Machine Learning for Beginners

Starting July 9

Tuesdays & Thursdays

6:00 - 8:00 pm US Pacific Time



Are you brand new to Machine Learning? Want to see how fun and easy it can be? This Machine Learning Training class for beginners course offers a step-by-step guide to understanding and working with Machine Learning and Machine Learning algorithms.

Don't worry if you do not know Programming. You can still learn Machine Learning and see how fun it can be to learn and apply Machine Learning in your job or any other applicable scenarios. Machine Learning uses simple to complex algorithms and has an easy learning curve, and is very forgiving.

Gain a new skill or complete a task by the end of each module, and, by the end of the course, you will be applying Machine Learning to applicable scenarios! You also learn basic principles which can make it easier for you to learn other advanced Machine Learning techniques in the future.


Course Schedule

  • Course Duration: 4 weeks (8 sessions)
  • Tuesdays and thursdays every week
  • 6:00pm - 8:00pm  US Pacific Daylight Time each day
  • July 9 - August 1, 2019 US Pacific Daylight time
  • Check local date and time for 1st session

What are the prerequisites?

  • No prerequisite is required.
  • Even if you do not have programming background you will be able to take this course and learn Machine Learning.

Course Outline

  1. Introduction to Machine Learning
  2. Fundamentals of Machine Learning
  3. Common Use Cases in Machine Learning
  4. Understanding Supervised and Unsupervised Learning Techniques
  5. Clustering
  6. Similarity Metrics
  7. Distance Measure Types: Euclidean, Cosine Measures
  8. Creating predictive models
  9. Understanding K-Means Clustering
  10. Understanding TF-IDF, Cosine Similarity and their application to Vector Space Model
  11. Implementing Association rule mining
  12. Understanding Process flow of Supervised Learning Techniques
  13. Decision Tree Classifier
  14. How to build Decision trees
  15. Random Forest Classifier
  16. What is Random Forests
  17. Features of Random Forest
  18. Out of Box Error Estimate and Variable Importance
  19. Naive Bayes Classifier
  20. Problem Statement and Analysis
  21. Various approaches to solving a Data Science Problem
  22. Pros and Cons of different approaches and algorithms
  23. Linear Regression
  24. Logistic Regression
  25. Text Mining
  26. Sentimental Analysis