Hey guys! Welcome to the Machine Learning Summer Group! Today we’re gonna start off with some tools and prerequisites for ML.

First off, we’d like you to install Anaconda, a free and open-source distribution of Python that contains most of the tools and packages used in ML. Here is the link: https://www.anaconda.com/products/individual

A guide to installing Anaconda: https://www.youtube.com/watch?v=5mDYijMfSzs

With Anaconda comes Jupyter Notebook, a web application to run codes in “blocks” and interact with data dynamically. Here is a link to get you started and give you an insight into how it works: https://www.youtube.com/watch?v=3C9E2yPBw7s

You could also use Google Colab, notebooks that execute on Google’s cloud servers, meaning you could leverage the power of Google’s hardware, including GPUs and TPUs. All you need... is a google account XP. https://colab.research.google.com/notebooks/intro.ipynb

However, we recommend that you install Anaconda and become familiar with Jupyter first.

Another terrific website to check out is Kaggle. It contains thousands of datasets used in ML, regularly hosts competitions, allows u to create notebooks in several languages and much more!

Last but not least, I’ve shared a drive link for a Python Crash Course, which can be completed in about 1.5 hours. It also contains an assignment and its solutions in Jupyter notebooks (the .ipynb files) that can either be opened on Colab or Jupyter once you’ve installed Anaconda. People who are comfortable in Python are free to skip this. However, if you are new to Python, please go through it thoroughly and also follow the BPHC Python Summer course.

Please use bitsmail to open the drive link. https://drive.google.com/drive/folders/1zIm5DiSFhsGo-HuvGRLXAH629zxR4K29?usp=sharing

Happy Learning!

Update:

7th July, 2021: NumPy Material and Practise Notebooks updated in the repo.

8th July, 2021: NumPy Assignment uploaded in the NumPy folder {Deadline : 10th July, 11:59 pm} . Solution to the NumPy practise questions uploaded as well.