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Hands-on Data Science: Complete Your First Portfolio Project
Welcome
How does this course work? (4:44)
Problem definition (1:56)
Pandas cheat sheet
Your first assignment!
Module 0: Technology
Git (1:38)
Anaconda (Python+libraries) (3:16)
Using Jupyter Notebooks (10:33)
Optional: workaround for slow computers (3:51)
Module 1: Setting the project structure
Creating a GitHub repository (5:14)
Connecting to the GitHub repository (6:16)
Gathering data (8:47)
Module 2: Data exploration
Data exploration assignment
Optional: Module 2 notebook update explanation (2:35)
Data visualization (9:57)
Data problems 1 (6:10)
Data problems 2 (3:09)
Module 3: Data cleaning
Data cleaning assignment
Getting rid of data problems (2:15)
Module 4: Data preparation
Data preparation assignment
Dealing with types (8:28)
Information extraction (4:28)
Data formatting (6:25)
Module 5: Benchmark model
Benchmark model assignment
Setting up the stage (9:01)
Evaluation (8:09)
Module 6: Feature engineering
Feature engineering assignment
Generating new features (6:58)
Adding new data (17:15)
Module 7: Model training
Model training assignment
Setting up the models (8:36)
Performance comparison (5:38)
Model 8: Tuning
Tuning assignment
Evaluating performances (7:55)
Decision on best model (6:59)
Thought on improvements (2:06)
Bonus: Classification
Bonus assignment
Transforming the problem (5:00)
Wrap up
Final touches (14:42)
Final word (1:18)
Bonus: Build a web app for your project
Your feedback
Data visualization
How to read histograms.pdf
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How to read histograms.pdf
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