Machine Learning 101 Classes for Begineers

Machine Learning 101 Class NYC

Python 101: 3 hours
Group size: 3-4

Machine Learning 101 Course Outline

COURSE SYLLABUS GUIDELINES

Machine Learning vs Statistical Modeling & Supervised vs Unsupervised Learning
Machine Learning Languages, Types, and Examples
Machine Learning vs Statistical Modelling
Supervised vs Unsupervised Learning
Supervised Learning Classification
Unsupervised Learning

Supervised Learning I

K-Nearest Neighbors
Decision Trees
Random Forests
Reliability of Random Forests
Advantages & Disadvantages of Decision Trees

Supervised Learning II
Regression Algorithms
Model Evaluation
Model Evaluation: Overfitting & Underfitting
Understanding Different Evaluation Models

Unsupervised Learning
K-Means Clustering plus Advantages & Disadvantages
Hierarchical Clustering plus Advantages & Disadvantages
Measuring the Distances Between Clusters – Single Linkage Clustering
Measuring the Distances Between Clusters – Algorithms for Hierarchy Clustering
Density-Based Clustering

Dimensionality Reduction & Collaborative Filtering

 

 

Syllabus

Unit 1: Introduction and Regression

How to dive into Machine Learning

Simple Linear Regression and Multiple Linear Regression

Forward and Backward Selection

Numpy/Scikit-Learn Lab

Unit 2:

Part Classification I

Logistic Regression – Application in Default and other variables

Discriminant Analysis

Naive Bayes

Supervised Learning Lab

Resampling and Model Selection

Cross-Validation

Bootstrap – Breaking it down into simple

Feature Selection

Model Selection and Regularization lab

Unit 3:

Classification II

Support Vector Machines SVM

Decision Trees – and Branch Analysis

Bagging and Random Forests

Decision Tree in Python and SVM Lab

Unit 4:

Unsupervised Learning – Breaking it down

Principal Component Analysis

Kmeans and Hierarchical Clustering

PCA and Clustering Lab

 

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