Agenda:
- Introduction to Machine Learning
- Exploratory Data Analysis (Feature selection mainly)
- Predictive Modeling (A quick introduction to Supervised an Unsupervised models)
- Introduction to Logistic regression
- Logistic Regression model building using an example use case from Kaggle
Pre-requisites:
1. Python 3 installed
2. Anaconda installed and Jupyter notebook running
3. Download the data (The data file is known as data.csv)
https://www.kaggle.com/leemun1/predicting-breast-cancer-logistic-regression/notebook
4. Download all the dependent python libraries in your system(Pandas, Numpy, Seaborn, Matplotlib, Scikit-learn, Statsmodels)
P.S: Some experience with programming will help. It's not a must though.
The above content, in order to cover elaborately might take 4-5 hours but we are going to try our best to cover it in 2/2.5 hours by not deep diving into the statistics part of most of the topics and getting into the hands-on bits right away.
If people are interested we will set up another workshop so we can go deeper.
Wayfair
GDG Organizer
GDG Organizer
GDG Organizer
Head of AI lab
Co-organizer
