Binary Classification & Explainability for Data Science!

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Kayla Every

46:29

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1. Introduction

1. Introduction to Me.mp4

02:35

2. What Well Be Covering

1. What will you learn.mp4

01:03

3. Problem Statement

1.1 aug test.csv

1.2 aug train.csv

1.3 Kaggle Link.html

1. Problem Statement and Jupyter Notebook Launch.mp4

02:54

4. Data Collection

1. Data Collection.mp4

05:41

2. How do you know your training data is reliable.html

5. Data Exploration

1. Exploratory Data Analysis.mp4

06:48

6. Feature Engineering

1. Feature Engineering.mp4

06:17

2. Different types of Feature Engineering.html

7. Binary Classification Model Selection

1. Model Selection.mp4

02:11

8. Data Transformation

1. Data Transformation.mp4

06:04

9. Binary Classification Model Training

1. Model Training.mp4

02:23

10. Model Evaluation

1. Model Evaluation.mp4

03:49

2. Model Evaluation.html

11. Model Explainability

1. SHAP Values and Explainability.mp4

06:44

2. What is a SHAP Value.html

Description

Predict if a person is looking for a new job or not

What You'll Learn?

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 - what is impacting the model's prediction?

Who is this for?

Data Scientists, Machine Learning Engineers, Data Analysts, and all others interested in Data Science!

What You Need to Know?

Basic understanding of Python and Data Science concepts

More details

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'll 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. Hopefully you will find be able to apply the concepts learned here to some useful projects of your own!

This course is best for those with basic Python knowledge and basic 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 this course is for:
Data Scientists, Machine Learning Engineers, Data Analysts, and all others interested in Data Science!

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Hey Guys! About Me: I'm a Data Scientist turned Machine Learning Engineer who absolutely loves working in tech. I'm always excited to tackle new problems, build out innovative solutions, and experiment with new technology. In my spare time I tinker around with LLMs, work on my app, and play guitar. Why I'm Doing This: I've wanted to put together some courses that contain useful tips, tricks, learnings and demos I've worked on throughout my career for some time now. Hopefully these courses are able to help you in your careers and are also fun!I'll be releasing courses and also youtube vids (shorter engineering tips) throughout the next year. I realize as technology advances and improves, certain vids may no longer be relevant. I'll try my best to update them.

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