Data Science for Non-programmers
- Created By raju2006
- Last Updated November 29th, 2023
- Overview
- Prerequisites
- Audience
- Audience
- Curriculum
Description:
This course provides a fun and non-technical introduction to Data Science. 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 an understanding 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.
Duration: Half Day
Course Code: BDT271
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 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 use Machine Learning & AI
- Basic Programming knowledge preferred.
- Anyone interested in Data Science concept.
- Anyone interested in Data Science concept.
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 for AI
- 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
- Trends around AI and ML
- References and Next steps
