Applied AI: NLP, Computer Vision, Robot & GenAI Deployment

About this course

Course Introduction:

Artificial Intelligence has rapidly evolved from academic theory to real-world application. From powering chatbots to controlling autonomous robots, analyzing images, and generating synthetic content, AI is everywhere. This course is designed to give learners a robust, practical foundation in applied AI. We’ll explore six key areas: Natural Language Processing, Computer Vision, Robotics, Hallucination Management in Generative AI, Deployment Strategies, and a curated toolbox of AI tools. Whether you’re looking to enter the AI field, enhance your data science skills, or manage AI projects more effectively, this course offers practical insights, hands-on techniques, and modern best practices to help you succeed.

Section 1: Natural Language Processing (NLP)We begin with Natural Language Processing—the field that enables machines to understand and generate human language. This section covers the Basics of NLP, followed by Text Preprocessing techniques like tokenization, stopword removal, and stemming. You'll explore Text Classification using supervised learning, delve into Named Entity Recognition (NER) for extracting structured data, and conduct Sentiment Analysis to gauge opinion from text. Finally, we’ll examine powerful Language Generation Models like BERT and GPT, highlighting how they’re transforming tasks like summarization, translation, and conversational AI.Section 2: Computer Vision

In this section, you’ll explore how machines “see” and interpret visual data. Starting with Image Processing Basics, you’ll learn about filtering, noise reduction, and enhancement. Feature Extraction dives into edge detection and feature mapping techniques. You'll then explore Object Detection algorithms like YOLO and SSD, as well as Image Segmentation for p

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