Duration: 48 hours

Target audience: ML/AI/Data-science Python developers

Description: This comprehensive course combines theoretical foundations with extensive practical coding and problem-solving focused on machine learning, intelligent agents, and the latest advances in large language models and automatic code/multi-modal content generation, preparing participants for cutting-edge AI development and research.

Key takeaways/benefits:

This course empowers you with comprehensive, hands-on expertise in machine learning, intelligent agents, and large language models, equipping you to innovate and excel in the rapidly evolving AI landscape:

• Acquire practical skills to build and deploy machine learning models, from foundational algorithms to advanced deep learning and reinforcement learning techniques, enabling you to solve real-world problems effectively.
• Gain hands-on experience with large language models (LLMs) such as GPT and BERT, including fine-tuning, prompt engineering, and integrating LLMs into intelligent agents for natural language understanding and generation.
• Learn to develop intelligent agents powered by LLMs that can perform autonomous decision-making, multi-turn conversations, and interact with external APIs and knowledge bases, preparing you for cutting-edge AI applications.
• Master essential machine learning tools and frameworks (TensorFlow, PyTorch, Keras, Hugging Face, LangChain, LlamaIndex, CrewAI, etc.) through project-based learning, building a portfolio of practical implementations.
• Understand reinforcement learning concepts and implement RL agents in simulated environments, gaining insights into policy optimization and reward mechanisms.
• Improve your ability to evaluate, tune, and deploy machine learning and LLM models with best practices for avoiding bias, managing hallucinations, and ensuring ethical AI use.
• Develop a strong foundation in both theoretical concepts and applied techniques, enabling you to confidently contribute to AI research, development, or deployment in industry.
• Expand your career opportunities by mastering in-demand skills in machine learning, natural language processing, and AI agent design, supported by mentorship from academy/industry experts.
• Enhance your understanding of AI ethics, safety, and responsible AI development to build trustworthy and human-centered intelligent systems.

Training program:

Module 1: Foundations of Machine Learning (8 hours)

• Overview of machine learning paradigms: supervised, unsupervised, and reinforcement learning

• Mathematical foundations: linear algebra, calculus, probability, and statistics essentials for ML

• Introduction to Python for ML: libraries (NumPy, Pandas, scikit-learn), data handling, and visualization

• Hands-on: Implement linear and logistic regression models from scratch and using scikit-learn

• Problem solving: Model evaluation, overfitting, underfitting, regularization techniques (L1, L2)

Module 2: Core Machine Learning Algorithms and Techniques (10 hours)

• Decision trees, random forests, and ensemble methods (bagging, boosting)

• Support Vector Machines (SVM) and kernel methods

• Clustering algorithms: k-means, hierarchical clustering, DBSCAN

• Dimensionality reduction: PCA, t-SNE

• Hands-on: Build classification and clustering pipelines with real datasets

• Problem solving: Hyperparameter tuning, cross-validation, and model selection

Module 3: Neural Networks and Deep Learning (10 hours)

• Fundamentals of neural networks: architecture, activation functions, loss functions

• Forward and backward propagation, gradient descent optimization

• Deep learning architectures: CNNs, RNNs, LSTMs

• Introduction to transformers and attention mechanisms

• Hands-on: Implement feedforward and convolutional neural networks using TensorFlow or PyTorch

• Problem solving: Training deep models, avoiding overfitting, and improving generalization

Module 4: Reinforcement Learning and Intelligent Agents (8 hours)

• Reinforcement learning basics: Markov Decision Processes, policies, rewards

• Dynamic programming, Monte Carlo methods, Temporal Difference learning

• Q-learning and Deep Q-Networks (DQN)

• Introduction to intelligent agents and their architectures

• Hands-on: Implement simple RL agents in OpenAI Gym environments

• Problem solving: Exploration vs. exploitation, reward shaping, and policy optimization

Module 5: Large Language Models (LLMs) and Natural Language Processing (10 hours)

• Overview of language models: from n-grams to transformers

• Architecture and training of LLMs (e.g., GPT, BERT)

• Tokenization, embeddings, and attention in NLP

• Fine-tuning and prompt engineering for LLMs

• Hands-on: Use Hugging Face Transformers library to build and fine-tune LLMs for text classification, summarization, and generation

• Problem solving: Handling bias, hallucinations, and evaluation of LLM outputs

Module 6: Agents Powered by LLMs and Practical Applications (8 hours)

• Architectures for LLM-powered agents: chatbots, autonomous decision-making agents

• Integration of LLMs with external APIs and knowledge bases

• Multi-modal agents combining vision, language, and action

• Ethical considerations and safety in agent design

• Hands-on: Build a conversational agent using LLM APIs and custom logic

• Problem solving: Context management, multi-turn dialogue, and agent evaluation

Module 7: Project Work and Real-World Problem Solving (6 hours)

• Project 1: End-to-end ML pipeline including data preprocessing, model training, and evaluation

• Project 2: Design and implement a reinforcement learning agent in a simulated environment

• Project 3: Fine-tune and deploy a large language model for a domain-specific task

• Project 4: Develop an LLM-powered agent with multi-turn interaction capabilities

• Code reviews, performance tuning, and presentation of results

Module 8: Review, Assessment, and Future Directions (4 hours)

• Recap of key concepts, algorithms, and tools

• Final coding assessment and problem-solving exercises

• Discussion on emerging trends: foundation models, multi-agent systems, and AI ethics

• Guidance on continued learning paths and research opportunities