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AI Courses

Cutting Edge AI Technologies and Tools

Generative AI

Generative AI

Age: 13 – 60

Market Demand: 40–50% CAGR, ~400% job growth since 2022, ~3x talent gap

Number of classes: 45

Per Class:   £25    |    €30    |    $35     |     ₹700

Course Content

AI Brain: Transformers & LLM Basics

  • How Large Language Models work
  • Understanding Transformers (attention mechanism in simple terms)
  • Tokens, embeddings, and context windows
  • Popular models (GPT, LLaMA, etc.)
  • Limitations of LLMs (hallucination, bias, cost)

Real AI Power: Working with LLMs and APIs

  • Using OpenAI APIs
  • Prompt engineering basics (zero-shot, few-shot)
  • System vs user prompts
  • Controlling output (temperature, max tokens)
  • Handling errors and rate limits
  • Cost optimization strategies

Build Smart Apps: Building AI Apps

  • Setting up projects (frontend + backend basics)
  • Connecting AI to web apps
  • Building simple tools (summarizer, chatbot UI)
  • Using Python for AI apps
  • Managing user input and responses
  • Deploying basic AI apps

See, Hear, Talk AI: Multimodal & Chatbots

  • Working with text, image, and audio inputs
  • Image understanding and generation basics
  • Speech-to-text and text-to-speech
  • Designing conversational chatbots
  • Context handling in conversations
  • Chatbot UX best practices

Autonomous AI: AI Agents & Tool Calling

  • Tool calling and function calling
  • Giving AI access to tools (calculator, APIs, DB)
  • Multi-step reasoning workflows
  • Building simple autonomous agents
  • Safety and control of agents

Open AI Models: Open Source & Hugging Face

  • Introduction to open-source AI
  • Using Hugging Face
  • Exploring model hub (LLMs, vision, audio)
  • Running models locally vs cloud
  • Comparing open vs closed models
  • When to use which model

AI with Memory: RAG & Knowledge Systems

  • What is Retrieval-Augmented Generation (RAG)
  • Why LLMs need external knowledge
  • Working with embeddings
  • Vector databases (basic idea)
  • Storing and retrieving documents
  • Building a Q&A system over your data
  • Improving accuracy with RAG

Customize AI: Fine-Tuning & Model Selection

  • When to fine-tune vs use prompting
  • Basics of fine-tuning
  • Preparing datasets
  • Evaluating model performance
  • Choosing the right model for your use case
  • Cost vs performance trade-offs

Voice AI: Audio AI & Advanced Systems

  • Speech recognition systems
  • Text-to-speech systems
  • Building voice assistants
  • Real-time voice interaction
  • Integrating voice with agents
  • Use cases (customer support, assistants)

Production AI: LangChain & Production Apps

  • Introduction to LangChain
  • Building pipelines and chains
  • Managing prompts and memory
  • Logging, monitoring, and debugging AI apps
  • Scaling AI applications
  • Deployment strategies (cloud, APIs)
  • Building end-to-end production-ready AI systems
Agentic AI

Agentic AI

Age: 13 – 60

Market Demand: 40–50% CAGR, ~300% job growth since 2023, ~4x talent gap

Number of classes: 45

Per Class:   £25  |  €30    |  $35   |   ₹700

Course Content

Getting Started: Build Your First AI Agent

  • Set up environment (API keys, IDE, Git)
  • Make your first API call using OpenAI
  • Build a simple chatbot (input → response loop)
  • Convert chatbot into a basic “task agent”
  • Mini Project: AI assistant that answers and summarizes

Designing Real AI Workflows

  • Build multi-step workflows (input → process → output)
  • Create prompt chains (summarize → analyze → generate)
  • Implement retry + error handling
  • Add structured outputs (JSON responses)
  • Mini Project: AI content generator (blog/email creator)

Multi-Model AI Systems (Real Comparison)

  • Connect multiple models (GPT, Claude, Gemini)
  • Route tasks to best model (logic-based selection)
  • Build evaluator system (AI checks AI output)
  • Compare outputs and improve accuracy
  • Mini Project: AI judge system (best answer selector)

Build with Agent Frameworks

  • Create agents using LangGraph / CrewAI
  • Build graph-based workflows (nodes + edges)
  • Create multi-agent collaboration (team of agents)
  • Add memory and state handling
  • Mini Project: Multi-agent research assistant

Tool-Using Agents (Real Power)

  • Add tools (calculator, APIs, search) to agents
  • Implement function/tool calling
  • Connect agents to real-world data (web, database)
  • Build autonomous decision-making flows
  • Mini Project: AI agent that researches and gives final answers

Build Real Applications (UI + Use Case)

  • Create chatbot UI using Gradio
  • Build a personal AI assistant (like ChatGPT clone)
  • Add voice or file input
  • Build a domain-specific agent (sales, study, coding)
  • Mini Project: Deployable AI assistant with UI

Deploy & Scale Your AI Agents

  • Deploy apps using Hugging Face Spaces / APIs
  • Add logging, monitoring, and debugging
  • Optimize cost (token usage, model selection)
  • Improve performance and response speed
  • Final Project: End-to-end AI agent (problem → build → deploy)
Data Science and MAchine Learning

Machine Learning and Data Science

Age: 13 – 60

Market Demand:

ML: 20–30% CAGR, ~150% job growth since 2020, ~2x talent gap

Data Science: 25–35% CAGR, ~200% job growth since 2020, 2–3x talent gap

Number of classes: 45

Per Class:   £25  |  €30 |  $35  |  ₹700

Course Content

Data Handling with NumPy & Pandas

  • Arrays, indexing, vectorization using NumPy
  • DataFrames, filtering, grouping using Pandas
  • Data loading (CSV, Excel, JSON, APIs)
  • Data cleaning (missing values, duplicates)
  • Mini Project: Clean and structure raw dataset

Data Visualization & EDA

  • Charts using Matplotlib and Seaborn
  • Correlation, covariance, distributions
  • Histograms, boxplots, pairplots
  • Exploratory Data Analysis (EDA) workflow
  • Mini Project: Full EDA report

Statistics for Machine Learning

  • Descriptive statistics (mean, median, variance, std)
  • Probability rules (addition, multiplication)
  • Distributions:
    • Bernoulli Distribution
    • Binomial Distribution
    • Poisson Distribution
    • Normal (Gaussian) Distribution
    • Uniform Distribution
    • Log-Normal Distribution
    • Pareto Distribution
  • Central Limit Theorem
  • Hypothesis Testing:
    • Z-Test
    • T-Test
    • Chi-Square Test
    • ANOVA
  • Mini Project: Statistical analysis report

Data Preprocessing & Feature Engineering

  • Handling missing values and outliers
  • Encoding techniques:
    • One Hot Encoding (OHE)
    • Label Encoding
    • Ordinal Encoding
  • Feature selection techniques
  • Handling imbalanced data (SMOTE)
  • Scaling (Normalization, Standardization)
  • Mini Project: Build preprocessing pipeline

Supervised Learning: Regression Algorithms

  • Linear Regression
  • Multiple Linear Regression
  • Polynomial Regression
  • Regularization:
    • Ridge Regression
    • Lasso Regression
    • ElasticNet
  • Model evaluation: MSE, RMSE, MAE
  • Cross Validation techniques
  • Mini Project: Price / sales prediction system

Supervised Learning: Classification Algorithms

  • Logistic Regression
  • K-Nearest Neighbors (KNN)
  • Naive Bayes (Gaussian, Multinomial, Bernoulli)
  • Support Vector Machine (SVM / SVC)
  • Metrics: Accuracy, Precision, Recall, F1, ROC-AUC
  • Hyperparameter tuning (Grid Search, Random Search)
  • Mini Project: Spam detection / classification system

Tree-Based & Ensemble Algorithms

  • Decision Tree (Gini, Entropy, Information Gain)
  • Random Forest
  • Bagging
  • Boosting:
    • AdaBoost
    • Gradient Boosting
    • XGBoost
  • Feature importance
  • Mini Project: High-performance ML model

Unsupervised Learning Algorithms

  • Clustering:
    • K-Means
    • Hierarchical Clustering
    • DBSCAN
  • Dimensionality Reduction:
    • PCA (Principal Component Analysis)
  • Anomaly Detection:
    • Isolation Forest
    • Local Outlier Factor (LOF)
  • Mini Project: Customer segmentation / anomaly detection

End-to-End ML Systems & Deployment

  • ML pipeline (data → preprocessing → model → prediction)
  • Model saving (Pickle)
  • Build apps using Flask / Streamlit
  • ETL pipelines and data ingestion
  • Experiment tracking (MLflow)
  • Deployment using AWS + Docker
  • Final Project: Complete ML system (real-world problem → deployed app)
Deep Learning

Deep Learning

Age: 13 – 60

Market Demand: 30–40% CAGR, ~180% job growth since 2020, 2–3x talent gap

Number of classes: 45

Per Class:   £25 | €30 |  $35 |  ₹700

Course Content

Build Your First Neural Network (From Scratch)

  • Code a neural network using only Python (no frameworks)
  • Implement activation functions: Sigmoid, ReLU, Tanh
  • Manually code forward pass + backpropagation
  • Train using Gradient Descent
  • Mini Project: Predict student scores / simple binary classifier

Train Real ANN Models (Using Frameworks)

  • Build ANN models using TensorFlow / Keras
  • Use optimizers: SGD, Adam, RMSProp
  • Apply loss functions (MSE, Cross-Entropy)
  • Add Dropout, regularization
  • Mini Project: Customer churn prediction / tabular classification

Build Image Classifiers with CNN

  • Load and preprocess image datasets
  • Build CNN with Conv → ReLU → Pooling layers
  • Train and test image classification models
  • Improve accuracy using data augmentation
  • Mini Project: Custom image classifier (plants, animals, objects)

Build Time-Series & Sequence Models

  • Prepare sequential/time-series datasets
  • Build models using RNN, LSTM, GRU
  • Train models for prediction tasks
  • Evaluate predictions and tune models
  • Mini Project: Stock price / sales forecasting system

Build Transformer-Based Apps

  • Use prebuilt transformer models (no heavy theory)
  • Implement text generation / summarization
  • Fine-tune transformer for simple task
  • Build sequence-to-sequence apps
  • Mini Project: AI text generator / chatbot

Build Unsupervised & Representation Models

  • Build Autoencoders (basic + denoising)
  • Use K-Means for clustering tasks
  • Apply Self-Organizing Maps (basic implementation)
  • Perform anomaly detection
  • Mini Project: Fraud detection / anomaly detection system

Build Real-World Deep Learning Systems

  • Build recommendation system (basic)
  • Use Boltzmann Machine / RBM (basic implementation)
  • Train and test complete DL pipelines
  • Use PyTorch for custom models
  • Final Project: End-to-end AI app (image / text / prediction system with UI)
Prompt Engineering

Prompt Engineering

Age: 13 – 60

Market Demand: 35–50% CAGR, 300–400% job growth since 2022, 3–5x talent gap

Number of classes: 35

Per Class:   £25  |  €30  |  $35   |   ₹700

Course Content

Prompt Foundations: Start Writing Working Prompts

  • Write prompts and instantly test outputs in ChatGPT
  • Experiment with different inputs and see how responses change
  • Break prompts into input → instruction → output format
  • Control length, tone, and style using simple tweaks
  • Mini Project: Build a prompt set for blog writing + summaries

Core Prompting: Make Prompts More Powerful

  • Use role prompting (act as teacher, coder, marketer, etc.)
  • Create few-shot prompts with examples
  • Force structured outputs (tables, JSON, bullet points)
  • Build reusable prompt templates
  • Mini Project: AI content generator (emails, captions, scripts)

Advanced Prompting: Build Smart Reasoning Prompts

  • Use Chain-of-Thought for step-by-step answers
  • Implement ReAct (reason + act workflows)
  • Try self-consistency (multiple answers → best output)
  • Create persona-based prompts (expert, beginner, critic)
  • Mini Project: AI problem solver (math, logic, case studies)

ChatGPT Mastery: Use AI Like a Pro

  • Customize behavior using instructions and memory
  • Use vision (image input → response)
  • Test agent-like workflows inside ChatGPT
  • Build repeatable workflows using saved prompts
  • Mini Project: Personal AI assistant (study / business helper)

API & Prompt Apps: Turn Prompts into Apps

  • Make API calls using OpenAI
  • Use Playground to test prompts quickly
  • Build simple prompt-based apps (Python/JS)
  • Stream responses in real-time
  • Mini Project: AI chatbot web app

Tool Calling & Agent Workflows

  • Create prompts that trigger function/tool calls
  • Build agents that use tools (calculator, APIs, DB)
  • Design multi-step workflows (input → tools → output)
  • Handle async execution and responses
  • Mini Project: AI assistant that fetches real data + answers

RAG Systems: Give AI Your Own Data

  • Convert text into embeddings
  • Store and search using vector databases
  • Build RAG pipeline (retrieve → generate)
  • Improve answers using your own documents
  • Mini Project: Chat with your PDF / notes system

Frameworks Power: Build Scalable Prompt Systems

  • Build workflows using LangChain
  • Create graph-based flows using LangGraph
  • Manage prompts, memory, and tools
  • Build multi-step AI pipelines
  • Mini Project: Multi-step AI workflow app

Multimodal AI: Create Images & Vision Apps

  • Generate images using DALL·E
  • Write prompts for image tools like Midjourney
  • Use image + text together (vision tasks)
  • Build apps that take image input
  • Mini Project: AI image generator + analyzer

Evaluation & Optimization: Improve Prompts Like a Pro

  • Test prompts with different inputs
  • Compare outputs and refine prompts
  • Use evaluation tools (basic + automated)
  • Optimize prompts for cost and speed
  • Mini Project: Prompt optimization system (best prompt selector)
Python For AI

Python For AI

(Basic to Advance)

Age: 10 – 60

Application: Major Prerequisite for all the AI domains.

Total number of classes: 35

Per Class:   £25  | €30 | $35  |  ₹700

Course Content

Python Basics: Start Coding Real Programs

  • Write programs using variables, data types, input/output
  • Perform calculations using operators
  • Build small scripts (calculator, unit converter, number checker)
  • Mini Project: Multi-purpose calculator (menu-driven program)

Control Flow: Build Logic-Based Programs

  • Write decision-making programs using if-else and nested conditions
  • Use loops (for, while) to automate tasks
  • Control flow using break and continue
  • Mini Project: Number guessing game / pattern generator

Data Structures: Work with Real Data

  • Store and manipulate data using lists, tuples, sets, dictionaries
  • Perform indexing, slicing, and updates
  • Solve real problems (search, sort, count frequency)
  • Mini Project: Contact book / student record manager

Functions & Modular Coding

  • Write reusable functions with parameters and return values
  • Organize code into modules
  • Use built-in functions effectively
  • Mini Project: Utility toolkit (multiple functions in one app)

File Handling & Error Handling

  • Read and write text/CSV files
  • Process real data from files
  • Handle errors using try-except
  • Build robust programs that don’t crash
  • Mini Project: Log analyzer / CSV data processor

Object-Oriented Programming (OOP)

  • Create classes and objects
  • Use attributes, methods, constructors
  • Implement basic inheritance
  • Build structured applications
  • Mini Project: Library system / simple banking system

Numerical Computing with NumPy

  • Work with arrays using NumPy
  • Perform fast calculations and vectorized operations
  • Handle matrices and reshape data
  • Mini Project: Data calculator / matrix operations tool

Data Handling with Pandas

  • Load and manipulate datasets using Pandas
  • Clean data (missing values, duplicates)
  • Filter, group, and analyze data
  • Mini Project: Real dataset analysis (sales / student data)

Data Visualization: Turn Data into Insights

  • Create charts using Matplotlib
  • Build advanced visuals using Seaborn
  • Plot trends, distributions, comparisons
  • Mini Project: Visual report dashboard

Introduction to AI & Machine Learning (Hands-on)

  • Train simple ML models using Scikit-learn
  • Work with features and labels on real datasets
  • Build basic prediction models
  • Try simple models using TensorFlow / PyTorch
  • Final Project: Simple prediction app (price / classification model)
JavaScript For AI

JavaScript For AI

(Basic to Advance)

Age: 10 – 60

Application: Prerequisite only if you want to implement AI using JavaScript

Total number of classes: 35

Per Class:   £25  |  €30  |  $35  |  ₹700

Course Content

JavaScript Basics: Start Building Scripts

  • Write JS programs using variables, data types, operators
  • Run code in browser and Node.js
  • Build small utilities (calculator, string tools)
  • Mini Project: Interactive calculator in browser

Control Flow & Functions: Build Logic

  • Use if-else and loops to control flow
  • Write reusable functions and understand scope
  • Solve real problems (validation, automation)
  • Mini Project: Form validator (age, email, inputs)

Arrays & Objects: Handle Real Data

  • Work with arrays and objects
  • Use JSON for storing and exchanging data
  • Apply map, filter, reduce for transformations
  • Mini Project: To-do app with data stored in objects

Async JavaScript: Handle Real-Time Operations

  • Use callbacks, promises, async/await
  • Fetch and process data asynchronously
  • Handle errors in async code
  • Mini Project: Weather app (real-time API data)

APIs: Connect to Real Services

  • Use fetch to call REST APIs
  • Handle JSON responses
  • Send and receive data from APIs
  • Mini Project: Movie search app using public API

DOM for AI Apps: Build Interactive UI

  • Capture user input and trigger events
  • Update UI dynamically
  • Build interactive web interfaces
  • Mini Project: Chat UI (input box + response display)

AI with APIs: Build AI Features

  • Use OpenAI APIs in JavaScript
  • Send prompts and display AI responses
  • Control outputs (tone, length, format)
  • Mini Project: AI text generator (captions, emails, answers)

TensorFlow.js: Run AI in Browser

  • Work with tensors using TensorFlow.js
  • Load and run pre-trained models
  • Perform predictions in browser
  • Mini Project: Image classifier (basic browser AI)

AI Web Apps: Build Complete AI Products

  • Combine UI + API + AI features
  • Build chatbots and generators
  • Handle user sessions and responses
  • Mini Project: ChatGPT-like web app

Deployment: Launch Your AI Apps

  • Deploy apps using hosting platforms
  • Optimize performance and loading
  • Handle scaling basics
  • Final Project: Deploy full AI app (frontend + AI integration)

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