In these 26 Days work with the following resources:
- Jupyter Notebooks with code snippets relevant to the pertaining topic dedicated for the Day
- Materials to upskill your understanding on the topic and help you see in new light
- Cheatsheets for every topic to help you think in the nick of time
- Reality anchoring tips - once per Day - to keep you focussed on the common goal.
Below is a brief description of what a learner can expect from this program:
- The initial Days would lay the foundations in programming & statistics on which we would model our study on machine learning.
- We'd start off with a primer on Git followed by the basics of Object Oriented programming in Python. Following this we'll slowly advance into Numpy, Pandas and Visualizations
- Once we get a tight hold on Python, we'll venture into statistics and exploratory data anlaysis which would involve a lot of the python and visualzation skills we'd learnt previously, to make the data ready for applying machine learning models.
- In the middle of the course you’ll learn about the supervised algorithms like Linear Regession, Logistic Regression,Decision Trees,Random Forests etc, and unsupervised ones like K-Means Clustering and a few more, how to implement them from scratch and how to use them for prediction tasks etc.
- In addition to our notebooks, the bonus materials in accordance to the respective topics will serve you well by giving you the industry perspective of machine learning.
- Towards the end of the study plan, we’ll delve a little into Natural Language Processing & Recommender Systems and make you adept at how to apply your machine learning knowledge for real world applications.