What You’ll Learn

This course equips learners with the essential tools to become AI Engineers. It covers foundational AI concepts, Python programming for NLP and AI, and practical applications for real-world business cases. Students will explore Large Language Models, LangChain for AI-driven applications, Hugging Face tools, and API integrations. The course also delves into advanced speech-to-text using Transformers, helping learners build AI expertise and impress interviewers.

Module 1: Introduction to AI Engineering

  • What is AI? Understanding AI, ML, and Deep Learning
  • The role of an AI Engineer
  • AI applications in different industries
  • Ethical considerations and AI regulations

Module 2: Programming Foundations for AI

  • Python for AI (NumPy, Pandas, Matplotlib)
  • Object-Oriented Programming (OOP) concepts
  • Data structures and algorithms for AI

Module 3: Mathematics for AI

  • Linear Algebra (Vectors, Matrices, Tensors)
  • Probability and Statistics (Bayes’ Theorem, Normal Distribution)
  • Calculus for Optimization (Derivatives, Gradients)

Module 4: Machine Learning Basics

  • Supervised vs. Unsupervised Learning
  • Key ML Algorithms (Linear Regression, Decision Trees, k-NN)
  • Model evaluation and performance metrics

Module 5: Deep Learning Fundamentals

  • Introduction to Neural Networks
  • Training Neural Networks (Backpropagation, Optimization)
  • Activation Functions and Loss Functions

Module 6: Advanced AI & Deep Learning

  • Convolutional Neural Networks (CNNs) for Image Processing
  • Recurrent Neural Networks (RNNs) and LSTMs for Sequence Data
  • Transformer Models and Large Language Models (LLMs)

Module 7: AI Development Tools & Frameworks

  • TensorFlow and PyTorch
  • Scikit-Learn for ML models
  • Hugging Face and LangChain for NLP

Module 8: Natural Language Processing (NLP)

  • Text Processing and Tokenization
  • Word Embeddings (Word2Vec, GloVe, BERT)
  • Chatbots and AI-powered assistants

Module 9: AI in the Cloud & Deployment

  • AI model deployment using Flask and FastAPI
  • Cloud AI tools (AWS, Google Cloud, Azure AI)
  • Edge AI and On-Device Machine Learning

Module 10: AI Applications & Real-World Projects

  • AI for Business Intelligence and Data Analytics
  • AI-powered Recommendation Systems
  • AI in Cybersecurity and Fraud Detection

Module 11: AI Ethics & Future Trends

  • Bias in AI and Fairness in Machine Learning
  • The Future of AI: AGI and Autonomous Systems
  • Responsible AI development and best practices

Final Capstone Project

  • Real-world AI project: From Data Collection to Deployment
  • Building an end-to-end AI solution
  • Presenting and optimizing AI models
 
 
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