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

  topicPython Basics

  • • Introduction to Python, Installation, and IDE Setup.
  • • Variables, Data Types, and Operators
  • • Conditional Statements (if-else, nested if)
  • • Loops (for, while, break, continue)

  
  

  topicFunctions & Data Structures

  • • Functions: Definition, Scope, Lambda
  • • Lists & List Comprehensions
  • • Tuples, Sets, Dictionaries
  • • String Manipulation (slicing, formatting)

  
  

  topic File Handling & OOP

  • • File I/O (read, write, append)
  • • Exception Handling (try/except)
  • • OOP: Classes, Objects, Methods
  • • Constructors, Inheritance, Polymorphism

• book_2 Module 2: AI & Machine Learning

  topicIntroduction to AI & ML

  • • What is AI? History, Evolution, AI Types
  • • AI Ethics & Societal Impact
  • • What is ML? Types: Supervised, Unsupervised, RL
  • • ML Workflow: Features, Labels, Train-Test

  
  

  topicRegression & Classification

  • • Linear Regression (math, coding)
  • • Logistic Regression (binary classification)
  • • Classification: k-Nearest Neighbours, Decision Trees
  • • Metrics: Accuracy, Precision, Recall, F1-score

  
  

  topicTree-Based Models

  • • Decision Tree advanced concepts (pruning)
  • • Random Forest (bagging, feature importance)
  • • XGBoost (boosting concept, hands-on)

  
  

  topicClustering & PCA

  • • Clustering: k-means, elbow method
  • • Hierarchical clustering basics
  • • Dimensionality Reduction: PCA, Variance explanation

  
  

  topicAnomaly Detection

  • • Isolation Forest (concept, implementation)
  • • DBSCAN (density-based clustering)
  • • Case Study: Anomaly detection in datasets

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

  topicPrompt Engineering Fundamentals

  • • What is Prompt Engineering?
  • • Zero-shot, One-shot, Few-shot techniques
  • • Prompt Design Principles (clarity, context)
  • • Prompting Frameworks (e.g., Chain-of-Thought)

  
  

  topic Introduction to Large Language Models (LLMs)

  • • LLM Architecture (Transformers, attention)
  • • Popular LLMs (GPT, Llama, Bard)
  • • Using LLM APIs & OpenAI Playground

  
  

  topic Fine-Tuning & Applications

  • • LLM Applications: Text, Summarization, Code, Chatbots
  • • Fine-tuning LLMs (LoRA, Transfer Learning)
  • • Ethics & Risks of LLMs (bias, misuse)

• book_2 Module 4: RAG & Embeddings

  topicUnderstanding RAG & Embeddings

  • • What is RAG? (Retriever + Generator)
  • • Embeddings: Word, Sentence, Document vectors
  • • Similarity Search (cosine, dot-product)
  • • RAG Use Cases (chatbots, enterprise search)

  
  

  topic Vector Databases

  • • Why Vector Databases? (Pinecone, Weaviate, FAISS)
  • • Indexing & Querying embeddings
  • • Hybrid Search Strategies (semantic + keyword)

  
  

  topicImplementing RAG

  • • Building a RAG pipeline (Retriever + LLM)
  • • Connecting LLMs with vector DBs (hands-on)
  • • Deploying a simple search-enhanced chatbot

• book_2 Module 5: Agentic AI

  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 AI Agent Creation

  • • Integrating LLMs with agent frameworks.
  • • Hands-on: Building an agent that can schedule appointments and send reminders.

• 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 Coding & Automation

  • • GitHub Copilot, Codeium: AI-assisted programming.
  • • OpenAI API, Google Gemini API: Integrating AI into applications.
  • • Zapier AI, Make.com: AI-powered workflow automation.