Micro-Lectures
Basic Concepts:
Classification
Regression
Clustering
Pre-Processing
Handling Missing Values
Encoding
Outliers
Scaling
Dimensionality-Reduction
Evaluation
Classification Metrics
Regression Metrics
Clustering Evaluation
Leave-Out & Cross-Validation
Generalization
Models and Model Selection
Linear Regression
K-Neirest Neighbors
Random Forests
Neural Networks
Tuning
Tool Introduction Units
Python Crash Course
Variables, Basic Data-Types, Collections
Branching and Looping
Functions, Classes, Modules
Pandas
DataFrames & Basic Operations
Filtering
Merging
Grouping
Melt & Pivot
Seaborn
Pandas Integration
Basic Plots: Line, Bar, Dist etc.
Plot Customizations with Matplotlib
Scikit-Learn
Proprocessing: Scalers, Encoders etc.
LinearRegression, NKKRegressor/Classifier, etc.
GridSearchCV