Course Outline

Introduction to Neural Networks

Introduction to Applied Machine Learning

  • Statistical learning vs. Machine learning
  • Iteration and evaluation
  • Bias-Variance trade-off

Machine Learning with Python

  • Choice of libraries
  • Add-on tools

Machine learning Concepts and Applications


  • Linear regression
  • Generalizations and Nonlinearity
  • Use cases


  • Bayesian refresher
  • Naive Bayes
  • Logistic regression
  • K-Nearest neighbors
  • Use Cases

Cross-validation and Resampling

  • Cross-validation approaches
  • Bootstrap
  • Use Cases

Unsupervised Learning

  • K-means clustering
  • Examples
  • Challenges of unsupervised learning and beyond K-means

Short Introduction to NLP methods

  • word and sentence tokenization
  • text classification
  • sentiment analysis
  • spelling correction
  • information extraction
  • parsing
  • meaning extraction
  • question answering

Artificial Intelligence & Deep Learning

Technical Overview

  • R v/s Python
  • Caffe v/s Tensor Flow
  • Various Machine Learning Libraries

Industry Case Studies


  1. Should have basic knowledge of business operation, and technical knowledge as well
  2. Must have basic understanding of software and systems
  3. Basic understanding of Statistics (in Excel levels)
  21 Hours


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