AI/ML for Executives & Managers


This course will provide an excellent start in understanding Artificial Intelligence/Machine Learning as a domain. The course is well- balanced between theory and hands-on lab, with the help of real-world uses case studies. It will also offer a good foundation for Deep learning.

What you will learn

This explainer course will offer simple definitions and analogies for blockchain technology. It will also define Bitcoin, Bitcoin Cash, Ethereum, Litecoin, blockchain, and initial coin offerings. Along the way, we’ll highlight promising use cases for blockchain technology.

Course Content

Unit 1: Introduction for Data Analytics, Machine Learning and Artificial Intelligence

  • What is Data Science
  • Applications of Data Science
  • Case Study : Investment research analysis
  • What is AI / ML
  • Typical Customer Segmentation Workflow
  • Case study: Building a customer service chatbot

Unit 2: ML : Understanding jargons:

  • Machine Learning Basics
  • Supervised machine learning
  • When to use Supervised Learning
  • Features and labels
  • Model evaluation
  • Clustering
  • Supervised vs. Unsupervised
  • When to use Unsupervised Learning
  • Cluster size selection

Unit 3: Building a data science team and responsibility assignment

  • AI-ML Project cycle Overview
  • Supervised machine learning
  • AI-ML Job Descriptions and skill sets
  • Matching skills to jobs
  • Assigning data science projects
  • Interpreting a team sprint
  • Classifying data tasks

Unit 4: Advanced topics and use cases in Machine Learning

  • Classifying machine learning tasks
  • Sentiment Analysis (Natural Language Processing)
  • Deep Learning and Explainable AI
  • Where should I use Deep Learning?
  • Finding the correct solution to given problem
  • Google Auto-ML usage to implement all these things very easily for non-developers

Unit 5: Develop broader understanding with case studies

  • Recommendation of Wines,
  • Employee productivity via ML, Healthcare chatbots
  • Automatic Music Tagging
  • Smart Cars.

Unit 7: Introduction to Python

  • Basics of Python (Python Crash Course)
  • Pandas for importing data and pre-processing
  • Numpy for Statistical Analysis
  • Where should I use Deep Learning?
  • Matplotlib for Data Visualization
  • Hands-on practice

Unit 9: Unsupervised Machine Learning: Clustering

  • K Means clustering
  • Hierarchical clustering
  • PCA (Principal Component Analysis) and its Applications
  • Hands-on Demo

Unit 10: Deep Learning foundation

  • Fundamentals of Neural Network Analysis (Demo)
  • Introduction to Recommendation System (Demo)
  • Introduction to Sentiment Analysis (Demo)
  • Introduction to Deep learning and TensorFlow (Demo)