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Python Machine Learning

via VDCI

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Overview

Master the basics of machine learning, including regression analysis and classification algorithms, in this hands-on course. Develop the skills to tackle real-world problems using machine learning, with a focus on Python programming and key data science libraries.

Syllabus

Fundamentals

Basic Regression Analysis

Linear Regression
Mean squared error
Training set vs Test set
Cross validation

Advanced Regression Analysis

Multi-linear regression
Feature engineering
Overfitting

Classification

Logistic Regression

Regression vs Classification
Logistic Regression
Sigmoid function

K-nearest Neighbors

K-nearest neighbors
Model-based vs memory-based
Parametric vs non-parametric
Evaluating performance

Decision Trees

Decision Trees

Decision tree
Interpretability
Bias-variance tradeoff

Random forest

Random forest
Ensemble methods
Hyperparameters

Final Portfolio Project

Taught by

Garfield Stinvil, Colin Jaffe, and Brian McClain

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