Supervised Machine Learning vs Unsupervised Machine Learning

The World has been talking about the next set of excitements on Machine Learning & Artificial Intelligence . 

This  course is focused on specific topics of Supervised and Unsupervised Machine Learning & the practical Problems for the participants who already have a fair amount of knowledge of Machine Learning and have been working in the field of Machine Learning & Analytics.

HIGHLIGHTS

• Definition of Supervised and Unsupervised Machine Learning 
• Supervised and unsupervised learning with a real-life example
• Types of Supervised and Unsupervised Machine LearningAlgorithms
• Classification and regression supervised learning
• Clustering and association unsupervised learning
• Algorithms used for supervised and unsupervised - Machine Learning Algorithms Mindmap
• Practical problems to Supervised and Unsupervised Machine Learning 
• Learning from the know label data to create a model then predicting target class for the given input data Vs Learning from the unlabeled data to differentiating the given input data
• Case study (BFSI & Pharma ) 

Who is the target audience?

• Corporate Executives looking to learn this technology & implement in his/her projects
• Entrepreneurs 
• Consultants and Professional Service Providers
• CEO's, Boards, and Senior VP's
• Technology Enthusiasts
• Anyone who’s looking for a shift in his/her career

This should be both online-based; For Corporate or more than 12 &  less than 15 in a group can be classroom based. 

Course Outline

The way World is shaping up

• Current Exciting Scenarios
• Statistics brush up
• Intro to Data types & Data Classification

Supervised Learning

• classification & Regression
• • Decision trees • Support vector machine (SVM) • k-Nearest Neighbors • Naive Bayes • Random forest • Linear regression • polynomial regression • SVM for regression

Unsupervised learning

• Clustering & Markov Models
• Hierarchical clustering

Best practices

• Industry-centric approaches
• Case Study

1.   Current scenarios - The way world is shaping up  
2.  Getting started with concepts of Supervised and Unsupervised Machine Learning
3. Supervised and Unsupervised Machine learning tools, design & advice for best suited to Industries
4.  Regression & Classification – Supervised learning & Unsupervised learning  - Use cases 
5.  Best practices