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Predictive Analytics 2 with Python – Neural Nets and Regression

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.


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

Mr. Kuber Deokar

Mr. Kuber Deokar

Mr. Kuber Deokar holds a Masters degree in Statistics from University of Pune, India, where he also taught undergraduate statistics. Mr. Deokar holds the position of Instructional Operations Supervisor at He is responsible for coordination of online courses, and ensures seamless interactions between the management team, course instructors, teaching assistants, and students. He also serves as the senior teaching assistant and shares instructional responsibilities for several courses, and handles consultancy assignments, working from our office in Pune, India.

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


07/07/2023 to 08/04/2023
Instructors: Mr. Kuber Deokar
11/10/2023 to 12/08/2023
Instructors: Mr. Kuber Deokar


03/08/2024 to 04/05/2024
Instructors: Mr. Kuber Deokar
07/12/2024 to 08/09/2024
Instructors: Mr. Kuber Deokar
11/08/2024 to 12/06/2024
Instructors: Mr. Kuber Deokar


03/14/2025 to 04/11/2025
Instructors: Mr. Kuber Deokar
07/11/2025 to 08/08/2025
Instructors: Mr. Kuber Deokar
11/14/2025 to 12/12/2025
Instructors: Mr. Kuber Deokar


Predictive Analytics 1 with Python – Machine Learning Tools

This course introduces the basic paradigm for predictive modeling: classification and prediction.
  • Skill: Introductory, Intermediate
  • Credit Options: ACE, CAP, CEU
Karolis Urbonas
Susan Kamp
Stephen McAllister
Amir Aminimanizani
Elena Rose
Leonardo Nagata
Richard Jackson

Frequently Asked Questions

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Predictive Analytics 2 with Python – Neural Nets and Regression

Additional Information

Organization of Course

This course takes place online at The Institute for 4 weeks. During each course week, you participate at times of your own choosing – there are no set times when you must be online. Course participants will be given access to a private discussion board. In class discussions led by the instructor, you can post questions, seek clarification, and interact with your fellow students and the instructor.

At the beginning of each week, you receive the relevant material, in addition to answers to exercises from the previous session. During the week, you are expected to go over the course materials, work through exercises, and submit answers. Discussion among participants is encouraged. The instructor will provide answers and comments, and at the end of the week, you will receive individual feedback on your homework answers.

Time Requirements

This is a 4-week course requiring 10-15 hours per week of review and study, at times of your choosing.


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.


The course includes hands-on work with Python, a free software environment with statistical computing capabilities.

Course Fee & Information

Courses may fill up at any time and registrations are processed in the order in which they are received. Your registration will be confirmed for the first available course date unless you specify otherwise.

Transfers and Withdrawals
We have flexible policies to transfer to another course or withdraw if necessary.

Group Rates
Contact us to get information on group rates.

Academic affiliation?  In most courses you are eligible for a discount at checkout.

New to  Click here for a special introductory discount code.

Invoice or Purchase Order
Add $50 service fee if you require a prior invoice, or if you need to submit a purchase order or voucher, pay by wire transfer or EFT, or refund and reprocess a prior payment.

Options for Credit and Recognition

This course is eligible for the following credit and recognition options:

No Credit
You may take this course without pursuing credit or a record of completion.

Mastery or Certificate Program Credit
If you are enrolled in mastery or certificate program that requires demonstration of proficiency in this subject, your course work may be assessed for a grade.

CEUs and Proof of Completion
If you require a “Record of Course Completion” along with professional development credit in the form of Continuing Education Units (CEU’s), upon successfully completing the course, CEU’s and a record of course completion will be issued by The Institute upon your request.

ACE CREDIT | College Credit
This course has been evaluated by the American Council on Education (ACE) and is recommended for college credit.  For recommendation details (level, and number of credits), please see this page. Please note that the decision to accept specific credit recommendations is up to the academic institution accepting the credit.

ACE Digital Badge
Courses evaluated by the American Council on Education (ACE) have a digital badge available for successful completion of the course.

This course is recognized by the Institute for Operations Research and the Management Sciences (INFORMS) as helpful preparation for the Certified Analytics Professional (CAP®) exam and can help CAP® analysts accrue Professional Development Units to maintain their certification.

Supplemental Information

Literacy, Accessibility, and Dyslexia

At, we aim to provide a learning environment suitable for everyone. To help you get the most out of your learning experience, we have researched and tested several assistance tools. For students with dyslexia, colorblindness, or reading difficulties, we recommend the following web browser add-ons and extensions:







  • Navidys (for colorblindness, dyslexia, and reading difficulties)
  • HelperBird for Safari (for colorblindness, dyslexia, and reading difficulties)


There is no additional information for this course.

Register For This Course

Predictive Analytics 2 with Python – Neural Nets and Regression