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Detailed Syllabus & Weekly Breakdown

Module 1: Introduction to Artificial Intelligence (Weeks 1–3)

Focus: Setting up the AI environment and understanding fundamentals

• History & Evolution: Understanding the timeline of AI, from Turing tests to modern neural networks.
• Python for AI: Crash course in Python programming, Pandas, and NumPy for data manipulation.
• Data Preprocessing: Handling missing values, categorical data, and feature scaling.
• Mathematical Foundations: Basics of linear algebra, calculus, and probability used in AI.

Module Outcome: Write Python scripts to clean data and understand the underlying math behind AI algorithms.

Module 2: Machine Learning Foundations (Weeks 4–6)

Focus: Predictive modeling and core ML algorithms

• Supervised Learning: Linear regression, logistic regression, and decision trees.
• Unsupervised Learning: K-Means clustering, PCA (Principal Component Analysis).
• Model Evaluation: Cross-validation, confusion matrices, precision, recall, and F1-score.
• Scikit-Learn: Building and tuning models using the Scikit-Learn library.

Module Outcome: Build, train, and evaluate machine learning models for classification and regression tasks.

Module 3: Deep Learning & NLP (Weeks 7–9)

Focus: Neural networks and language processing

• Neural Networks Basics: Perceptrons, activation functions, and backpropagation.
• TensorFlow & PyTorch: Introduction to deep learning frameworks.
• Natural Language Processing (NLP): Tokenization, word embeddings (Word2Vec), and text classification.
• Computer Vision Basics: Introduction to Convolutional Neural Networks (CNNs).

Module Outcome: Construct basic neural networks to process text and image data.

Module 4: AI Applications & Ethics (Weeks 10–12)

Focus: Real-world implementation and responsible AI

• Generative AI: Overview of Large Language Models (LLMs) and Prompt Engineering.
• Deployment: Serving ML models via REST APIs using Flask/FastAPI.
• AI Ethics: Bias in datasets, privacy concerns, and explainable AI (XAI).
• Capstone Project: End-to-end development of an AI solution.

Module Outcome: Deploy a functional AI model via API and understand the ethical implications of AI development.

Comprehensive Learning Outcomes

• Algorithmic Mastery: Understand and implement core machine learning and deep learning algorithms.
• Practical Implementation: Use Python, TensorFlow, and Scikit-Learn to build real-world AI applications.
• Deployment Fluency: Transition from Jupyter Notebooks to production-ready API deployments.

Assessment Weightage

Assessment Type	Weightage	Focus Area
Coding Assignments	30%	Weekly programming tasks to ensure algorithmic clarity.
AI Model Capstone Project	40%	A project where students must clean data, train a model, and deploy it via API.
Technical Interview Viva	30%	An evaluation testing end-to-end knowledge of ML pipelines and AI concepts.

The "WhiteCollar" Career Advantage

The tech industry is rapidly pivoting towards AI. This course ensures you don't just know how to use AI tools, but you understand how to build them. Recruiters prefer developers who can architect end-to-end ML pipelines, making you a highly sought-after professional from Day 1.