Binary Classification & Explainability for Data Science!
Comprehensive Machine Learning Project: Predict if a person is looking for a new job or not.
Machine Learning,Artificial Intelligence
Lectures -15
Resources -2
Quizzes -4
Duration -45 mins
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Course 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 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!
Goals
- Hands on Data Science project and experience that can be applied across industries.
- Build a machine learning model that can be 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?
Prerequisites
Basic understanding of Python and Data Science concepts. Beginner to Senior level Data Scientists, Machine Learning Engineers, Data Analysts, and other tech professionals.
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Curriculum
Check out the detailed breakdown of what’s inside the course
Introduction
1 Lectures
-
Introduction to Me 02:35 02:35
Problem Statement
2 Lectures
![Tutorialspoint](/market/public/assets/images/loader.gif)
Data Collection
2 Lectures
![Tutorialspoint](/market/public/assets/images/loader.gif)
Data Exploration
1 Lectures
![Tutorialspoint](/market/public/assets/images/loader.gif)
Feature Engineering
2 Lectures
![Tutorialspoint](/market/public/assets/images/loader.gif)
Binary Classification Model Selection
1 Lectures
![Tutorialspoint](/market/public/assets/images/loader.gif)
Data Transformation
1 Lectures
![Tutorialspoint](/market/public/assets/images/loader.gif)
Binary Classification Model Training
1 Lectures
![Tutorialspoint](/market/public/assets/images/loader.gif)
Model Evaluation
2 Lectures
![Tutorialspoint](/market/public/assets/images/loader.gif)
Model Explainability
2 Lectures
![Tutorialspoint](/market/public/assets/images/loader.gif)
Instructor Details
![Kayla Every](https://www.tutorialspoint.com/assets/profiles/762683/profile/200_160825-1701901873.jpeg)
Kayla Every
Hey everyone! My courses contain useful tips, tricks, and demos I've worked on throughout my career. Hopefully, the learnings from these courses help you in your career as well, and are also fun!
I'll be releasing courses and also YouTube videos (shorter engineering tips) throughout the next year. I realize as technology advances and improves, certain videos may no longer be relevant. I'll try my best to update them.
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