Predictive Analytics 2 with Python – Neural Nets and Regression
As a continuation of Predictive Analytics 1, this course introduces to the basic concepts in predictive analytics, with a focus on Python, to visualize and explore predictive modeling.
Overview
In this course you will continue work from Predictive Analytics 1, and be introduced to additional techniques in predictive analytics, also called predictive modeling, the most prevalent form of data mining. Predictive modeling takes data where a variable of interest (a target variable) is known and develops a model that relates this variable to a series of predictor variables, also called features. Four modeling techniques will be used: linear regression, logistic regression, discriminant analysis and neural networks. The course includes hands-on work with Python, a free software environment with capabilities for statistical computing.
- Introductory, Intermediate
- 4 Weeks
- Expert Instructor
- Tuiton-Back Guarantee
- 100% Online
- TA Support
Learning Outcomes
In this course, students will learn:
- Distinguishing between profiling (explanation) tasks and prediction tasks for linear and logistic regression
- Specifying and interpreting linear regression models to predict continuous outcomes
- Specifying and interpreting logistic regression models for classification
- Using discriminant analysis for classification
- Using neural nets for prediction and classification
- Preprocessing text for text mining, and using a predictive model with the resulting matrix
Who Should Take This Course
Marketing and IT managers, financial analysts and risk managers, accountants, data analysts, data scientists, forecasters. This course is especially useful if you want to understand what predictive modeling might do for your organization, undertake pilots with minimum setup costs, manage predictive modeling projects, or work with consultants or technical experts involved with ongoing predictive modeling deployments.
Our Instructors
Course Syllabus
Week 1
Linear and Logistic Regression
- Review Predictive Analytics 1
- Linear regression for descriptive modeling
- Fitting the model Assessing the fit
- Inference
- Linear regression for predictive modeling
- Choosing predictor variables
- Generating predictions
- Assessing predictive performance
- Logistic regression for descriptive modeling
- Odds and logit
- Fitting the model
- Interpreting output
- Logistic regression for classification
- Choosing predictor variables
- Generating classifications and probabilities
- Assessing classification performance
Week 2
Discriminant Analysis and Neural Nets
- Discriminant analysis for classification
- Statistical (Mahalanobis) distance
- Linear classification functions
- Generating classifications
- Rare cases and asymmetric costs
- Integrating class ratios and misclassification costs
- Neural network structure
- Input layer
- Hidden layer
- Output layer
- Back propagation and iterative learning
Week 3
Text Mining
- Representing text in a table
- Term-document matrix
- Bag of words
- Preprocessing of text
- Tokenization
- Text reduction
- Term Frequency – Inverse Document Frequency (TF-IDF)
- Fitting a predictive model
Week 4
Additional Topics: Looking Ahead
- Multiclass classification
- Network analytics
- Project
Class Dates
2022
Instructors:
Instructors:
2023
Instructors:
Instructors:
Instructors:
2024
Instructors:
Prerequisites
Predictive Analytics 1 with Python – Machine Learning Tools
- Skill: Introductory, Intermediate
- Credit Options: ACE, CAP, CEU
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Predictive Analytics 2 with Python – Neural Nets and Regression
Additional Information
Homework
In addition to assigned readings, this course also has supplemental video lectures, and an end of course data modeling project.
Course Text
The required text for this course is Data Mining for Business Analytics: Concepts, Techniques, and Applications in Python, by Shmueli, Bruce, Gedeck and Patel. This same text is also used in the these courses: “Predictive Analytics 1 – Machine Learning Tools – with Python” and “Predictive Analytics 3 – Dimension Reduction, Clustering and Association Rules – with Python”. Please order a copy of your course textbook prior to course start date.
Software
The course includes hands-on work with Python, a free software environment with statistical computing capabilities.
Supplemental Information
Literacy, Accessibility, and Dyslexia
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Register For This Course
Predictive Analytics 2 with Python – Neural Nets and Regression