Online/ e-LMS
Mentor Based
Beginners
8 Weeks
About
This program provides an overview of Artificial Intelligence, exploring its key principles, applications, and methodologies. Participants will gain insights into core AI concepts such as machine learning, neural networks, and natural language processing. Designed for beginners, the program emphasizes practical understanding through hands-on exercises and real-world examples.
Aim
To introduce participants to the fundamental concepts and techniques of Artificial Intelligence (AI), laying a strong foundation for understanding and applying AI in various fields.
Program Objectives
- To provide a comprehensive introduction to Artificial Intelligence.
- To familiarize participants with the fundamental techniques and tools of AI.
- To explore real-world applications of AI across industries.
- To discuss ethical considerations and challenges in AI.
- To inspire participants to pursue further learning and careers in AI.
Program Structure
Module 1: Introduction to Artificial Intelligence
- Overview of AI
- Definition, History, and Evolution of AI
- Key Concepts: Intelligence, Machine Learning, and Automation
- AI vs. Human Intelligence
- Applications of AI in Various Domains
- AI Foundations
- Philosophical and Ethical Implications
- Key Milestones in AI Development
- Types of AI: Narrow AI, General AI, and Superintelligent AI
- AI Technologies and Tools
- AI Ecosystem and Frameworks
- Popular Programming Languages for AI (Python, R, etc.)
- Overview of AI Libraries (TensorFlow, PyTorch, etc.)
Module 2: Machine Learning
- Introduction to Machine Learning
- Definitions and Types (Supervised, Unsupervised, Reinforcement Learning)
- Key Algorithms and Techniques
- Supervised Learning
- Regression and Classification Techniques
- Evaluation Metrics (Accuracy, Precision, Recall, F1 Score)
- Unsupervised Learning
- Clustering Algorithms (K-means, DBSCAN)
- Dimensionality Reduction (PCA, t-SNE)
- Reinforcement Learning
- Concepts of Reward Systems
- Deep Q-Learning and Policy Optimization
Module 3: Deep Learning
- Foundations of Deep Learning
- Neural Networks: Structure and Functioning
- Activation Functions, Loss Functions
- Deep Learning Architectures
- Convolutional Neural Networks (CNNs)
- Recurrent Neural Networks (RNNs)
- Generative Adversarial Networks (GANs)
- Training and Optimization
- Backpropagation and Gradient Descent
- Hyperparameter Tuning and Model Regularization
Module 4: Natural Language Processing (NLP)
- Introduction to NLP
- Text Preprocessing and Representation
- N-grams, Bag of Words, TF-IDF
- Core NLP Techniques
- Sentiment Analysis, Named Entity Recognition
- Machine Translation and Text Summarization
- Transformers and Large Language Models
- Overview of Transformers (BERT, GPT)
- Applications of Large Language Models in Industry
Module 5: Computer Vision
- Introduction to Computer Vision
- Fundamentals of Image Processing
- Object Detection and Recognition
- Advanced Techniques in Vision
- Image Segmentation (U-Net, Mask R-CNN)
- Applications in Healthcare, Automotive, and Security
Module 6: AI in Practice
- AI Applications in Industry
- Use Cases in Healthcare, Finance, Retail, and Robotics
- Smart Cities, Autonomous Vehicles
- AI in Research and Development
- Emerging Trends in AI Research
- AI in Scientific Discovery
- AI Ethics and Governance
- Responsible AI and Ethical Implications
- Regulatory Frameworks and AI Policies
Module 7: Advanced Topics in AI
- Explainable AI (XAI)
- Importance and Challenges of Interpretability
- Techniques for Building Transparent AI Models
- Federated Learning
- Decentralized Machine Learning Models
- Applications in Privacy-Sensitive Domains
- AI for Social Good
- AI in Environmental Sustainability
- Applications in Education and Public Health
Module 8: Future Directions in AI
- AI Trends and Technologies
- Advances in Quantum AI
- Neuromorphic Computing
- AI Research Challenges
- Open Problems in AI Development
- Cross-Disciplinary Research Opportunities
- AI for the Next Decade
- Speculations on Superintelligence
- The Role of AI in Shaping Future Societies
Participant’s Eligibility
- Students and professionals from any field curious about AI
- Beginners with no prior experience in AI or programming
- Entrepreneurs and business leaders exploring AI adoption
- Anyone interested in understanding how AI works and its applications
Program Outcomes
- Understanding of core AI concepts and methodologies
- Ability to identify AI applications in real-world scenarios
- Hands-on experience with basic AI tools and techniques
- Awareness of ethical and societal considerations in AI
- Preparation for advanced AI and machine learning courses
Fee Structure
Fee: INR 21,499 USD 291
We are excited to announce that we now accept payments in over 20 global currencies, in addition to USD. Check out our list to see if your preferred currency is supported. Enjoy the convenience and flexibility of paying in your local currency!
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Key Takeaways
Program Deliverables
- Access to e-LMS
- Real Time Project for Dissertation
- Project Guidance
- Paper Publication Opportunity
- Self Assessment
- Final Examination
- e-Certification
- e-Marksheet
Future Career Prospects
- AI Research Assistant
- Machine Learning Enthusiast
- Entry-Level Data Analyst
- AI Content Developer for Education
- Technology Consultant
Job Opportunities
- AI Support Specialist
- Junior Data Scientist
- AI Tester and Quality Analyst
- AI Business Strategist
- Research Intern in AI Projects
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