Machine Learning: Understanding Solutions and Developing Applications – On Demand



Price: $400 With Certification

Format: On-Demand

Duration: 20 Hours

Target Audience

This is a technical, hands-on course where students will work with the latest technologies in a series of labs to learn how to create their own solutions. To be successful in this course you need to be a seasoned software developer who is comfortable and fluent in one or more modern development languages (preferably R, Python, or Spark).


Students are also expected to have a strong knowledge of the mathematical and statistical concepts which underlie Machine Learning as this course will NOT cover these concepts in-depth!

Machine Learning is a discipline of Artificial Intelligence focused on teaching machines to gather and apply knowledge.

We are already beginning to see the profound effects that educated intelligence systems are having on our world. The decade ahead promises to be one in which we will see an explosive growth in Machine Learning applications, techniques, solutions, and platforms.

In this training class we will focus on learning the core concepts of Machine Learning and getting hands-on with the latest technologies to learn how to create your own solutions!

Course Outline

What's Included

  • Chapter 1: What is Machine Learning?
  • Chapter 2: Machine Learning Tools
    • Lab: Getting familiar with ML environment
  • Chapter 3: Machine Learning Concepts
    • Lab: Basic stats
  • Chapter 4: Feature Engineering
    • Lab: visualizing data
  • Chapter 5: Linear regression
    • Lab
    • Use case: House price estimates
  • Chapter 6: Logistic Regression
    • Lab
    • Use case: credit card application, college admissions
  • Chapter 7: SVM (Supervised Vector Machines)
    • Lab
    • Use case: Customer churn data
  • Chapter 8: Decision Trees & Random Forests
    • Labs
    • Use case: predicting loan defaults, estimating election contributions
  • Chapter 9: Naive Bayes
    • Lab
    • Use case: spam filtering
  • Chapter 10: Clustering (K-Means)
    • Lab
    • Use case: grouping cars data, grouping shopping data
  • Chapter 11: Principal Component Analysis (PCA)
    • Lab
    • Use case: analyzing retail shopping data
  • Chapter 12: Recommendation (Collaborative filtering)
    • Lab
    • Use case: movie recommendations, music recommendations

With this class you will receive:

  • 20 Hours of Learning Content
  • 1 Year of unlimited access to course
  • Ability to stop, pause, rewind and move through all chapters
  • Format: Self-Paced Learning

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