Artificial Intelligence
Introduction to Artificial Intelligence
Artificial Intelligence (AI) involves the creation of systems that can perform tasks that typically require human intelligence. This module provides an overview of AI, including its key concepts, applications, and foundational techniques.
Fundamentals of AI
Learn about the fundamental concepts of AI, including intelligent agents, problem-solving, and reasoning. Explore the different types of AI, such as narrow AI and general AI, and understand their applications.
Machine Learning Basics
Discover the basics of machine learning, a subset of AI. Understand supervised and unsupervised learning, classification, regression, clustering, and model evaluation techniques.
Deep Learning and Neural Networks
Explore deep learning, a specialized area of machine learning. Learn about neural networks, including feedforward, convolutional, and recurrent networks, and their applications in tasks such as image and speech recognition.
Natural Language Processing (NLP)
Understand natural language processing (NLP), which focuses on the interaction between computers and human language. Explore text processing, sentiment analysis, language generation, and common NLP applications.
AI Tools and Technologies
Learn about the various tools and technologies used in AI development, including popular frameworks and libraries such as TensorFlow, PyTorch, and scikit-learn. Understand how these tools facilitate AI model creation and deployment.
Ethical Considerations in AI
Explore the ethical implications of AI, including issues of bias, fairness, privacy, and accountability. Learn about the challenges of ensuring ethical AI practices and the impact of AI on society.
Applications of AI
Discover real-world applications of AI across various industries, including healthcare, finance, automotive, and more. Explore case studies and examples to understand how AI is transforming different sectors.
Future Trends in AI
Learn about emerging trends and future developments in AI. Explore advancements in technology, research directions, and potential impacts of AI on various aspects of life and industry.
Artificial Intelligence Syllabus
Introduction to Artificial Intelligence
- Overview of Artificial Intelligence: Definition, History, and Evolution
- Applications of AI in Various Domains: Healthcare, Finance, Gaming, etc.
- Ethical and Societal Implications of AI: Bias, Privacy Concerns, AI Ethics
AI Foundations
- Introduction to Machine Learning (ML) and Deep Learning (DL): Basic Concepts and Differences
- Supervised Learning: Regression, Classification (Linear Regression, Logistic Regression)
- Unsupervised Learning: Clustering, Dimensionality Reduction (K-means, PCA)
Neural Networks and Deep Learning
- Introduction to Neural Networks: Perceptrons, Activation Functions (ReLU, Sigmoid)
- Deep Neural Networks (DNNs): Architecture, Forward and Backward Propagation
- Convolutional Neural Networks (CNNs): Image Recognition, Transfer Learning
- Recurrent Neural Networks (RNNs): Sequential Data, Natural Language Processing (NLP)
Natural Language Processing (NLP)
- Introduction to NLP: Tokenization, Text Preprocessing
- NLP Techniques: Named Entity Recognition (NER), Sentiment Analysis, Text Generation
- Language Models: Word Embeddings (Word2Vec, GloVe), Transformers (BERT, GPT)
Reinforcement Learning
- Introduction to Reinforcement Learning (RL): Agents, Environments, Rewards
- RL Algorithms: Q-Learning, Policy Gradient Methods
- Applications of RL: Game Playing (AlphaGo), Robotics, Autonomous Systems
AI in Practice
- AI Development Lifecycle: Problem Formulation, Data Collection, Model Selection, Evaluation
- Model Deployment and Scaling: Cloud Services (AWS, Azure, Google Cloud)
- AI Tools and Frameworks: TensorFlow, PyTorch, Scikit-Learn, Keras
AI Applications
- Computer Vision: Object Detection, Image Segmentation, Facial Recognition
- AI in Healthcare: Disease Diagnosis, Medical Imaging Analysis
- AI in Finance: Algorithmic Trading, Fraud Detection
AI and Ethics
- AI Bias and Fairness: Mitigation Strategies, Fairness in ML Models
- Privacy and Security in AI: Data Protection, Confidentiality
- Regulation and Governance: AI Policies, Compliance with AI Ethics Guidelines
AI Research and Innovation
- Latest Trends in AI Research: Generative Models, AutoML, Explainable AI
- Future of AI: Ethical AI, Human-AI Collaboration, AI for Social Good
Hands-on Projects and Case Studies
- Implementing AI Algorithms: Hands-on Projects Covering Supervised, Unsupervised, and Reinforcement Learning
- Real-world AI Applications: Case Studies from Industry and Academia
Career Development in AI
- Skills and Competencies for AI Professionals: Programming, Mathematics, Problem-solving
- AI Certifications and Career Paths: Data Scientist, Machine Learning Engineer, AI Researcher
- Job Market Trends and Opportunities: Salary Insights, Industry
AI and Society
- Impact of AI on Jobs: Automation, Reskilling, Future of Work
- AI in Education: AI-driven Learning Systems, Personalized Learning
- AI for Sustainability: Climate Change, Resource Management
AI Governance and Policy
- Global AI Policies and Regulations: GDPR, AI Ethics Guidelines (IEEE, ACM)
- AI Governance Frameworks: Responsible AI Practices, Risk Management
Emerging Technologies and AI
- AI and Internet of Things (IoT): Smart Cities, Connected Devices
- AI and Blockchain: Smart Contracts, Decentralized AI
Final Project and Capstone
- Capstone Project: Designing and Implementing an AI Solution from Scratch
- Presentation and Evaluation: Demonstrating Proficiency in AI Concepts and Applications
Training
Basic Level Training
Duration : 1 Month
Advanced Level Training
Duration : 1 Month
Project Level Training
Duration : 1 Month
Total Training Period
Duration : 3 Months
Course Mode :
Available Online / Offline
Course Fees :
Please contact the office for details