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Python to Neural Networks : A Guide for Beginners

shambhavicodes profile image Shambhavi Mishra ・2 min read

Last Month, I completed a year of my exploration with Machine Learning and Data Science and thus, I decided to pen down the resources I have followed till now.

I don’t say this is the way to be followed, this is just my share of experiences and mistakes which landed me here. This summary is for all my peers, who like me, find themselves lost on ‘how to-s’ of Data Science.

Python 101

Video Resources I followed :

Telusko’s Python Playlist : I started learning Python with this series by Navin Reddy which built my foundations for this journey
I have also followed lectures by Charles Severance, you can find them here .
Sentdex is a much recommended YouTube Channel, I came across it pretty late.

Books I followed :

  • Automate the Boring Stuff with Python
  • Fluent Python

Machine Learning

Video Resources I followed :

I started by taking up a Udemy Course which gave an overview of all the algorithms and its implementation, Machine Learning A-Z by Kirill Eremenco.
While I explored the project ideas after the course, I knew I was missing on to something which was deeper and more mathematical when I read that everyone was doing a Machine Learning Course by Andrew Ng. Truly, the mathematical concepts built from this course helped me sail smoothly through the next phase. Here’s the link to the course.
You can find it on Coursera too.

Books I followed :

I have relied majorly on Machine Learning Mastery with Python - Jason Brownlee
I also followed O’Reilly Python Data Science Handbook for a few things.

Delving Deeper to the Neural Nets

  • ‘s Specialisation Course : Again, I have finished them on YouTube (I didn’t know about financial aid a year back on Coursera).
  • Stanford cs230 : Another course taken by Andrew Ng which is based on Deep Learning and its applications.
  • Stanford cs224n : A course based on Natural Language Processing, it was my stepping stone to NLP. I learnt through doing the assignments and following up from books and blogs (say, Olah’s Blog for LSTM). Stanford cs231n : While learning Computer Vision, this course is my guide. Solving assignments and trying out related projects was enough to substantiate the theory. Also, I can share my handwritten notes on this series, if required.

I can’t emphasize more on trying out self-paced Projects to implement whatever you learn! Through the year I have finished many projects, some internship assignments that helped me learn so much.


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