INSTRUCTOR
Hunter Jackson
COURSE OUTLINE
Each class will be an engaging, interactive session where we build tools together to make predictions about our data. The classes will be focused on actually building the predictive tools; however, each class will have supplementary lecture notes that describe the methodologies in further detail and extra programming tasks if anyone wants extra practice.
Course Info
Class: M/W 6 - 9 pm @ Spark baltimore
Office hours: TBD
Class 1: Course Intro + Installation:
- Class intro: slides
- Class 1 Notes: slides
- Install git and create a github account
- Intro to ipython notebooks
- Python style guide
- Think like a computer scientist
- Tons of additional resources here
- Intro to course project
Class 2: Command Line + Python Basics:
Class 3: Python Basics Pt. 2:
Class 4: Intro to Numpy
Class 5: Intro to Pandas:
Class 6: Data Viz Pt. 1
Class 7: Human Learning and Data Exploration:
- Solutions to writing functions nb
- Iris dataset exploration/Human Learning
- Linear Regression as an exploratory tool
Class 8: Intro to ML
Class 9: Bias Variance Tradeoff
Class 10: K-Nearest Neighbors
- Numpy references
- Sklearn KNN notebook
- KNN Lab
- Model Evaluation
- Interesting article on p-value hacking
Class 11: Logistic Regression and Titanic
Class 12: Getting data from API and Web
Class 13: Natural Language Processing
Class 14: Data Mining Review, Naive Bayes, and AUC Evaluation:
Class 15: Review!
Class 16: Decision Trees
Class 17: Feature Scaling and Decision Trees Lab
Class 18: Ensemble Learning and Recommendation Systems
Class 19: Unsupervised Learning: K-means clustering
Class 20: Unsupervised Learning: Dimensionality Reduction
Class 21: Support Vector Mahchines and Time Series
Class 22: Relational databases
- Relational databases/sql
- Bring your data and lets work on projects!!
React
Typescript
Django
Laravel
Facebook
Microsoft
Google
Alibaba
D3
Tencent