Course 811:
Machine Learning Concepts

(2 days)


Course Description

Overview of Machine Learning concepts for managers, programmers, and data analysts unfamiliar with what ML can do and how it can be applied to solve various business cases that traditional queries and reporting can’t.

Learning Objectives

Obtain an understanding of Machine Learning concepts, terminologies, and tools to quick start attendees on their path to mastering this complex subject and incorporate these technologies into their own use cases. Machine Learning involves a combination of many different disciplines such as advanced mathematics, data manipulation, programming, and business analysis. The goal of this class is to introduce attendees to such topics as:

  • What are predictive models and how they are used
  • Understanding of basic statistics used in generating the results
  • Differences between Cluster, Classification, and Regression Analysis
  • Typical use cases in various industries
  • Overview of some of the tools, languages, and frameworks used with emphasis on Python to illustrate the typical lifecycle of the ML process


Basic understanding of databases and light familiarity SQL with some basic programming experience in any language is helpful.

Who Should Attend

Managers, programmers, data analysts, report writers, or anyone who wants to learn the big picture of what Machine Learning is all about and become familiar with the technologies involved.

Course Outline


  • Concepts
    • Distinguish Traditional Reporting Using SQL vs. Modeling Using Statistics & Machine Learning
    • Making Predictions and Identifying Patterns or Similarities
  • Math
    • Basic Primer on Statistics
      • Central Tendency
      • Exploratory Data Analysis
      • Randomness

 Business Cases

  • Traditional Business Reporting
    • SQL
    • Data Warehousing
  • Predictions and Pattern Recognition
    • Identify Natural Groupings (Cluster Analysis)
    • Predicting Future Values (Regression Analysis)
      • Estimate the Future Cost of a Commodity
      • Predict the Value of a Stock
    • Classify a Transaction into a Category (Classification Analysis)
      • Is a Credit Card Swipe Likely to Be Fraudulent
      • Is a Potential Customer Likely to Be Profitable
    • Recommendation Engine
      • Cross-Market Products a Customer is Likely to Be Interested In

 Data Preparation

  • Common ETL
    • Basics of Prepping Data from SQL, No-SQL, Semi-Formatted Text Files Like CSV, etc.
    • Categorical Data
    • Binned vs. Continuous Data
  • Unformatted Text Data Such as Resumes, Twitter, etc.
    • Document Term Matrix
  • Training and Testing Sets


  • Python
    • NumPy, SciPy, Matplotlib
    • Pandas
    • Spark
    • Tensorflow
  • R


  • Concepts
    • Supervised
    • Unsupervised
    • Predicting Classification vs. a Value
  • Linear Regression
  • Classification
    • Logistic Regression
    • Decision Trees
    • Naïve Bayes
    • SVM
    • Neural Networks
  • Regression Trees
  • Cluster Analysis
    • K-Means
    • Hierarchical Clustering


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