Machine Learning & Explainability for Data Science
Comprehensive Machine Learning Project: Predict if a person is looking for a new job or not.
Ratings 5.00 / 5.00
What You Will Learn!
- Hands on Data Science project and experience that can be applied across industries.
- Build a machine learning model that is used for binary classification problems - will user do A or B?
- Understand the steps required to build a machine learning model - data collection, exploration, transformation, model selection, training and evaluation.
- Understand explainability in Data Science using SHAP and derive data insights - what is impacting the model's prediction?
Description
You will build a binary classification machine learning model to predict if a person is looking for a new job or not. You'll go through the end to end machine learning project-- data collection, exploration, feature engineering, model selection, data transformation, model training, model evaluation and model explainability. We will brainstorm ideas throughout each step and by the end of the project you'll be able to explain which features determine if someone is looking for a new job or not.
The template of this Jupyter Notebook can be applied to many other binary classification use cases. Questions like -- will X or Y happen, will a user choose A or B, will a person sign up for my product (yes or no), etc. You will be able to apply the concepts learned here to many useful projects throughout your organization!
This course is best for those with beginner to senior level Python and Data Science understanding. For more beginner levels, feel free to dive in and ask questions along the way. For more advanced levels, this can be a good refresher on model explainability, especially if you have limited experience with this. Hopefully you all enjoy this course and have fun with this project!
Who Should Attend!
- Beginner to Senior level Data Scientists, Machine Learning Engineers, Data Analysts, and other tech professionals.