Machine Learning Series Logistic Regression

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Machine Learning Series: Logistic Regression
MP4 | Video: AVC 1280x720 | Audio: AAC 44KHz 2ch | Duration: 1 Hour | 192 MB
Genre: eLearning | Language: English

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 matDescriptionlib. 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.
 

               

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