Another exquisit release
Lynda.Machine.Learning.and.AI.Foundations.Classification.Modeling-XQZT
Title: Machine Learning and AI Foundations: Classification Modeling
Publisher: Lynda
Size: 251M (262090857 B)
Files: 6F
Date: 08/21/2018
Course #: 645050
Type: Big Data
Published: 8/20/2018
Modified: N/A
URL: www.lynda.com/course-tutorials/Machine-Learning-AI-Foundations-Classification-Problems/645050-2.html
Author: Keith McCormick
Duration: 2h
Skill: Beginner
Exer/Code: [ ]
Installation:
Unpack that shit, run that shit
Notes:
One type of problem absolutely dominates machine learning and
artificial intelligence: classification. Binary
classification, the predominant method, sorts data into one of two
categories: purchase or not, fraud or not, ill or not, etc.
Machine learning and AI-based solutions need accurate,
well-chosen algorithms in order to perform
classification correctly. This course explains why
predictive analytics projects are ultimately
classification problems, and how data scientists can choose the
right strategy (or strategies) for their projects.
Instructor Keith McCormick draws on techniques from both
traditional statistics and modern machine learning, revealing
their strengths and weaknesses. Keith explains how to define your
classification strategy, making it clear that the right choice is
often a combination of approaches. Then, he demonstrates 11
different algorithms for building out your model, from
discriminant analysis to logistic regression to artificial
neural networks. Finally, learn how to overcome challenges such as
dealing with missing data and performing data
reduction.eeNote: These tutorials are focused on the theory and
practical application of binary classification algorithms. No
software is required to follow along with the course.