Learn how to code machine-learning/AI with the sklearn library and how to apply that code for data science.
- Classes will cover Pandas-lib, Analyzing data, cleaning data (w/ sklearn), applying a predictor, in-depth on how predictors work & programming your own.
*Python knowledge is strongly recommended
- Classes will cover Pandas-lib, Analyzing data, cleaning data (w/ sklearn), applying a predictor, in-depth on how predictors work & programming your own.
*Python knowledge is strongly recommended
Topic |
Class Recording |
Lecture Notes and Homework |
Date |
1. What is ML?/Overview |
-- |
07/12 |
|
2. Environment Setup |
-- |
07/15 |
|
3. Pandas Library |
-- |
07/19 |
|
4. Kaggle Titanic Problem |
-- |
07/22 |
|
5. Linear Regression |
-- |
07/26 |
|
6. Predictors |
-- |
07/29 |
|
7. Hyper-parameter Tuning |
-- |
-- |
08/02 |