Advanced Classification and Prediction

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Advanced-Classification-and-Prediction

Advanced Classification and Prediction

with Joseph Santarcangelo

Audience:
Anyone interested in Advanced Classification and Prediction

Time to complete:
3 Hours

Available in:
English

This is an introduction course to Machine learning for classification

This course cover several popular classification methods and some validation methods.

You will learn the theory behind k nearest neighbors, Logistic Regression, Support Vector Machines, Trees and Random Forests. Then learn how to apply and test these models on python using scikit-learn.

Course Syllabus

1. Introduction

a) Intro to Classification

b) Preliminaries

2. k nearest neighbors

a) k nearest neighbors

3. Model Validation

a) Cross Validation

4. Linear Classifiers

a) Generic linear classifiers

b) Logistic Regression

c) Support Vector Machines

5. Trees and Random forests

a) Overview of trees and random forests (no training methods )

General Information

This course provides an introduction to classification, the course is broken up into normal section and an advanced section. The normal section will give you a good working knowledge.

The advanced section is similar to what you would see in an applied undergraduate course in Machine learning, with more emphasis on examples then proofs.

Pre-requisites

Some linear algebra but there is a review section and for the advanced section some calculus is required.

Recommended skills prior to taking this course

Some programing skills preferably in python