AI, Machine Learning with Python

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Why Learn AI-ML?

Learning AI and ML equips students with cutting-edge skills in data analysis, automation, and intelligent decision-making, making them highly valuable in the job market. These technologies are transforming industries like healthcare, finance, and software development, creating a high demand for AI/ML professionals. Mastering AI/ML opens career opportunities in data science, machine learning engineering, and AI research, providing competitive salaries and job security. Additionally, AI-driven innovation empowers students to solve real-world problems, develop smart applications, and stay ahead in the rapidly evolving tech landscape.

Course Structure

• book_2 Module 1: Coding with Python

  topicIntroduction to Python

  • • Introduction to Python, Installation, and Environment setup.
  • • Variables, Data Types, and Operators, Conditional Statements, Loops

  
  

  topicFunctions & Data Structures

  • • Functions and Scope.
  • • Lists, Tuples, Sets, Dictionaries, and String Manipulation.
  • • List & Dictionary Comprehensions.

  
  

  topic File Handling & Modules

  • • Reading/Writing Files, Importing & Using Modules.
  • • Exception Handling.

  
  

  topic Introduction to Object-Oriented Programming (OOP)

  • • Classes & Objects.
  • • Methods and Constructors.
  • • Inheritance & Polymorphism.

• book_2 Module 2: Foundations of AI & Machine Learning

  topicIntroduction to AI

  • • What is AI? History and evolution.
  • • Types of AI: Narrow, General, Super.
  • • AI ethics and societal impact.

  
  

  topicIntroduction to Machine Learning (ML)

  • • What is ML? Supervised, unsupervised, and reinforcement learning.
  • • Key ML concepts: Features, labels, models, training, testing.
  • • Basic Python for ML (NumPy, Pandas).

  
  

  topicSupervised Learning

  • • Linear regression, logistic regression.
  • • Classification algorithms (e.g., k-nearest neighbors, decision trees).
  • • Model evaluation metrics (accuracy, precision, recall, F1-score).

  
  

  topicUnsupervised Learning

  • • Clustering (e.g., k-means).
  • • Dimensionality reduction (e.g., PCA).

• book_2 Module 3: Prompt Engineering & Large Language Models (LLMs)

  topicPrompt Engineering Fundamentals

  • • What is prompt engineering?
  • • Effective prompting techniques: Zero-shot, one-shot, few-shot learning.
  • • Prompt design principles: Clarity, specificity, context.
  • • Prompting frameworks.

  
  

  topic Introduction to Large Language Models (LLMs)

  • • Architecture of LLMs (Transformers).
  • • Popular LLMs (e.g., GPT models, Llama, Bard).
  • • Using LLMs via APIs.

  
  

  topic LLM Applications

  • • Text generation, summarization, translation.
  • • Code generation and explanation.
  • • Question answering and chatbots.

  
  

  topic LLM Creation/Fine tuning

  • • Transfer learning concepts.
  • • Fine tuning an existing LLM on custom datasets.
  • • Practical examples using open source models.

  
  

  topic Prompt Engineering for specific tasks

  • • Data extraction.
  • • Sentiment analysis.
  • • Creative writing.

  
  

  topic • Ethical implications of LLMs

  
  
• book_2 Module 4: Vector Databases

  topicUnderstanding Vector Embeddings

  • • What are vector embeddings?
  • • Generating embeddings (word embeddings, sentence embeddings).
  • • Similarity search and semantic search.

  
  

  topic Introduction to Vector Databases

  • • Why vector databases?
  • • Popular vector databases (e.g., Pinecone, Weaviate, Milvus).
  • • Setting up and using a vector database.

  
  

  topicImplementing Semantic Search

  • • Indexing and querying vector data.
  • • Building search applications using vector databases.
  • • Hybrid search strategies.

  
  

  topic Implementing Vector Databases

  • • Hands on implementation of vector databases.
  • • Semantic search and retrieval.
  • • Building a semantic search applications with vector databases.

• book_2 Module 5: AI Agent Creation

  topicIntroduction to AI Agents

  • • Concept of autonomous agents.
  • • Types of AI agents: Reactive, deliberative, hybrid.
  • • Agent architecture and components.
  • • Example: Simple reactive agent implementation.

  
  

  topic Building AI Agents with LLMs

  • • Integrating LLMs with agent frameworks.
  • • Memory and planning mechanisms.
  • • Tool use and API integration.
  • • Hands-on: Building an agent that can schedule appointments and send reminders.

  
  

  topic Agent Deployment and Testing

  • • Testing and evaluation methodologies.
  • • Memory and planning mechanisms.
  • • Monitoring and maintenance.
  • • Example: Deploying an agent to a cloud platform.

• book_2 Module 6: Practical Use of Various AI Tools

  topicAI-powered Text Generation

  • • ChatGPT, Bard, Claude: Content creation, summarization, email drafting.
  • • Jasper AI, Copy.ai: Marketing content, ad copies, SEO content.

  
  

  topic AI Image & Video Generation

  • • MidJourney, DALL·E: AI-generated art, illustrations.
  • • Runway ML, Pika Labs: AI-powered video editing & animation.
  • • Canva AI, Adobe Firefly: AI-powered design tools.

  
  

  topicAI for Automation & Productivity

  • • Zapier AI, Make.com: AI-powered workflow automation.
  • • Notion AI: Automated notes, document generation.
  • • Otter.ai: AI-powered transcription & meeting summarization.

  
  

  topic AI in Coding & Development

  • • GitHub Copilot, Codeium: AI-assisted programming.
  • • OpenAI API, Google Gemini API: Integrating AI into applications.

  
  

  topic AI in Business & Analytics

  • • Tableau AI, ChatGPT for Excel: AI-driven data analysis.
  • • Chatbots (Botsonic, ManyChat): AI-powered customer service.
  • • AI-powered SEO & marketing tools (SurferSEO, Frase.io).