Please help us improve and share your feedback! If you find better tutorials or links, please share them by opening a pull request.
In this 5 week module we will introduce students to Python as well as the basic concepts and techniques of Machine Learning. To develop skills of using recent machine learning software for solving practical problems. To gain experience of doing independent study and research.
Prerequisites Introduction to Python and Data Science tools
This course is directed to HackYourFuture Alumni and everyone is expected to have the basic notions of programming.
Preparation Introduction to Python and Data Science tools
Please check the Installation guide below.
Environment setup Introduction to Python and Data Science tools
Installation Guide : Install python 3.75 and Pycharm community (free) version for practical concepts.
For more information, please check this link :https://www.guru99.com/how-to-install-python.html
Week | Topic | Preparation | Lesson plan | Homework |
---|---|---|---|---|
1. | Introduction about course and Python 1.Introduction to Machine learning + application 2.Python installation + Pycharm 3.Python basic course. |
Preparation | Lesson plan | Homework |
2. | More Python and preparation of data 1.More Python1 2.Installing anaconda + Jupyter notebook 3.Importing libraries 4.Input Dataset. |
Preparation TBD | Lesson plan | Homework TBD |
3. | Basic Life cycle of ML model 1.Data cleaning 2.Splitting the dataset into training/test dataset 3.Create a model 4.Train a model 5.Make predictions 6.Evaluate and improve 7. Exercise - Spam classification/prediction system (code for a solution is provided, only parts are covered in class) |
Preparation TBD | Lesson plan | Homework TBD |
4. | Building your own model 1.Introduction to Linear regression. 2.Building of a ML predictive model for Co2 emission based on engine size using Linear regression. |
Preparation TBD | Lesson plan | Homework TBD |
5. | Follow-up on last week’s work and ML Tree structures 1.Supervised learning 2.Unsupervised learning 3.Reinforcement learning |
Preparation TBD | Lesson plan | Homework TBD |
External Documentation Introduction to Python and Data Science tools Content