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Do not miss out on the most updated AI-ML Training!

ABOUT THE TRAINING

Who should attend?

  • Newbies who are not familiar with AI or its implications
  • Developers aspiring to be a data scientist or machine learning engineer
  • Analytics managers
  • Analytics professionals
  • Business analysts
  • Information architects
  • What will you learn?

  • You will have a basic understanding of Artificial Intelligence and Machine Learning and the impact of these emerging technologies in the industry.
  • You will be able to define key terminology used in AI space
  • Acquire knowledge of the statistical and heuristic aspects of machine learning
  • Learn about major applications of AI across various fields like customer service, financial services, healthcare etc.
  • Gain practical mastery over principles, algorithms, and applications of machine learning
  • Comprehend theoretical concepts and how they relate to the practical aspects of machine learning

  • Course Structure

    Course Objective:

    At the end of the session, the audience would have high level view about

  • Key Analytics Fundamentals - AI Vs Machine Learning Vs Deep Learning
  • Why AI now? AI Venn Diagram and Flowchart
  • Important Stages/Steps in E2E Analytics
  • Case Studies – Telecom/IoT Analytics/Social Media Analytics Framework
  • ML Approach Selection Criteria
  • Machine Learning – Data Processing Challenges
  • Machine Learning Algorithms, Model – Mathematical Space Representation
  • Machine Learning Categories – Supervised/Un-Supervised
  • Python Packages for Machine Learning with hands on lab
  • Entry Profile & Pre-requisite Skill-set

  • Developers and Architects, who wish to write, build and AIML Framework.
  • Java, Python, Linux/Unix knowledge desirable
  • Session 1

    What is Artificial Intelligence?

  • Key Analytics Fundamentals - AI Vs Machine Learning Vs Deep Learning
  • Why AI Now?
  • AI Venn Diagram and History
  • AI Systems Flowcharts
  • Analytics Stages
  • Key Steps in Analytics – Golden Rules to Follow
  • ML Selection Criteria – Is ML Approach Really Required…?
  • Case Study 1- Telecom Analytics Framework
  • Case Study 2 – Sales/Social Media Analytics
  • Session 2

    Machine Learning and Data Science

  • What is Machine Learning and Algorithms..?
  • Machine Learning Algorithms, Model – Mathematical Space Representation
  • ML Data Storage & Processing Challenges
    • Handling Limited Resources
    • Managing Missing Data
    • Detecting Outliers and Anamolies
  • Building ML in Mathematical Space – Key Steps
    • ML Data/Feature Set...Step 1
    • ML Model Building...Step 2
    • ML Model Predictions...Step 3
  • Session 3

    Recap of Sessions 1 and 2

    Practical Session - End to End Example in Python

    • Python Packages
    • Linear Regression
    • Classification
    • SVM

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