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Machine Learning

To get started with machine learning, you can follow a structured learning path that progresses from beginner to advanced levels. Below is a comprehensive guide to help you navigate through the essential concepts, tools, and projects in machine learning.

Pre-requisites

Before diving into machine learning, ensure you have a solid understanding of the following concepts:

  • Python and pip package management
  • Google Colab or Jupyter Notebooks

Resources

If you want small videos, try the following playlist. I highly recommend it. Also check other playlists on the channel. These are very important for understanding the concepts.

One can start also with the following courses, Try both.

Next Steps

Try applying the concepts you learn in the courses by working on small projects. Here are some project ideas to get you started:

Machine Hack

This is a platform similar to Kaggle, but it is more focused on Indian audience. It has a lot of competitions and datasets that you can explore. Also the difficulty level of the competitions is a bit lower than Kaggle, so it is a good place to start if you are new to machine learning. Here is the link to the website: Machine Hack

Kaggle

Once you feel comfortable with the basics of machine learning, visit Kaggle website and start exploring datasets. Kaggle is a great platform to practice your skills, participate in competitions, and learn from the community.
They have a inbuilt code editor and you can run your code in the browser without installing anything on your local machine. Also, they regularly host competitions where you can apply your skills to real-world problems. Here is the link to the website: Kaggle

Maths and Statistics

Once you are comfortable with the basics of machine learning, and its libraries, you can start learning about the mathematical and statistical concepts that underpin machine learning algorithms.
This is important to understand how the algorithms work and how to tune them for better performance.

The following youtube channel has every mathematical concept for machine learning explained in a simple way. StatQuest with Josh Starmer

Also, there is a Youtuber named Krish Naik who has a lot of videos on machine learning, data science, and deep learning. He explains the concepts in a simple way and also provides practical examples. Here is the link to his channel: Krish Naik