• Formulating real world problems as AI and ML problems
  • Classification and Regression Problems
  • Intuitive and Simple Algorithms: KNN, Decsion Tree and Simple Linear Classifier
  • Representation of the world and real data: Emphasis on Text, Image, Speech and Sequences
  • Visualization, Data Preparation and Unsupervised Learning
  • End to end Problem Solving: Navigating through three specific problems and case studies