aurimas13/machine-learning-goodness

The Machine Learning project including ML/DL projects, notebooks, cheat codes of ML/DL, useful information on AI/AGI and codes or snippets/scripts/tasks with tips.

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

• Table of contents
• Worthy Books
• Worthy Tools
• Worthy Repositories
• Notebooks
• Notes
• 100DaysOfMLCode
• FinishYearWithML
• Public
• Jupyter in Browser
• Logo
• License

Worthy Books

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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 Tools

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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 Repositories

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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

Notebooks

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Done notebooks of various datasets can be found here.

Notes

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Additional notes that we covered through lectures or material that I mentioned and spoke about can be found here.

100DaysOfMLCode

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Materials from the challenge of #100DaysOfMLCode for each day can be found here under README section there.

FinishYearWithML

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Materials from the challenge of #FinishYearWithML for each day can be found here under README section there.

Public

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Public folder contains two files:

• todolist - the TO DO List.
• Images - the Images folder.

Jupyter in Browser

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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.

Logo

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The Logo of the repository can be found here.

License

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The MIT LICENSE can be found here.