Machine Learning 101 Class Bootcamp Course NYC

1. Python Scikit-learn Library

2. Supervised vs Unsupervised Learning

3. Regression vs Classification models

4. Categorical vs Continuous feature spaces

5. Modeling Fundamentals: Test-train split, Cross validation(CV), Bias–variance tradeoff, Precision and Recall, Ensemble models

6. Interpreting Results of Regression and  Classification Models (Hands On)

7. Parameters and Hyper Parameters

8. SVM, K-Nearest Neighbor, Neural Networks

9. Dimension Reduction

Hands on:

1. Understanding and Interpreting results of Regression and Logistic Regression using Google Spreadsheets and Python

2. Calculating R-Square, MSE, Logit manually in excel for enhanced understanding (Multiple Regression)

3. Understanding features of Popular Datasets: Titanic, Iris (Scikit) and Housing Prices

4. Running Logistic Regression on Titanic Data Set

5. Running Regression, Logistic Regression, SVM and Random Forest on Iris Dataset