The decision to use AI/ML is not just technical. It is a decision that, if planned for and responsibly adopted, can be transformational to a company’s stakeholders, workforce, and long-term business trajectory. The marketing hype is in full force and it is incumbent upon company leaders to learn the key terminology, relevant use cases, and how AI differs from previous analytic tools. While developing implementation strategies, understanding of the skills needed, development approaches, and characteristics of a successful AI implementation will help managers and executives navigate the landscape of available AI solutions, machine learning frameworks, and the large ecosystem of AI tools.

This course will help professionals develop a fundamental understanding of machine learning and derive practical solutions using predictive analytics. It introduces the concepts related to Supervised and Unsupervised machine learning from basic regression and classification to decision trees and clustering. The course will make use of Python for the hands-on implementation of the models.

Learning Objectives

  • Be able to understand and analyze AI/ML opportunities, have balanced conversations with your clients on AI/ML topics.
  • Develop an ability to understand/analyze different industry verticals for AI/ML problems by studying relevant case studies (including UBER in NY, Netflix for content recommendations, TAG prediction and association for content, a cancer diagnosis in healthcare, Amazon fashion discovery engine, Chatbot concepts etc.)
  • Understand AI, ML, DL concepts and foundation for its algorithms, how it is different from AI and Deep Learning.
    Learn broader overview of various Machine Learning algorithms.
  • Learn how to start implementing algorithms using the real-world datasets.
  • Get the current industry status of Deep Learning
    Start thinking about how to automate things by relating to the case studies discussed in the sessions.
  • Develop basic understanding of Python and tools to perform Data analytics and ML in Python.
Technical Background:

We recommend that all students have:

  • A basic understanding matrix vector operations and notation
  • A basic knowledge of statistics
  • Basic command line operations
Hardware / PC capabilities:

A workstation with the following capabilities:

  • A web browser (Chrome/Firefox)
  • Internet connection
  • A firewall allowing outgoing connections on TCP ports 80 and 443
  • The following developer utilities should be installed:
    • Anaconda
    • Jupyter Notebook (will be installed using Anaconda)
    • All libraries will be installed using Anaconda
Software Requirements:
  • All code will be written in Python with the use of the following libraries: Pandas/NumPy are the libraries for matrix calculations and data frame operations.
  • We strongly recommend to browse through the available tutorials for these packages, for instance, the official one scikit-learn, Matplotlib
  • If software requirements cannot be satisfied due to the security policy of your employer, please inform us about the situation to find an appropriate solution for this issue.

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: Machine Learning - 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
  • 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 6: Introduction to Python

  • Basics of Python (Python Crash Course)
  • Pandas for importing data and pre-processing
  • Numpy for Statistical Analysis
  • Matplotlib for Data Visualization
  • Hands-on practice

Unit 7: Unsupervised Machine Learning: Clustering

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

Unit 8: 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)