Foundation Course
on 
Machine Learning

using Python

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Program Objective

This program is designed for familiarizing and providing hands-on experience to participants on various statistical and machine learning techniques for superevised and unsupervised learning using python

Program Benefits

The participants will acquire the knowledge required for

  • Data summarization, visualization and aggregation
  • Statistical techniques for decision making
  • Supervised (predictive modeling) learning techniques
  • Unsupervised learning techniques
  • Methods for model significance, accuracy, adequacy and generalizability checks
  • Hands-on experience on the usage of open-source package Python

Eligibility

  • Any science or engineering graduate working in the analytics field or aspiring to get into analytics can participate in the program.
  • The knowledge of a programming language is desirable but not necessary
  • The examination will be conducted at the end of the course and successful candidates will be issued the certificate

Course Content

  1. Introduction to Data Science
  2. Introduction to Python
  3. Descriptive Analytics
  4. Data Preprocessing and Visualization
  5. Test of Hypothesis
  6. Normality Test
  7. Analysis of Variance
  8. Cross Tabulation and Chi-Square Test
  9. Linear Regression
  10. Dummy Variable Regression
  11. Logistic Regression
  12. Classification and regression tree (CART)
  13. Random Forest
  14. Bagging
  15. Naïve Bayes Classifier
  16. K-Nearest Neighbor
  17. Support Vector Machine (SVM)