This is what your first 30 days of machine learning should look like.
What you are going to read below is a roadmap for the first 30 days of getting started with machine learning as a complete beginner.
If you don't know how to code or anything about what machine learning is, then this blog post is for you.
Step 1: Learn the basics of Python (~12 days)
Python is a programming language that you can use for machine learning.
Now, why do I recommend Python over any other language?
- It is easy to learn
- Has the largest community for machine learning
- Lots of learning resources
This 4 hour tutorial on FreeCodeCamp will help you get started with Python.
Some of the important topics covered in this tutorial are:
- List Functions
- Return Statement
- If Statements
- If Statements & Comparisons
- Dictionaries ... and lots more
Getting through this course will take you about 12 days if you spend an hour and a half on it each day. Take your time to understand every concept and ask for help online if needed.
Make sure you're consistent.
Step 2: Understand how machine learning works. (~3 days)
This video series by 3blue1brown will help you understand how neural networks work.
It will take you roughly 3 days to complete this series.
Neural networks are just one part of machine learning, there are many other "classical" machine learning algorithms as well.
However, to keep things simple, we'll only be focusing on neural networks.
Step 3: Get started with Machine Learning (~15 days)
Now it is time for you to dive into machine learning, this 10 part course will teach you the fundamentals of machine learning in TensorFlow.
You'll learn about:
- Neural Networks
- Computer Vision
- Convolutions and pooling
- Image Augmentation
- Natural Language Processing
This roadmap will provide a solid foundation to your machine learning journey, cheers!
Before you leave, make sure to follow me on Twitter👇
Interested in reading more such articles from Pratham Prasoon?
Support the author by donating an amount of your choice.