Jupyter notebooks
Unit 1: Programming refresher
| Lesson | Topics | Notebook | GitHub | Download |
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| 02: Programming Basics | Running jupyter notebooks | Dog Simulation |
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| 04: Python Data Types & Operators | Data types, operators, files & error handling | In-class demo |
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| Activity solution |
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| 05: Loops & conditionals | If statements, for & while loops, range() function | In-class demo |
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| Activity solution |
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| 06: Python functions | Defining & calling functions, arguments, generators | In-class demo |
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| Activity solution |
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Unit 2: Applied data science with Python
| Lesson | Topics | Notebook | GitHub | Download |
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| 09: NumPy | Arrays & array operations, indexing, slicing | In-class demo |
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| Activity solution |
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| Extra NumPy practice |
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| Extra NumPy practice (Solutions) |
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| Extra NumPy slicing & indexing practice |
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| Extra NumPy slicing & indexing practice (Solutions) |
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| 10: Pandas | DataFrames, series, data manipulation | In-class demo |
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| Strings & Sorting practice |
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| Series practice |
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| Datetime practice |
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| DataFrame practice |
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| INC1: Incremental capstone 1 | Import and clean data | Incremental capstone 1 solution |
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| 11: Data visualization | Matplotlib, Seaborn | In-class demo |
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| Vocareum activity solution |
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| 12: Math and statistics fundamentals | Linear algebra, statistics basics | Feature space and model complexity demo |
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| Statistical data types demo |
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| Anscombe's quartet demo |
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| 13: Probability distributions | Discrete and continuous distributions, central limit theorem | In-class demo |
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| 14: Advanced statistics | Confidence intervals, hypothesis testing | In-class demo |
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| Activity solution |
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| 15: Data Wrangling | Manipulating and cleaning data | In-class demo |
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| Activity solution |
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| 16: Feature Engineering | Creating and transforming features | In-class demo |
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| Unit2_Competition: Unit 2 Competition | Become Obsessive about a r2 | AndrewT - (0.29638) |
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Unit 3: Machine learning
| Lesson | Topics | Notebook | GitHub | Download |
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| 19: Supervised learning regression | Regression models: types, training, evaluation, regularization | In-class demo |
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| Activity solution |
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| 20: Supervised learning classification | Classification models: types, training, evaluation, regularization | Demo |
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| INC6: Save the Princess | Incremental capstone 6 | Save the Princess Starter |
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| Save the Princess (Solution) |
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| 21: Ensemble methods | Ensemble learning: bagging, boosting, stacking | Demo |
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| 22: Unsupervised learning | Clustering, dimensionality reduction | In-class demo |
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| Activity solution |
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| 23: Recommendation Systems | Collaborative, Content, and Hybrid Filtering. | In-class demo |
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| Andrew's Partial Activity |
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| Andrew's Partial Activity Solution |
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Unit 4: Deep Learning with TensorFlow and PyTorch
| Lesson | Topics | Notebook | GitHub | Download |
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| 26: Artificial neural networks | Supervised ML with simple neural networks in sklearn | In-class demo |
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| 27: Deep neural networks | Supervised ML with simple neural networks in sklearn | In-class demo (same as Lesson 26) |
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| Activity (same as Lesson 26) |
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| 28: TensorFlow | Regression and classification with neural networks in TensorFlow | In-class demo, part 1 |
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| In-class demo, part 2 |
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| Activity |
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| Activity solution |
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| 29: PyTorch | Basic deep learning with PyTorch | In-class demo, part 1 |
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| Activity part 1 |
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| Activity part 1 solution |
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| In-class demo, part 2 |
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| Activity part 2 |
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| Activity part 2 solution |
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| 31: Convolutional Neural Networks | Convolutional and pooling layers, CNN architectures | In-class demo |
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| Activity solution |
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| 30: Model optimization and performance improvement | Optimizers, hyperparameter tuning | In-class demo, part 1 |
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| In-class demo, part 2 |
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| Activity |
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