/Machine-Learning-Roadmap

A roadmap for getting started with Machine Learning

MIT LicenseMIT

Introduction

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.

Connect with me:

@shanmukh05

Why I made this?

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 :)

Prerequisites

  • Mathematics
    • Linear Algebra
      • Details

      18.06 Linear Algebra course by MIT is the best course to learn basics of Linear Algebra

    • Matrix Algebra
      • Details

      Matrices course by Khan Academy is the best course to learn basics of Matrix Algebra

    • Probability and Statistics
      • Details

      Statistics and Probability course by Khan Academy is best course available.

    • Calculus
      • Details

      Differential Calculus is the best course to learn basics of Differential Calculus.

  • Programming Fundamentals
  • Programming Language
    • Python
      • Details

      Python tutorial is best place to learn basic syntax of Python.

Machine Learning

Deep Learning

Frameworks/Libraries

"No tutorial/course is better than Documentation :)"

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.

What next ?

  • Competitions
    • Kaggle
      • Details

      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

    • ML Contests

      • This website contains a list of ongoing ML competitions across various platforms

    • List of ML hackathon platforms

      • This blog written by Vetrivel PS has list of Data Science competition platforms.

  • Research
    • Papers with Code
      • Details

      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.

Other Resources

Advanced resources

  • 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.

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