Course Outline

The whole course is broken down into different sections :

1. Machine Learning 101
2. Python - To learn python
3. Work Environment - Introduce you to topics like Jupyter Notebook and Anaconda
4. Data Analysis - How do we analyze the data we have using libraries
5. Numpy
6. Data Visualization - How to do it with MatplotLib.
7. Scikit-learn - Used to train models. Will learn complete workflow of a machine learning project.
8. Supervised Learning
9. Neural Networks
10. Data Engineering - On topics like Hadoop and Spark
11. Story Telling and Communication - You need to be able present your ML work

This course follows a story line where we are going to get hired by a company and all the tasks are thrown at us by a boss.

The Story

We just got hired at Keiko Corp. They are the latest silicon valley startup. They do a lot of ML projects. We are the new Machine Learning and Data Science expert. Bruno is our boss and he is really excited to make an impact at Keiko Corp by our help. But since you dont know what is ML you called your childhood friends (in this case the creater of the course) to help you in this tricky scenerio. So they are going to help you throughout the journey so that you dont get fired form the job. So it will work like this - Each day you are going to get some task at work and then your childhood friends are going to help you to solve it.