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A repo for May 2016 data science training for Capital One.
- Mark Fenner [email protected]
- Michael (Mike) Galvin [email protected]
- Syllabus
- Class Format and Philosophy
- Installation and Configuration
- Post-class: what do I learn next?
- The Zen of Python: Pythonic Principles
- Good Programming Practices
- Python Input/Output, Building Modules
- Debugging in Python
- Organizing Code, Logging, Unit Tests - Failing Helpfully
- Iterators, Generators, Classes
- Linear Regression
- Introduction to Jupyter
- Introduction to Pandas
- Exploratory Data Analysis and Regression with Python
- Python Challenges
- Modeling Challenges
- Visualization Challenges
- Evaluation Metrics
- Train/Test Splits, Cross Validation and Regularization
- Dealing with Categorical Features, Train/Test Splits & Cross Validation in Python
- Classification Error Metrics
- Feature Selection, Feature Extraction, Dimensionality Reduction
- Scikit-learn Algorithm Cheat Sheet
- Supervised Learning: Logistic Regression, KNN
- Supervised Learning Challenges
- Solutions to Supervised Learning Challenges
- Algorithm Bonanza: A Survey of Other Classification Algorithms
- More on Supervised Learning Algorithms: Time Series, Decision Trees, Random Forests
- Even more on Supervised Learning Algorithms: Support Vector Machines, Kernel Trick
- Unsupervised Learning: All Major Clustering Algorithms
- Dealing with BIGish data: Stochastic Gradient Descent
- Fizz Buzz
- Check if Parentheses Match
- Calculating Varience and Covarience
- Project Euler: 13 Adjacent Digits with Max Product
- Cash Register iPad App for Change
- Caesar Cipher Encryption
- Anagram Checker & Guessing Game Strategy
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