State level Faculty Development -Machine Learning with Python
Participants – 20
Coordinator – Mr.YogeshRaje (+91 9923723839)
Resourse Persons – Mr.Prabhakar G. – Persistence System, Pune | Prof .Yogesh Raje –VIIT,Baramati | Dr. Acharya H.S.- Allana Institue of Management ,Pune | Dr.Ashfak Shaikh – Allana Institute of Management ,Pune
Artificial Intelligence (AI) is a field that has a long history but is still constantly and actively growing and changing. In this workshop, you’ll learn the basics of modern AI as well as some of the representative applications of AI and Machine Learning. Along the way, we also hope to excite you about the numerous applications and huge possibilities in the field of AI and Machine Learning, which continues to expand human capability beyond our imagination.
Machine Learning is a growing field that is used when searching the web, placing ads, credit scoring, stock trading and for many other applications.
Machine learning is a type of artificial intelligence (AI) that allows software applications to become more accurate in predicting outcomes without being explicitly programmed. The basic premise of machine learning is to build algorithms that can receive input data and use statistical analysis to predict an output value within an acceptable range.
This workshop will cover the basic algorithm that helps us to build and apply prediction functions with an emphasis on practical applications. Attendees, at the end of this workshop, will be technically competent in the basics and the fundamental concepts of Machine Learning such as:
- Understand components of a machine learning algorithm.
- Apply machine learning tools to build and evaluate predictors.
- How machine learning uses computer algorithms to search for patterns in data.
- How to use data patterns to make decisions and predictions with real-world examples.
|Day 1||Introduction to AI
Introduction to Machine Learning
Application of Machine Learning
Artificial Intelligence & Machine Learning
Database Mining and Machine Learning
Python- Installation,Data types ,OOP
Hands on Session
|Day 2||Scikit-learn –Installing scikit-learn
Essential Libraries and Tools – Numpy ,SciPy,matplotlib,pandas,
Supervised Learning Introduction and Examples
Unsupervised Learning and Examples
Linear Regression and implementation
|Day 3||Introduction to Gradient Decent Algorithm
Designing Neural Network Model
Model Representation Methods
Concept of Intelligent Agents
Problem Solving Agents in AI Applications
|Day 4||Single Layer Neural Network
Multilayer Neural Network Architecture
Training the Network
Backward Propagation Training
Using the Network
Importing & Exporting Network
Importing & Exporting Training Data
Introduction to Dynamic Neural Network
|Day 5||Case Study: Bank Customer Churn
Case Study: Character Recognition
Case Study: Iris Clustering
Case Study :Object Detection