The master python for data science is an initiative of the Nairobi Women in Machine Learning and Data Science community. This initiative sorts to empower community members by improving their skill sets and by so doing enabling the members to be ready to take up the various opportunities in the industry.
The course is virtual and completely self paced with a slack community to help you collaborate with others who are interested in the course as well.
At the end of this assignment you will be required to upload a notebook with the data challenge (as per below) before you proceed to the next assignment.
For this lesson we will move through chapter one to three of the Python for data science handbook by Jake VanderPlas (link below) and learn:
Chapter 5 : Machine Learning
- What Is Machine Learning?
- Introducing Scikit-Learn
- Hyperparameters and Model Validation
- Feature Engineering
- In Depth: Naive Bayes Classification
- In Depth: Linear Regression
- In-Depth: Support Vector Machines
- In-Depth: Decision Trees and Random Forests
- In Depth: Principal Component Analysis
- In-Depth: Manifold Learning
- In Depth: k-Means Clustering
- In Depth: Gaussian Mixture Models
- In-Depth: Kernel Density Estimation
- Application: A Face Detection Pipeline
- Further Machine Learning Resources
We will be looking at a competition on Kaggle: Santander Customer Transaction Prediction (Link on the datasets channel on the community slack)
In this challenge, we will identify which customers will make a specific transaction in the future, irrespective of the amount of money transacted.
Submissions in this challenge will be in the form of Python data analysis using Jupyter Notebooks to the repository. To make this as interactive as possible, everyone will share links to their notebooks that are well documented on Slack as soon as they're done with their analysis. You can find the data on the competitions page.
This will be a good chance to learn how to make reports using Jupyter notebook and make visualizations. Looking forward to what you will come-up with