Artificial Intelligence and Machine Learning Essentials
Master the Essentials of AI and Machine Learning: From Concepts to Real-World Application
About This Course
This three-day course covers essential AI and ML concepts, deep learning fundamentals, and hands-on model development using Python. Participants will explore both theoretical and practical aspects of AI and ML, with a focus on real-world applications and model building.
Aim
To equip PhD scholars and academicians with foundational and practical skills in artificial intelligence (AI) and machine learning (ML). This course aims to provide a comprehensive understanding of AI and ML concepts, introduce deep learning frameworks, and offer hands-on experience in developing ML models.
Workshop Objectives
- Gain a foundational understanding of AI and ML concepts.
- Learn the differences between supervised and unsupervised learning.
- Understand the basics of deep learning and neural networks.
- Get hands-on experience with TensorFlow and PyTorch.
- Develop and deploy a machine learning model using Python.
Workshop Structure
Day 1: Introduction to AI and ML Concepts
- Overview of AI and ML
- History and evolution of AI and ML
- Current trends and future directions
- Supervised vs Unsupervised Learning
- Key differences and use cases
- Common algorithms and applications
- Introduction to Algorithms
- Overview of essential AI/ML algorithms (e.g., Decision Trees, K-Means)
- Understanding algorithm selection and performance evaluation
Day 2: Deep Learning and Neural Networks
- Basics of Neural Networks
- Structure of neural networks: Layers, neurons, and activation functions
- Introduction to backpropagation and gradient descent
- Introduction to TensorFlow and PyTorch
- Overview of popular deep learning frameworks
- Practical exercises on building simple neural networks
- Practical Examples and Exercises
- Hands-on exercises with real-world datasets
- Building and training neural networks from scratch
Day 3: Hands-on Session
- Creating a Machine Learning Model in Python
- Step-by-step guide to building a machine learning model
- Data preprocessing, model selection, training, and evaluation
- Capstone Project: Model Development
- Developing a complete ML model using Python
- Presenting findings and discussing challenges
Who Should Enrol?
Data scientists, AI/ML enthusiasts, computer science students, researchers, and academicians.
Important Dates
Registration Ends
09/26/2024
IST
Workshop Dates
09/27/2024 – 09/29/2024
IST 5 PM
Workshop Outcomes
- Understand core AI and ML concepts and algorithms.
- Build and train neural networks using deep learning frameworks.
- Develop a complete machine learning model in Python.
- Apply AI and ML techniques to solve real-world problems.
- Gain practical experience through hands-on exercises and projects.
Fee Structure
Student Standard fee
₹1499 | $40
Ph.D. Scholar / Researcher Standard fee
₹1999 | $45
Academician / Faculty Standard fee
₹2999 | $50
Industry Professional Standard fee
₹4999 | $75
What You’ll Gain
- Live & recorded sessions
- e-Certificate upon completion
- Post-workshop query support
- Hands-on learning experience
Join Our Hall of Fame!
Take your research to the next level with NanoSchool.
Publication Opportunity
Get published in a prestigious open-access journal.
Centre of Excellence
Become part of an elite research community.
Networking & Learning
Connect with global researchers and mentors.
Global Recognition
Worth ₹20,000 / $1,000 in academic value.
View All Feedbacks →