Machine Learning Roadmap
1.0.0
Hello there. I am Shanmukha Sainath, working as AI Engineer at KLA Corporation. I have done my Bachelors from Department of Electronics and Electrical Communication Engineering department, IIT Kharagpur.
Internet world is huge, so as resources to learn any new things. There are numerous free and paid resources to learn Machine Learning. Having many options in hand confuses and it's difficult to select best one (saying from experience). So, I have collected best resources to get started with Machine Learning and continue career in this field.
Feedback and suggestions are welcome :)
18.06 Linear Algebra course by MIT is the best course to learn basics of Linear Algebra
Matrices course by Khan Academy is the best course to learn basics of Matrix Algebra
Statistics and Probability course by Khan Academy is best course available.
Differential Calculus is the best course to learn basics of Differential Calculus.
6.006 Intoduction to Algorithms is the course by MIT to learn basics of Data Structures and Algorithms.
Python tutorial is best place to learn basic syntax of Python.
But I am sharing other resources for some libraries to learn them quickly. Whenever you got stuck at some function or implementation. It is always better to refer documentation/tutorials/code present in official website.
NumPy is a library that enables Numerical Computing in Python. In Machine Learning we always work with arrays. NumPy helps to operate these arrays using large number of functions available.
pandas is a fast, powerful, flexible and easy to use open source data analysis and manipulation tool, built on top of the Python programming language. To know more about usage and advantages of Pandas visit Package Overview page
This will help to get used to some frequent operations done with Pandas.OpenCV-Python is a library of Python bindings designed to solve computer vision problems. OpenCV-Python is a Python wrapper for the original OpenCV C++ implementation.
Refer to official tutorials for more details and implementation.The Python Imaging Library adds image processing capabilities to Python interpreter. This library provides extensive file format support, an efficient internal representation, and fairly powerful image processing capabilities.
NLTK is a leading platform for building Python programs to work with human language data. It provides over 50 corpora and lexical resources such as WordNet, along with a suite of text processing functions for classification, tokenization, stemming, tagging, parsing, and semantic reasoning, wrappers for industrial-strength NLP libraries
This will help to get used to some frequent operations done with NLTK.spaCy is an open-source software library for advanced Natural Language Processing, written in the programming languages Python and Cython.
This course by spaCy helps to get started with spaCy.Matplotlib is a comprehensive library for creating static, animated, and interactive visualizations in Python.
Refer to official tutorials for more details and implementation.Seaborn is a Python data visualization library based on matplotlib. It provides a high-level interface for drawing attractive and informative statistical graphics.
Refer to official tutorials for more details and implementation. Refer to gallery to knoe about various types of plots present in seaborn.Plotly's Python graphing library makes interactive, publication-quality graphs. Examples of how to make line plots, scatter plots, area charts, bar charts, error bars, box plots, histograms, heatmaps, subplots, multiple-axes, polar charts, and bubble charts.
Scikit-learn is a free software machine learning library for the Python programming language. It features various classification, regression and clustering algorithms. It is designed to interoperate with the Python numerical and scientific libraries NumPy and SciPy.
Intro to ML with Scikit-Learn and 50 scikit-learn tips are best freely available courses provided by Data School to learn Scikit-Learn
fastai is a deep learning library which provides practitioners with high-level components that can quickly and easily provide state-of-the-art results in standard deep learning domains, and provides researchers with low-level components that can be mixed and matched to build new approaches. Check About page for more information.
Refer to official tutorials for more details and implementation.PyTorch is a Deep Learning framework developed by Meta that enables fast, flexible experimentation and efficient production through a user-friendly front-end, distributed training, and ecosystem of tools and libraries.
TensorFlow is a Deep Learning framework developed by Google. It is a free and open-source software library for machine learning and artificial intelligence. It can be used across a range of tasks but has a particular focus on training and inference of deep neural networks.
Kaggle is biggest data sceince community where one can share their work, particpate in competitions, learn from free courses and lot more.
To get more out of Kaggle, participate in any competition which is in field of your interest. Competitions are aminly divided into 3 categories Tabular, Computer Vision, NLP. If there are no any active competitions attempt past competitions which interests you. If you got stuck at any point refer publicly avaliable notebooks / post in discussion forum. There are enoromous number of datasets available on Kaggle. You can also download datasets and start your own project
This website contains a list of ongoing ML competitions across various platforms
This blog written by Vetrivel PS has list of Data Science competition platforms.
Papers with Code is a free and open resource with Machine Learning papers, code, datasets, methods and evaluation tables.
Everything in PwC are divided into categories which makes it easy to get particular paper. Go to the category / field that interests you (Browse State-of-the-Art). Select any paper based on benchmarked dataset / Most implemented / Libraries. You can also find code implementations in various frameworks.
Read the paper. Implement the algorithm/model with your favourite framework. Train it with dummy data to check. It's best way to get into research.
Andrej Karpathy
KrishNaik
StatQuest with Josh Starmer
3Blue1Brown
DeepLearningAI
Lex Fridman
Yannic Kilcher
Henry AI Labs
What's AI
Daniel Bourke
TensorFlow
deeplizard
Aladdin Persson
Digital Sreeni
AI Summer
Distil
Google AI
Meta AI
Open AI
AWS Machine Learning
Microsoft AI Blog
Lil'Log
Hack Weekly
DeepwizAI
aman.ai
Papers with Code
arXiv
arXiv Sanity
SciHub
Kaggle
Papers with Code
Open ML
CS231n : Computer Vision
CS224n : Natural Language Processing
CS224W : Machine Learning with Graphs
CS285 : Reinforcement Learning
llm-course : Compiled Resources to learn about LLMs
DeepLearning.AI
Alpha Signal: The weekly digest for AI Researchers and Engineers
Papers with Code
What's AI
DAIR.AI's Top ML papers of the week
Daily Dose of Data Science
This site has all list of available Cloud GPUs and their pricing
Yannic Kilcher (Discord)
CORD.ai (Slack)
MLSpace: The Machine Learning Community (Abhishek Thakur) (Discord)
Weights & Biases: Train and fine-tune models, manage models from experimentation to production
Hugging Face: The platform where the machine learning community collaborates on models, datasets, and applications.
PyTorch Lightning: PyTorch Lightning is the deep learning framework for professional AI researchers and machine learning engineers who need maximal flexibility without sacrificing performance at scale.
AutoMl Libraries: PyCaret, H2o AutoML, AutoKeras, FLAML
Deployment [Beginner]: Flask, Streamlit
LangChain: LangChain is a framework designed to simplify the creation of applications using large language models.
