 | 8 architectures of Neural Networks | 8 architectures of Neural Network every ML engineer should know | 8 architectures |
| 19 | Neural Networks for the Prediction of Organic Chemistry Reactions | The use of neural networks for predicting reaction types | NNs for Prediction of Organic Chemistry Reactions |
| 20 | Expert System for Predicting Reaction Conditions: The Michael Reaction Case | Models were built to decide the compatibility of an organic chemistry process with each considered reaction condition option | Expert System for Predicting Reaction Conditions |
| 21 | Machine Learning in Chemical Reaction Space | Looked at reaction spaces of molecules involved in multiple reactions using ML-concepts | Machine Learning in Chemical Reaction Space |
| 22 | Machine Learning for Chemical Reactions | An overview of the questions that can and have been addressed using machine learning techniques | Machine Learning for Chemical Reactions |
| 23 | ByTorch overview | BoTorch as a framework of PyTorch | ByTorch overview |
| 24 | ByTorch official | Bayesian optimization or simply an official website of BoTorch | ByTorch official |
| 25 | VS Code Cheatsheet | VS Code Shortcut Cheatsheet | VS Code Cheatsheet |
| 26 | Simple Machine Learning Cheatsheet | The Machine Learning Cheatsheet of all fields making it and common used algorithms | Machine Learning Cheatsheet |
| 27 | DeepMind & UCL on Reinforcement Learning | DeepMind & UCL lectures as videos on Reinforcement Learning | DeepMind & UCL on Reinforcement Learning |
| 28 | Stanford Machine Learning Full Course | Full machine Learning course as lecture slides given at Stanford University | Stanford Machine Learning Full Course |
| 29 | Coursera's Deep Learning Specialization | DL Specialization given by teh great Andrew Ng and his team at deeplearning.ai | Coursera's Deep Learning Specialization |
| 30 | Simple Clustering Cheatsheet | Simple Unsupervised Learning Clustering Cheatsheet | Clustering Cheatsheet |
| 31 | Cheatsheet on Confusion Matrix | Cheatsheet on accuracy, precision, recall, TPR, FPR, specificity, sensitivity, ROC and all that stuff in Confusion matrix | Cheatsheet on Confusion Matrix |
| 32 | Cheatsheets for Data Scientists | Various and different cheatsheets for data scientists | Cheatsheets for Data Scientists |
| 33 | K-Means Clustering visualisation | Simple graphics explaining K-Means Clustering | K-Means Clustering visualisation |
| 34 | Youtube channel by 3Blue1Brown | Youtube channel on animated math concepts | Animated math concepts |
| 35 | Essence of Linear Algebra | Youtube playlist on Linear Algebra by 3Blue1Brown | Linear Algebra |
| 36 | The Neuroscience of Reinforcment Learning | The Princeton slides of Neuroscience for Reinforcement Learning | The Neuroscience of Rein forcement Learning |
| 37 | Reinforcement Learning of Drug Design | Reinforcement Learning implementation of Drug Design | Reinforcment Learning of Drug Design |
| 38 | Brain-Computer Interface with backing | Advanced BCI with a flexible and moldable backing and penetrating microneedles | Brain-Computer Interface with backing |
| 39 | Big O Notation | Great and simple explanation on Big O notation | Big O Notation |
| 40 | 6 Data Science Certificates | 6 Data Science Certificates to boost your career | 6 Data Science Certificates |
| 41 | On the Measure of Intelligence | The new concept to measure how human-like artificial intelligence is | On the Measure of Intelligence |
| 42 | A Collection of Definitions of Intelligence | 70-odd definitions of intelligence | A Collection of Definitions of Intelligence |
| 43 | Competition-Level Code Generation with AlphaCode | AlphaCode paper | Competition-Level Code Generation with AlphaCode |
| 44 | Machine Learning | What is Machine Learning? A well explained introdution | Machine Learning |
| 45 | Autoencoders | Introduction to Autoencoders and dive into Undercomplete Autoencoders | Autoencoders |
| 46 | ChatGPT Cheatsheet | A must-have Cheatsheet for anyone that is using ChatGPT a lot | ChatGPT Cheatsheet |
| 47 | Scikit-learn Cheatsheet | Scikit-Learn Cheatsheet fo Machine Learning | Scikit-Learn Cheatsheet |
| 48 | Top 13 Python Deep Learning Libraries | Summary of top libraries in Deep learning using Python | Top 13 Python Deep Learning Libraries |
| 49 | A Simple Guide to Machine Learning Visualisations | Summary of visual inspection on ML models performance | A Simple Guide to Machine Learning Visualisations |
| 50 | Discovering the systematic errors made by machine learning models | Summary to discover errors on Machine Learning models that achieve high overall accuracy on coherent slices of validation data | Discovering the systematic errors made by machine learning models |
| 51 | Hypothesis Testing Explaine? | Explanation of Hypothesis Testing | A Simple Guide to Machine Learning Visualisations |
| 52 | Intro Course to AI | Free introductory AI course for beginner's given by Microsoft | Intro Course to AI |
| 53 | ChatGPT productivity hacks | ChatGPT productivity hacks: Five ways to use chatbots to make your life easier | ChatGPT productivity hacks |
| 54 | Triple Money with Data Science | Article on how a fellow tripled his income with Data Science in 18 Months | Triple Money with Data Science |
| 55 | Predictions on AI for the next 10 years | Andrew Ng's prediction on AI for the next 10 years | Predictions on AI for the next 10 years |
| 56 | Theory of Mind May Have Spontaneously Emerged in Large Language Models | Publication overviewing LLM models like ChatGPT | Theory of Mind May Have Spontaneously Emerged in Large Language Models |
| 57 | How ChatGPT Helps You To Automate Machine Learning? | ChatGPT in Machine Learning | How ChatGPT Helps You To Automate Machine Learning? |
| 58 | The ChatGPT Cheat Sheet | Un-official ChatGPT cheat sheet | The ChatGPT Cheat Sheet |
| 59 | OpenAI Cookbook | Official ChatGPT cheat sheet | OpenAI Cookbook |
| 60 | Knowledge-augmented Graph Machine Learning for Drug Discovery: A Survey from Precision to Interpretability | Graph Machine Learning implementation in Drug Discovery | Knowledge-augmented Graph Machine Learning for Drug Discovery: A Survey from Precision to Interpretability |
| 61 | A Simple Guide to Machine Learning Visualisations | Guide to ML visualisations | A Simple Guide to Machine Learning Visualisations |
| 62 | How to Visualize PyTorch Neural Networks – 3 Examples in Python | 3 examples of PyTorch visualisations | How to Visualize PyTorch Neural Networks – 3 Examples in Python |
| 63 | Role of Data Visualization in Machine Learning | Role of visualisation in ML | Role of Data Visualization in Machine Learning |
| 64 | Interpreting A/B test results: false positives and statistical significance | Interpretation of A/B test results | Interpreting A/B test results: false positives and statistical significance |
| 65 | Complete Guide to A/B Testing Design, Implementation and Pitfalls | Complete Guide to A/B Testing | Complete Guide to A/B Testing Design, Implementation and Pitfalls |
| 66 | Tips for Data Scienists and DataEngineers in their interviews | Tips for interviews by Seattle Data Guy | Tips for Data Scienists and DataEngineers in their interviews |
| 67 | Git Cheat Sheet for Data Science | Cheat Sheet of Git commands for Data Science | Git Cheat Sheet for Data Science |
| 68 | CNN for Breast Cancer Classification | Overview of an algorithm to automatically identify whether a patient is suffering from breast cancer or not by looking at biopsy images | CNN for Breast Cancer Classification |
| 69 | Goodhart’s Law | Overview of Goodhart’s Law used at OpenAI | Goodhart’s Law |
| 70 | How to Build an ML Platform from Scratch | Standard way to design, train and deploy model | directly How to Build an ML Platform from Scratch |
| 71 | Recap of Self-Supervised Learning | Overview of Self-Supervised Learning | Recap of Self-Supervised learning |
| 72 | Recap of MLOps (2021) | Overview of MLOps | Recap of MLOps (2021) |
| 73 | Recap of MLOps (2020) | Overview of MLOps | Recap of MLOps (2020) |
| 74 | Art of Neural Networks | Artistic representations of Neural Networks | Art of Neural Networks |
| 75 | Design patterns of MLOps | A summary of design patterns in MLOps | Design patterns of MLOps |
| 76 | How to Stay on Top of What’s Going on in the AI World | Resources on how to keep up with all the news and navigate through the endless stream of AI information | How to Stay on Top of What’s Going on in the AI World |
| 77 | ChatGPT and Whisper API | Integration tool for developer of ChatGPT and Whisper API | ChatGPT and Whisper API |
| 78 | 20 Machine Learning Projects That Will Get You Hired | Projects thta should get you hired as an ML Engineer | 20 Machine Learning Projects That Will Get You Hired |
| 79 | 7 Top Machine Learning Programming Languages | Top programming languages used in Machine learning | 7 Top Machine Learning Programming Languages |
| 80 | Effective Testing for Machine Learning Projects (Part I) | Blog post on Effective Testing for ML projects (Part I) | Effective Testing for Machine Learning Projects (Part I) |
| 81 | Effective Testing for Machine Learning Projects (Part II) | Blog post on Effective Testing for ML projects (Part II) | Effective Testing for Machine Learning Projects (Part III) |
| 82 | Effective Testing for Machine Learning Projects (Part III) | Blog post on Effective Testing for ML projects (Part III) | Effective Testing for Machine Learning Projects (Part III) |
| 83 | Decision making at Netflix | How Netflix uses A/B tests to make decisions that continuously improve their products, so they can deliver more joy and satisfaction to members | Decision making at Netflix |
| 84 | What is an A/B Test? | How Netflix uses A/B tests to inform decisions and continuously innovate on their products | What is an A/B Test? |
| 85 | Interpreting A/B test results: false positives and statistical significance | Interpreting A/B test results by looking at false positives and statistical significance | Interpreting A/B test results: false positives and statistical significance |
| 86 | Complete Guide to A/B Testing Design, Implementation and Pitfalls | End-to-end A/B testing for your Data Science experiments for non-technical and technical specialists with examples and Python implementation | Complete Guide to A/B Testing Design, Implementation and Pitfalls. |
| 87 | 10 Statistical Concepts You Should Know For Data Science Interviews | Statistical Concepts necessary to be known for Data Science interviews | 10 Statistical Concepts You Should Know For Data Science Interviews. |
| 88 | Evaluating Deep Learning Models: The Confusion Matrix, Accuracy, Precision, and Recall | Overview of evaluating ML models with metrics of Confusion Matrix, Accuracy, Precision, and Recall | Evaluating Deep Learning Models: The Confusion Matrix, Accuracy, Precision, and Recall |
| 89 | Artificial intelligence in medicine: Overcoming or recapitulating structural challenges to improving patient care? | Perspective of AI in Medicine | Artificial intelligence in medicine: Overcoming or recapitulating structural challenges to improving patient care? |
| 90 | Graph Neural Network in Drug Discovery | Deep Learning application to transform Drug Discovery process to increase the efficiency in finding new compounds | Graph Neural Network in Drug Discovery |
| 91 | New AI approach to reduce noise in X-ray data | Overview of the usage of autoencoders to replace noisy X-ray data with noise-free input signals | New AI approach to reduce noise in X-ray data |
| 92 | Natural Language Processing | The guide covers how it works, where it is applied top technques and more | Natural Language Processing |
| 93 | Big O Cheatsheet | Big O Cheatsheet for Data Structures #1 | Big O Cheatsheet |
| 94 | Big O Cheatsheet | Big O Cheatsheet for Data Structures #2 | Big O Cheatsheet |
| 95 | A Comprehensive Survey of AI-Generated Content (AIGC): A History of Generative AI from GAN to ChatGPT | A historical overview of generative AI techniques and applications | A Comprehensive Survey of AI-Generated Content (AIGC): A History of Generative AI from GAN to ChatGPT |
| 96 | ChatDoctor | A Medical Chat Model Fine-tuned on LLaMA Model using Medical Domain Knowledge | ChatDoctor |
| 97 | ALL CHEAT SHEET | Cheatsheets from Artificial Intelligence to Data Engineering to Machine Learning to Linux to Mathematics to R to Matlab and many more fields | ALL CHEAT SHEET |
| 98 | GMAI | Paper on a Generalist Medical AI (GMAI) to drive the development of large-scale medical AI models, increase accuracy on medical tasks, make complex medical information easier to access and assist surgical teams | GMAI |
| 99 | 9 essential ChatGPT prompts | 9 essential ChatGPT prompts with examples | 9 essential ChatGPT prompt |
| 100 | IPython ChatGPT extension | Extension that allows you to use ChatGPT directly from your Jupyter Notebook or IPython Shell | IPython ChatGPT extension |
| 101 | OpenAssistant | Open-source alternative to ChatGPT | OpenAssistant |
| 102 | DINOv2 | Unsupervised Vision Transformer Model can be used as a backbone for almost all your CV tasks | DINOv2 |
| 103 | Datamol | Open-source toolkit that simplifies molecular processing and featurization workflows for ML scientists in drug discovery | Datamol |
| 104 | ChatGPT vs GPT4 comparison | Image comparing ChatGPT with GPT | ChatGPT vs GPT4 comparison |
| 105 | Self-Supervised Learning Cookbook | Research and all the notes on the dark matter of intelligence | Self-Supervised Learning Cookbook |
| 106 | Prompt Engineering Cheat Sheet | Helping to write great prompts to Chat Bots like GPT | Prompt Engineering Cheat Sheet |
| 107 | GitHub Copilot Guide | GitHub Copilot Guide as slides | GitHub Copilot Guide |
| 108 | Comparison of GitHub Copilot with ChatGPT | Comparison of a chatbot with a programming helper as slides | Comparison of GitHub Copilot with ChatGPT |
| 109 | Comparison of GitHub Copilot with Codeium | Comparison of coding helpers; one payable, other open source | Comparison of GitHub Copilot with Codeium |
| 110 | Getting started with AutoGPT | Getting started with AutoGPT - Insallation - Use Cases - Possibl Misuse | [https://github.com/aurimas13/Machine-Learning-Goodness/blob/main/Notes/AutoGPT_Guide.pdf] |
| 111 | Useful AI Tools | Useful AI Tools from Copilot to AutoGPT to MidJourney to Grammarly to converational bots | [https://github.com/aurimas13/Machine-Learning-Goodness/blob/main/Notes/Useful_AI_Tools.pdf] |
| 112 | ChatGPT Prompting Cheat Sheet | Cheatsheet of useful ChatGPT prompts | ChatGPT Prompting Cheat Sheet |
| 113 | MACHINE LEARNING A First Course for Engineers and Scientists | Machine Learning beginner to advaned informaton from Cambridge University | MACHINE LEARNING A First Course for Engineers and Scientists. |
| 114 | Machine Learning Projects | Machine Learning Projects | Machine Learning Projects |
| 115 | Python Data Science Handbook | Python Data Science Handbook | Python Data Science Handbook |
| 116 | An Introduction to Statistics with Python | Statistics is a branch of mathematics that deals with the collection, analysis, interpretation, presentation, and organization of data | An Introduction to Statistics with Python |
| 117 | Python for Everybody | Python for Everybody | Python for Everybody |
| 118 | Machine Learning with Python for Everyone (Addison-Wesley Data & Analytics Series) | Machine Learning with Python for Everyone | Machine Learning with Python for Everyone (Addison-Wesley Data & Analytics Series) |
| 119 | Python for Data Analysis | Python for Data Analysis | Python for Data Analysis |
| 120 | Python Data Science Essentials | Python Data Science Essentials | Python Data Science Essentials |
| 121 | Graph Data Modeling with Python | Graph Data Modeling with Python | Graph Data Modeling with Python |
| 122 | 50 Days of Python — A Challenge a Day. | 50 Days of Python — A Challenge a Day. | 50 Days of Python — A Challenge a Day. |
| 123 | Tiny Python Projects | Tiny Python Projects | Tiny Python Projects |
| 124 | Mind Blowing AI tools | AI tools from writing to video to design to productivity to marketing to Chatbot | Mind Blowing AI tools |
| 125 | 150+ Python Projects with Source Code | 179 Python Projects with Source Code | 150+ Python Projects with Source Code |
Worthy GitHub repositories related to the ML/DL/NN/AGI courses with all details included can be found here:
| Number | Title | Description | Link |
|---|---|---|---|
| 1 | Advanced AI course | Code Academy Advanced AI course in Lithuania | Advanced AI course |
| 2 | GitHub on Coursera's Deep Learning Course | GitHub Repo for Coursera's Deep Learning Specialization by deeplearning.ai | GitHub on Coursera's DL Course |
| 3 | Notes on Coursera's Deep Learning Course | Lecture Notes for Coursera's Deep Learning Specialization by deeplearning.ai | Notes on Cousera's DL Course |
| 4 | Category Theory on Machine Learning | Github containing list of publications of Category Theory in various AI fields | Category Theory on ML |
| 5 | Foundations of Machine Learning | Understand the Concepts, Techniques and Mathematical Frameworks Used by Experts in Machine Learning | Foundations of ML |
| 6 | Awesome RL | Github repository on amazing materials on Reinforcement Learning | Awesome RL |
| 7 | Optimizing Chemical Reactions | Optimizing Chemical Reactions with Deep Reinforcement Learning | Optimizing Chemical Reactions |
| 8 | Machine Learning cheatsheets | Machine Learning cheatsheets on Supervised, Unsupervised & Deep Learning as well as Tips and Tricks | Machine Learning cheatsheets |
| 9 | ML Youtube Courses | Most recent Machine Leaning courses available on Youtube | ML Youtube Course |
| 10 | Machine Learning Course Notes | Notes on the courses related to Machine Learning | Machine Learning Course Notes |
| 11 | Effective Testing for ML Projects | GitHub repository for Effective Testing for ML Projects | Effective Testing for ML Projects |
| 12 | ChatDoctor | GitHub repository for ChatDoctor while it is written about it on 90th day or accessed as 96 item on tool | ChatDoctor GitHub |
| 13 | Auto-GPT | GitHub repository of an experimetal application showcasing the capabilites of GPt4 | Auto-GPT |
| 14 | Vicuna-13B | An open-source chatbot trained by fine-tuning LLaMA on ~70K user-shared ChatGPT conversations | Vicuna-13B |
| 15 | Prompt Engineering Guide | Prompt Engineering Guide | Prompt Engineering Guide |
| 16 | Best-of Machine Learning with Python | 910 curated ML projects | Best-of Machine Learning with Python |
| 17 | Data Science for Beginners - A Curriculum | Azure Cloud Advocates at Microsoft are pleased to offer a 10-week, 20-lesson curriculum all about Data Science | Data Science for Beginners - A Curriculum |
| 18 | Data-Science-Interview-Resources | Data Science Interview Resources | Data-Science-Interview-Resources |
| 19 | AWESOME DATA SCIENCE | Open source Data Science repository to learn and apply data science skills towards solving real world problems | AWESOME DATA SCIENCE |
| 20 | Datamol | Open-source toolkit that simplifies molecular processing and featurization workflows for ML scientists in drug discovery | Datamol |
| 21 | privateGPT | A magical tool where you can ask questions to your documents without an internet connection by just using the power of LLMs | privateGPT |
| 22 | RT-2 modle | A model that uses up to 55B params backbone and fine-tunes it to directly output robot actions that are executed in the real world | RT-2 |
| 23 | GPTCache | A tool that allows you to cache the results of GPT-3 API calls and reuse them later | GPTCache |
| 24 | Awesome AI-Powered Developer Tools | Tools that leverage AI to assist developers in tasks such as code completion, refactoring, debugging, documentation, and more | Awesome AI-Powered Developer Tools |
Done notebooks of various datasets can be found here.
Additional notes that we covered through lectures or material that I mentioned and spoke about can be found here.
Materials from the challenge of #100DaysOfMLCode for each day can be found here under README section there.
Materials from the challenge of #FinishYearWithML for each day can be found here under README section there.
Public folder contains two files:
First nice thing is that you could run Jupyter also through browser by doing so going here and reading more about it in this article.
If you find difficulty in running Jupyter Notebook through Browser then you could use Google Colab by clicking here. Functionalities of both machines are similar.
The Logo of the repository can be found here.
The MIT LICENSE can be found here.