Introduction To Machine Learning

#programming #python #learning #data #machine

May 6 through June 24, 2023. Saturdays 1:30-4:30pm.  

Register now, last call as its less than 2 weeks away until start date.

The IEEE North Jersey Section Communications Society (ComSoc chapter) is offering a course entitled "INTRODUCTION TO MACHINE LEARNING". As more  and more organizations make a push for data-driven decisions, it is important to know how to extract value from the information available.  This course will provide practical experience with these techniques so students can be productive with computational approach to using modern tools for analyzing data.

This course is an introduction to statistical techniques for machine learning and data mining, assuming basic background knowledge of python programming and basic math.  It emphasizes mathematical methods and computer applications related to automated learning for prediction, classification, knowledge discovery, and forecasting in modern data science. Special emphasis will be given to the collection, mining, and analysis of large data sets.

Statistical software (Python, Scikit-learn) will be used throughout the course for the exploration of different learning algorithms and for the creation of appropriate graphics for analysis. Applications include recommendation systems, predictive customer models, text mining, and sentiment analysis.

The IEEE North Jersey Section's Communications Society Chapter can arrange for providing IEEE Certificate of Completion (free) and CEUs - Continuing Education Units (for a $5 charge) upon completion of the course.  Course prices: $150 for Undergrad/Grad/Life/ComSoc members, $200 for IEEE members, $300 for non-IEEE members

  Date and Time




  • Start time: 06 May 2023 01:30 PM
  • End time: 23 Jun 2023 04:30 PM
  • All times are (UTC-05:00) Eastern Time (US & Canada)
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  • FDU Metropolitan Campus
  • 960 River Road
  • Teaneck, New Jersey
  • United States 07666
  • Building: Becton Building
  • Room Number: Room 302
  • Click here for Map

  • Contact Event Hosts
  • Amit Patel - a.j.patel @ ieee . org  and Kalyan Mondal – k.mondal @ ieee . org

  • Co-sponsored by Education Committee
  • Starts 03 April 2023 07:00 AM
  • Ends 03 June 2023 12:00 AM
  • All times are (UTC-05:00) Eastern Time (US & Canada)
  • Admission fee ?


Thomas Long


Introduction To Machine Learning Course


Thomas Long, teaches in NYC and has over 20 years of academic teaching experience at different universities/institutions as well as research, consulting and entrepreneurship.


Topics: The primary objective of this course is to provide students with an understanding of the statistical tools and techniques used in machine learning and data mining. The material covered includes an introduction to the concepts of machine learning and data mining and uses an applied exploratory approach. On the completion of the course, students will be able to:

1. Describe the concepts of machine learning and identify examples of its use in data science
2. Employ statistical software to collect data,create training and test sets, and perform predictions
3. Identify the characteristics of massive data sets and describe the tools needed to analyze them
4. Create regression models for predicting outcome variables in terms of predictors
5. Perform classifications for data sets using nearest neighbor and probabilistic algorithms
6. Analyze decision tree models and display them with appropriate graphics
7. Introduce unsupervised algorithms using k-means clustering

Subjects covered include: Python and Statistics, Data Cleaning, Exploratory Data Analysis, Regression (Multiple, Polynomial, Logistic), Classification, k-Nearest Neighbors, Decision Trees, Ensemble Methods, Bayes, Unsupervised Learning, k-means clustering.

Technical Requirements: Students will need access to the Python programming language. In addition to a standard Python installation, most programming exercises will use the package Scikit-learn.  Basic programming skills and some familiarity with the Python language are assummed.
Students are expected to be able to bring a laptop onto which most of these libraries can be pre-installed using python's pip install.   to learning more about both Data Science and Python.

The course is intended to be subdivided into 3-hour sessions. Each lecture is further subdivided into lecture, guided and independent project based exercises to build experience with hands-on techniques.  This course will be held at FDU - Teaneck, NJ campus.  Checks should NOT be mailed to this address or online payments collected.  Email the organizer for any questions about course, registration, or other issues.