AI ML complete Course
Discover the true power of Artificial Intelligence (AI) and Machine Learning (ML) through this comprehensive course, which covers everything from the basics to advanced methods for creating, deploying, and improving smart systems used in real-world scenarios.
- Learn the fundamentals of AI and ML, including supervised, unsupervised, and reinforcement learning.
- Master key algorithms such as linear regression, logistic regression, decision trees, random forests, and support vector machines.
- Explore deep learning concepts like neural networks, CNNs, and RNNs.
- Understand advanced AI applications, including natural language processing (NLP), computer vision, and reinforcement learning techniques.
AI ML complete Course – Syllabus
Generative AI
- Introduction to Generative AI
- Overview of Generative AI
- Key differences: Generative AI vs. Traditional AI
- Major use cases and applications
Core NLP Techniques
- NLP Basics
- Encoding Techniques
- NLP Models
Advanced NLP Concepts
- Advanced Architectures
- Generative Models
Large Language Models (LLMs)
- LLM Foundations
- Instruction and Fine-Tuning
Deployment and LLM Operations
- Deployment Considerations
- LLMOps for Scalability
Ethical and Responsible AI Use
- Ethical Considerations
- Real-World Case Studies
Prompt Engineering
- Foundations of Generative AI
- Introduction to Prompt Engineering
- Advanced Prompt Techniques
- Crafting and Optimizing Prompts
- Evaluating and Testing Prompts
Machine Learning
- Introduction to Machine Learning Data Preprocessing
- Supervised Learning Algorithms
- Unsupervised Learning Algorithms
- Advanced Machine Learning Algorithms
- Model Evaluation and Optimization
Deep Learning
- Introduction to Deep Learning
- Fundamentals of Neural Networks
- Training Deep Neural Networks
- Deep Learning Architectures
- Advanced Deep Learning Models
Natural Language Processing
- Introduction to NLP
- Text Preprocessing
- Training Deep Neural Networks
- Text Encoding and Feature Extraction
- Traditional NLP Algorithms
- Advanced NLP Models
- NLP Tasks and Applications
- Named Entity Recognition (NER)
- Machine Translation
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