Machine Learning Is For Everyone (Lecture)
- Created By raju2006
- Last Updated December 26th, 2023
- Overview
- Prerequisites
- Audience
- Audience
- Curriculum
Description:
In this Course understand Easily identifies trends and patterns,No human intervention needed (automation) ,Continuous Improvement,,Handling multi-dimensional and multi-variety data,Wide Applications.
Long Description:
Unlock the power of Machine Learning in our comprehensive course. Seamlessly identify trends and patterns, all without the need for human intervention through automation. Embrace continuous improvement as you gain proficiency in handling multi-dimensional and multi-variety data, opening doors to a wide array of applications. Our Machine Learning Lecture is your gateway to understanding the real-world applications of AI and Machine Learning, including patterns and use cases. Explore concepts such as Supervised and Unsupervised learning, demystify AI vs ML vs DL, and broaden your AI vocabulary with techniques like Classification, Clustering, and Regression. Join us for a transformative journey that culminates in a hands-on ML demonstration, equipping you with the tools and knowledge for what comes next
Course Code/Duration:
BDT37 / 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 usage of tools through a ML Demo
- Familiarity with Java(or a similar object oriented language), XML is required.
- Developers,Business Analysts who want to start a career in or wants to learn about the exciting domain of Data Science and Machine Learning, Non-technical professionals who waant to start a career in Machine Learning.
- Developers,Business Analysts who want to start a career in or wants to learn about the exciting domain of Data Science and Machine Learning, Non-technical professionals who waant to start a career in Machine Learning.
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 Learning offerings in Industry
- Demo – ML using Azure ML studio
- Demo – ML using Scikit-learn
- References and Next steps
Structured Activity/Exercises/Case Studies:
- Demo – ML using Azure ML studio
- Demo – ML using Scikit-learn
Training material provided:
Yes (Digital format)
