Artificial Intelligence And Machine Learning For Non-Programmer
- Created By raju2006
- Last Updated December 20th, 2023
- Overview
- Prerequisite
- Audience
- Audience
- Curriculum
Short Description:
Embark on an engaging journey into the world of AI and ML basics designed specifically for executives. This course offers a non-technical and enjoyable introduction to Artificial Intelligence and Machine Learning, equipping you with the vocabulary and fundamentals needed to navigate this exciting domain. Gain awareness of AI and Machine Learning patterns and their real-world applications. Understand key Machine Learning concepts, including Supervised and Unsupervised learning techniques, and demystify the differences between AI, ML, and DL. Expand your AI vocabulary to grasp techniques such as Classification, Clustering, and Regression. Plus, witness a live ML demo to illustrate tools and next steps. Elevate your executive knowledge with "AI & ML Basics for Executives" training.
Long Description:
This course provides a fun and non-technical introduction to the Artificial Intelligence and Machine Learning. It provides the vocabulary and basics for this exciting new world of Artificial Intelligence and Machine Learning.
Artificial Intelligence and Machine Learning Lecture helps in awareness about AI &Machine Learning patterns and use cases in real world. Along the way, you’ll get anunderstanding of Machine Learning concepts like Supervised and Unsupervised learning techniques and usages. Demystify the difference between AI vs ML vs DL along with usage patterns. You would expand your vocabulary in the AI to understand techniques like Classification, Clustering and Regression. Finally, we would do a ML demo to illustrate few tools and next steps.
Course Code/Duration:
BDT1 / 1 Day
Learning Objectives:
After this course, you will be able to:
- Describe Supervised and Unsupervised learning techniques and usages
- Compare AI vs ML vs DL
- Understand techniques like Classification, Clustering and Regression
- Discuss how to identify which kinds of technique to be applied for specific use case
- Understand the popular Machine offerings like Amazon Machine Learning, TensorFlow, Azure Machine Learning, Spark mlib, Python and R etc.
- Understand the relation between Data Engineering and Data Science
- Understand the Data Science process
- Discuss Machine Learning use cases in different domains
- Identify when to use or not useMachine Learning
- Define how to form a ML team for success
- Understand usage of tools through a ML Demo and hands-on labs.
- Basic Programming
- Anyone interested
- Anyone interested
Course Outline:
- Course Introduction
- History and Background of AI and ML
- Compare AI vs ML vs DL
- Describe Supervised and Unsupervised learning techniques and usages
- Machine Learning patterns
- Classification
- Clustering
- Regression
- Gartner Hype Cycle for Emerging Technologies Machine Learningofferings in Industry
- Discuss Machine Learning use cases in different domains
- Understand the Data Science process to apply to ML use cases
- Identify the different roles needed for successful ML project
- Hands-on: Create account for Microsoft Azure Machine Learning Studio
- Demo:ML using Azure ML studio
- Demo – ML using Scikit-learn
- References and Next steps
Structured Activity/Exercises/Case Studies:
- Hands-on 1: Create account for Microsoft Azure Machine Learning Studio
- Hands-on 2:ML using Azure ML studio
- Hands-on 3:Demo of ML using Scikit-learn
Training material provided:
Yes (Digital format)
