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Step into the world of Machine Learning 🔥

Following is a very simple learning path designed for students to start with Machine learning.

Step 1 : Decide whether Machine Learning is your cup of tea !

Analyze the following !

  • What is Artificial Intelligence (AI) and Machine Learning (ML)?
  • Impact and applications of AI/ML across various industries like Healthcare, Finance, Agriculture etc., and how it's improving human lives.
  • Skills required for AI/ML

Note : Don't let the AI craze which is happening around us let you start with Machine Learning. If you are really convinced about it's impact and if you feel it's your cup of tea, then go for it. Spend good time in analyzing these. If Yes, move to the next step

Step 2 : Learn and Practise Python

Tip : Try Jupyter notebook extensions which is totally cool.

Step 2a : Python for Data Science

Note : These are just the most commonly used libraries, not the entire set

Code Samples

Step 3 : Refresh Essential High School Mathematics

Essential areas in Mathematics

  • Statistics
  • Probability Theory
  • Linear Algebra
  • Calculus

Tip : Instead of going by the subjects, go by the topics which is more essential for machine learning

Statistics

Probability Theory

Linear Algebra

Calculus

Tip : It's okay to skip this step and learn the required math concepts where ever you encounter it. But I highly encourage you to go through the basics atleast (Comprehensive Guides and Khan Academy videos in this playlist) since it will make you feel comfortable when you go through the machine learning algorithms.

Step 4 : Structured Online Courses - MOOC

Tip 1: Whenever you learn an algorithm, make sure you do the following things.

  • 1. Understand the basic intuition of how it works (without math)
  • 2. Understand the underlying mathematics (Intuition you have now will give enough confidence to crack the math)
  • 3. Implement the algorithm from scratch in python (Atleast for very important concepts like Linear Regression, Logistic Regression, Gradient Descent, Neural Networks etc.,)
  • 4. Solve a simple real world problem by downloading a relevant dataset. You can use Machine Learning libraries in this phase

Tip 2: Document and Maintain your code in a github

Step 4a : Exploratory Data Analysis (EDA)

Step 4b : Machine Learning Pipeline

Important Tip : Start learning EDA and Machine Learning pipeline parallely once you are done with Linear and Logistic regression algorithms in the MOOC

Step 5 : Start solving problems with datasets.

Step 6 : Start with Deep Learning

Note: Do any one of the above courses.

Sample/Recommended Plan

Assuming 8-10 hours a week,

Steps Topics Approx Time Period
Step 1 Decide whether Machine Learning is your cup of tea Not more than a week
Step 2 Learn and Practise Python Approx 2 weeks
Step 2a Python for Data Science Not more than a week
Step 3 Refresh Essential High School Mathematics Not more than a week
Step 4 Structured online course MOOC + Other Algorithms Approx 2 months
Step 4b Exploratory Data Analysis (EDA) Approx 2 weeks
Step 4c Machine Learning Pipeline Concepts Approx 1 weeks
Step 5 Start solving problems with datasets. Approx 2 weeks
Step 6 Start with Deep learning Approx 2/3 months

Tip : Personalize the above plan as per your needs.

Other Resources for Machine Learning

Learning Resources

Cheat Sheets

Data Sources

Competitions Platforms

Tip : Your first task here is to build a simple end to end pipeline for a given problem, submit the solution and seeing your name in the leaderboard. Then try to improve your solution iteratively which makes you move higher in the leader board and the actual learning happens here in the iteration.

Best Blogs

Best Forums

Tip: Download Feedly app and subscribe to all these blogs, forums and newsletters

Best Youtube Content

Get Engaged with AI Community

Note : Being active in these communities will help you to stay motivated and informed about the latest AI advancements.

Tip : Build a professional profile and follow the active community members and research leaders once you sign up

💥 Important Note : Topics and resources mentioned in this document is just a drop in the ocean. Please don't restrict yourself only to these resources 💥

Learn ! Practise ! Make mistakes ! Learn from those mistakes ! Repeat !

Happy learning guys 👍✌️ !

Connect with me here !