Artificial Intelligence and Machine Learning Essentials
Master the Essentials of AI and Machine Learning: From Concepts to Real-World Application
Virtual (Google Meet)
Mentor Based
Advanced
3 Days
27 -September -2024
5 PM IST
About
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
Intended For
Data scientists, AI/ML enthusiasts, computer science students, researchers, and academicians.
Important Dates
Registration Ends
2024-09-26
Indian Standard Timing
Workshop Dates
2024-09-27 to 2024-09-29
Indian Standard Timing 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.
Mentor Profile
Designation: Assistant Professor
Affiliation: Presidency University, Bengaluru, SJCIT , Chikkabalapur, IIITB, Bengaluru
Dr. Galiveeti Poornima is a distinguished academician and researcher specializing in Machine Learning (ML) and Deep Learning. With a Ph.D. in Computer Science from Presidency University, Bengaluru, she has devoted her research to pioneering advancements in Signed Language Recognition, particularly for Indian Sign Languages. Her expertise extends to the application of ML and AI in healthcare, IoT, and cybersecurity, where she has contributed significantly to the development of intelligent systems for medical diagnostics and social media analytics.
Fee Structure
List of Currencies
FOR QUERIES, FEEDBACK OR ASSISTANCE
Key Takeaways
- Access to Live Lectures
- Access to Recorded Sessions
- e-Certificate
- Query Solving Post Workshop
Future Career Prospects
- Machine Learning Engineer
- Data Scientist
- AI Research Scientist
- Data Analyst
- AI Consultant
- Academic Researcher in AI/ML
Job Opportunities
- Tech companies specializing in AI/ML
- Research institutions
- Financial services (e.g., risk analysis, fraud detection)
- Healthcare (e.g., predictive analytics, diagnostic tools)
- Government agencies (e.g., data analysis, automation)
- Academic institutions
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Recent Feedbacks In Other Workshops
good
1)He should know how to operate basic teams operation because it is where he is teaching. On More
Concepts were clear and fairly easy to follow.