AI Course in Hauz Khas

This module provides an overview of Artificial Intelligence and its real-world relevance.

Learners understand how intelligent systems function and learn key AI terminologies.

Different types of AI and their applications are explained clearly.

This module builds a strong foundation for further learning.

This module introduces Python as the core language used in AI development.

Learners start with programming basics and gradually move to logical coding.

Hands-on exercises help understand Python syntax and structure.

Regular practice improves coding confidence.

This module covers mathematical concepts required for AI algorithms.

Topics include linear algebra, probability, and basic statistics.

Learners understand how math supports AI decision-making.

Concepts are explained using practical examples.

This module focuses on machine learning concepts and techniques.

Learners explore supervised and unsupervised learning methods.

Model building, testing, and evaluation are explained practically.

Hands-on implementation strengthens understanding.

This module teaches data preprocessing and preparation techniques.

Learners clean, organize, and transform raw datasets effectively.

Feature engineering improves model performance and accuracy.

The importance of data quality is emphasized.

This module introduces neural networks and deep learning concepts.

Learners understand layers, neurons, and activation functions.

Applications such as speech and image recognition are explored.

Practical sessions improve clarity and confidence.

This module focuses on AI techniques used for text analysis.

Learners work with sentiment analysis, chatbots, and text processing.

Business use cases are discussed in detail.

This module supports conversational AI development.

This module explains how AI works with images and visual data.

Learners understand image processing and object detection concepts.

Hands-on projects involve real image datasets.

Industry applications are explored.

This module covers AI model integration into real applications.

Learners understand APIs, deployment workflows, and cloud basics.

Model monitoring and performance evaluation are introduced.

This enhances real-world implementation skills.

This module focuses on ethical challenges in AI development.

Learners understand bias, data privacy, and transparency issues.

Responsible AI practices are discussed through examples.

This builds professional accountability.