xREL.v3

University of OXford & University of camBRiDGE

...is a collective term for characteristics that the two institutions share.

Machine Learning Series: Logistic Regression

by Dhiraj Kumar
Publisher: Technics Publications
Release Date: June 2019
ISBN: 9781634625517

Lecture Size: 05x50
Lecture Date: 05.2019

Lecture Link: https://www.oreilly.com/

|  Dhiraj, a data scientist and machine learning evangelist,
| continues his teaching of machine learning algorithms by going
| into the logistic regression algorithm in this video series. Learn
| all about this powerful machine learning classification algorithm
| in this video series containing these 8 topics:

Introducing Logistic Regression. This first video in the
| logistic regression series introduces this powerful classification
| algorithm. The logistic regression algorithm is used when the
| dependent variable or target variable is categorical. Simple
| Logistic Regression and Multinomial Logistic Regression are
| explained. Learn about the five important assumptions of logistic
| regression. Learn about the Sigmoid function.
Contrasting Logistic Regression with Linear Regression. This
| second video in the logistic regression series compares logistic
| regression with linear regression in terms of their purpose, use
| cases, equations, error minimizations, and assumptions.
Preprocessing Data in Logistic Regression. This third video in
| the logistic regression series covers the four ways of
| preprocessing data before performing logistic regression: missing
| data handling, categorical data handling, splitting into train and
| test set, and feature scaling. This video contains a hands-on
| component so you can follow along and preprocess the data set
| using all four approaches.
Using Seaborn for Data Visualization. This fourth video in the
| logistic regression series explains how to perform data
| visualization using Seaborn, which is a Python data visualization
| library based on matplotlib. Seaborn provides the high-level
| interface to create statistical graphs. This video contains a
| hands-on component so you can follow along and create data
| visualization graphs.
Creating a Logistic Model. This fifth video in the logistic
| regression series explains how to create a logistic model using
| the Titantic dataset. The hands-on part of this video uses
| sklearn's LogisticRegression class.
Predicting Output from the Logistic Model. This sixth video in
| the logistic regression series explains how to predict the output
| from a logistic model, using the scikit-learn's predict()
| function.
Checking the Accuracy of a Logistic Model. This seventh video
| in the logistic regression series explains how to check the
| accuracy of a logistic model.
Using the Confusion Matrix to Determine Model Performance.
| This eighth video in the logistic regression series explains how
| to gauge the performance of a logistic model using the confusion
| matrix.