• HTML: Build the structure of the web.
• CSS: Design and style engaging user interfaces.

• Mastering the core foundations of modern JavaScript
• Essential building blocks like variables, data types, and logical operators, and learn to control program flow using conditionals and loops.
• Functions, arrays, and objects
• ES6+ features like arrow functions, template literals, and async/await
• Hands-on coding challenges

• DOM Manipulation: Learn how to bring your web pages to life by dynamically interacting with HTML and CSS.
• Event Handling: Master event listeners, bubbling, and delegation to create responsive and interactive user experiences.
• Asynchronous JavaScript: Dive deep into asynchronous operations with Promises and the Fetch API to communicate with servers and handle data.
• Browser Storage: Explore localStorage and sessionStorage to persist data on the client-side.
• Web APIs: Get hands-on with essential browser APIs to build modern, feature-rich web applications.

• TypeScript: Write robust, type-safe JavaScript for scalable applications.
• Angular: Develop powerful, modern front-end applications with a feature-rich framework.

• Node.js: Build scalable, efficient backend services.
• PostgreSQL: Manage and query relational databases effectively.

• Essential Linux Shell Commands
• Git: Master version control for collaborative software development.
• VS Code

Yes! This course is designed from the ground up for individuals with no prior programming experience. We start with the absolute basics and build your skills progressively.

You'll have access to our dedicated student community, live Q&A sessions with the instructor, and personalized feedback on your projects to ensure you're never stuck.

Not at all. Any modern computer (Windows, macOS, or Linux) capable of running a web browser and a code editor is sufficient for this course. We'll guide you through setting up a free and efficient development environment.

• Introduction to AI
  • What is Artificial Intelligence?
  • Real-world impact and applications of AI in software engineering and daily life
• Understanding Machine Intelligence
  • How arithmetic and logical operations are handled by machines
  • From logic to learning: Why traditional programming can't solve everything.
  • What is Machine Learning?
  • Common Machine Learning algorithms (e.g., Linear Regression, Decision Trees, K-Means)
• Learning from the Brain
  • How the human brain learns
  • Biological neurons: Structure and function
  • Synaptic learning and information flow in the brain

• Artificial Neural Networks (ANNs)
  • What are ANNs?
  • How ANNs are inspired by the brain (but are not the same)
  • Structure of an ANN: Layers, neurons, weights, activation functions
  • Differences between ANNs and biological neural networks
  • History and evolution of ANN-based problem solving
  • Training an ANN: Core concepts and processes
  • Forward propagation: How a neural network makes predictions.
• Training an ANN: The Backpropagation Algorithm
  • What is backpropagation and why is it essential?
  • Introduction to Gradient Descent
  • The Chain Rule and its role in updating weights
  • Step-by-step example of training a neural network using backpropagation
  • Common challenges: Vanishing gradients, overfitting
  • Limitation of AI: Black box nature of neural networks
• Deep Learning
  • What is Deep Learning?
  • Why depth (multiple layers) matters in solving complex problems
  • Real-world examples of Deep Learning (e.g., image recognition, natural language processing)
  • A brief introduction to key architectures: CNNs and RNNs (to be explored in detail in future modules).
• Summary & Revision
  • Key takeaways from each section
  • Addressing common misconceptions:
    • "Neural networks mimic the brain exactly." — This is incorrect. In reality, artificial neural networks are fundamentally different from how the brain actually works. They are only loosely inspired by the brain’s structure and rely on simplified mathematical models rather than biological processes. In fact, we do not yet fully understand how the brain processes information, which makes true imitation impossible.
    • "AI has real intelligence": This is incorrect. Actually, AI is a mathematical system with no consciousness or true understanding.
  • Quiz or practice questions to reinforce learning

• Feedforward Neural Networks (FFNN)
• Convolutional Neural Networks (CNNs) for image processing
• Recurrent Neural Networks (RNNs) for sequence data
• Understanding the Transformer architecture: The foundation of modern LLMs

• Python
• PyTorch: A Flexible Deep Learning Framework
• Jupyter Notebooks: Interactive Development and Experimentation
• VS Code: Lightweight IDE for AI Development
• High Performance Computing (HPC) for AI
  • GPUs (Graphics Processing Units)
  • TPUs (Tensor Processing Units)
  • CUDA: Enabling GPU Acceleration for AI Tasks
• Google Colab: Cloud-Based Notebooks with GPU/TPU Support

• Why Python for AI and ML?
• Installing Python and Setting Up Your Environment
• Using Jupyter Notebooks for Python Coding
• Python Basics
  • Variables, Data Types, and Type Casting
  • Input and Output
  • Conditional Statements (if, elif, else)
  • Loops (for, while)
• Data Structures in Python
  • Lists, Tuples, and Sets
  • Dictionaries: Key-Value Data Storage
  • List and Dictionary Comprehensions
• Functions and Modules
  • Defining and Calling Functions
  • Arguments, Return Values, and Scope
  • Lambda Functions and Higher-Order Functions
  • Importing and Creating Python Modules
• Working with Files and Exceptions
  • Reading and Writing Files
  • Exception Handling: try, except, finally
• Object-Oriented Programming (OOP) in Python
  • Classes and Objects
  • Inheritance and Polymorphism
  • Constructors and Magic Methods (__init__, __str__)
• Introduction to NumPy
  • Arrays and Array Operations
  • Broadcasting and Vectorization
  • Basic Math and Statistics with NumPy
• Introduction to Pandas
  • Series and DataFrames
  • Reading and Writing CSV/Excel Files
  • Basic Data Cleaning and Manipulation
• Data Visualization (Optional but Recommended)
  • Plotting with Matplotlib
  • Quick Charts with Seaborn
• Pythonic Coding and Best Practices
  • Writing Clean and Readable Code
  • Introduction to Debugging
  • Common Python Idioms
  • Working with Virtual Environments (venv, pip)
• Hands-On Mini Projects
  • Implementing a simple calculator
  • Data exploration on a sample CSV dataset using Pandas
  • Building a class to represent a student record system
  • Plotting a graph of daily temperatures using Matplotlib

• Introduction to PyTorch and Its Ecosystem
• Installing PyTorch and Verifying Installation
• Tensors: Creation, Shapes, and Basic Operations
• Tensor Operations: Arithmetic, Reshaping, and Indexing
• Automatic Differentiation with autograd
• Building Models with torch.nn.Module
• Common Loss Functions and Optimizers
• The Full Training Loop: A Step-by-Step Guide
• Hands-On: A Simple Regression Model (y = 2x)
• Using the GPU with PyTorch
• Working with Data: Datasets and DataLoaders
• Saving and Loading Models
• FFNN Project: Image Classification on MNIST
• CNN Project: Image Classification on CIFAR-10
• RNN Project: Character-Level Text Generation
• LSTM Project: Sentiment Analysis on IMDb Reviews
• Transformer Project: Next Word Predictor
• Applying Transfer Learning with Pre-trained Models
• Best Practices and Debugging Tips for PyTorch
• From Training to Production: An Overview of Model Deployment

• Introduction to Natural Language Processing (NLP)
• From Traditional NLP to LLMs
• The Engine Behind LLMs: Predicting the Next Token
• Understanding Tokenization and Embeddings
• The Inner Workings of LLMs: How They Generate Human-like Text
• Introduction to Leading LLMs: ChatGPT, Gemini, LLaMA, and Copilot
• Advanced Prompting Techniques and Prompt Engineering

• The Modern Approach: Why Not Train from Scratch?
  • The Challenge of Scale: Cost, Data, and Time
  • The Power of Pre-Training
  • Transfer Learning vs. Fine-Tuning
• Exploring the Pre-Trained Model Ecosystem
  • Anatomy of a Pre-Trained Model: Body and Head
  • Model Hubs: Hugging Face and PyTorch Hub
  • Choosing the Right Model for Your Task
• The Fine-Tuning Workflow: A Step-by-Step Guide
  • Step 1: Load a Pre-Trained Model and Freeze Layers
  • Step 2: Adapt the Output Head for a New Task
  • Step 3: Train the New Head on a Custom Dataset
  • Step 4: Unfreeze and Fine-Tune the Body with a Low Learning Rate
• Fine-Tuning in Action: Key Domains
  • Fine-Tuning for Computer Vision (e.g., ResNet, EfficientNet)
  • Fine-Tuning for Natural Language Processing (e.g., BERT, GPT)
• Practical Considerations & Best Practices
  • Data Preprocessing for Pre-Trained Models
  • Resource Management: GPUs, Quantization, and Pruning
  • Ethical Implications and Inherited Bias
• Summary & Revision
  • Key Takeaways and Benefits
  • Common Pitfalls to Avoid
  • Quiz and Practice Scenarios

• Leveraging Pre-Built AI Services and APIs
• AI Orchestration with LangChain and LlamaIndex
• Working with Vector Databases
• Building Retrieval-Augmented Generation (RAG) Systems
• Data Preparation and Indexing for RAG
• Fine-Tuning Pre-Trained Models
• Real-Time Object Detection with YOLO

• Problem Definition and Requirements Gathering
• Feasibility Analysis and Data Assessment
• Designing AI Solutions
• Data Preparation and Model Development
• Model Evaluation and Testing
• Deployment Strategies for AI Models
• Monitoring, Maintenance, and Model Retraining
• Key Differences Between Traditional and AI Development Lifecycles

• Limitations of AI: The "black box" problem
• Explainability: Introduction to Explainable AI (XAI)
• Understanding and mitigating bias in AI models
• Ensuring fairness, transparency, and accountability in AI systems
• Security considerations in AI systems
• Building responsible AI products with a human-centric approach

No, this program is specifically designed for experienced software engineers who are comfortable with at least one programming language and have experience building applications.

Not at all. We cover the necessary concepts from a practical, software engineer's perspective. The focus is on application and integration, not on theoretical research.

A modern computer is sufficient for most of the course. For more computationally intensive tasks like model fine-tuning, we will guide you on how to leverage cloud services.

Part 1: Basics of C Programming

• Introduction to C and its real-world applications
• Structure of a C program
• Compilation and execution
• Character set, tokens, identifiers
• Keywords, variables, and constants
• Basic data types and type conversions
• Operators: arithmetic, relational, logical, bitwise
• Operator precedence and expressions
• Input and output statements (scanf, printf)
• Writing and executing simple programs

Part 2: Control Flow and Logic Building

• Conditional statements: if, if-else, nested if, switch
• Looping constructs: while, do-while, for
• Loop control: break, continue, nested loops
• Logical problem-solving using control statements

Part 1: Arrays and Enumerated Types

• One-dimensional arrays: declaration, initialization, access
• Two-dimensional arrays and matrix operations
• Practical programs: matrix addition, multiplication
• Search algorithms: sequential (linear) search
• Sorting: bubble sort
• Enumerated data types and typedef

Part 2: Strings and Text Processing

• Declaring and initializing strings
• Reading and displaying strings
• String manipulation using standard library functions
• Basic string matching programs

Part 1: Functions and Modularity

• Function definition, prototype, and invocation
• Parameter passing: by value and by reference
• Recursion and examples
• Passing arrays to functions
• Macros and preprocessor directives
• Command-line arguments

Part 2: Structures, Unions, and Storage Classes

• Structure definition, declaration, and member access
• Array of structures
• Passing structures to functions
• Unions and memory sharing
• Storage classes: auto, static, extern, register

Part 1: Pointers and Dynamic Memory

• Pointer basics and pointer arithmetic
• Pointers with arrays and strings
• Pointer to pointer
• Array of pointers
• Pointer to functions
• Pointer to structures
• Dynamic memory allocation: malloc, calloc, realloc, free

Part 2: File Handling in C

• File types: text and binary
• File operations: open, close, read, write
• Standard file I/O functions: fscanf, fprintf, fread, fwrite
• File navigation: fseek, ftell
• Programs for reading/writing structured data using files

Yes! This course is perfect for those with no prior programming experience. We start from the very beginning and build your knowledge step-by-step.

This course is ideal for students and beginners who want to build a strong foundation in C programming and problem-solving. No prior coding experience is required.

No. The course begins with the basics and progresses gradually. All you need is a willingness to learn and practice.

Most exercises are first done on paper to help you build strong logical thinking and problem-solving skills. For running code on your computer, you’ll need a code editor (like VS Code) and a C compiler. Don’t worry. We’ll guide you through the installation and setup during the course.

Not at all. The course emphasizes hands-on learning through coding exercises, examples, and practical applications.

Yes. The course is aligned with common academic topics and also builds essential skills for technical interviews. While the order of topics may vary, it closely matches syllabi such as B.Tech (Kerala). We encourage you to verify the course content with your own curriculum.