| What is Jupyter? |
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| Notebook Basics |
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| Running Code |
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| Markdown |
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| More on Colab |
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| Python Overview |
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| Basic Data Structures |
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| Numpy |
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| Pandas |
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| Lab |
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| Homework 1 |
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| Conditionals-Loops |
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| Functions |
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| Null Values |
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| Groupby |
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| Kaggle Baseline |
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| Assignment 2 |
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| Twitter |
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| Web Mining |
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| Visualizations - Seaborn |
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| Strings - Regular Expressions |
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| Feature Dummies |
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| Assignment 3 |
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| The Simplest Neural Network with Numpy |
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| Train Test Split |
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| Introduction to Logistic Regression |
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| K Nearest Neighbor |
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| ROC Support Vector Machines |
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| Visualization & Screen Scraping Homework |
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| Intro Modeling Homework |
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| Matrix Regression |
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| Regression Basics |
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| Ridge and Lasso Regression |
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| Principal Component Analysis |
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| Titanic Feature Creation |
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| Intro to Corpus |
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| Scikit Learn Text |
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| What’s Cooking |
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| Bag of Popcorn |
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| Sentiment |
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| Time Series Intro |
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| Forcasting Rossman |
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| Neural Networks Quick Overview |
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| Tensor Tutorial |
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| Convolutional Neural Network Tutorial |
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| Pytorch MNIST |
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| Introduction to Tensorflow |
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| Convolutional Neural Network Tutorial |
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| Revisiting IRIS with PyTorch |
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| Revisiting Boston Housing with Pytorch |
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| Revisiting Titanic with Pytorch |
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| Revisiting Titanic with Ludwig |
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| Revisiting Titanic with FastAI |
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| FastAI - Tabular |
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| FastAI - Pets |
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| AutoML with TPOT |
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