This topic covers the basics of what NLP is, how it works, and its relevance in today’s AI landscape. It introduces key concepts like tokenization, stemming, and text classification.

Gives students a clear understanding of how AI can interpret human language and opens doors to industries such as tech, customer service, and healthcare.

''Intro to Natural Language Processing (NLP)'' by ''deeplizard'' on YouTube

 Students will learn how to clean and prepare raw text for NLP tasks, including removing stop words, tokenizing text, and dealing with special characters.

 Preprocessing is essential for ensuring accurate NLP model performance in applications like chatbots or text analysis tools.

''Introduction to NLP | Text Cleaning and Preprocessing'' by ''Normalized Nerd''

 Explore different language models that computers use to understand and generate human text. N-grams, Bag of Words, and TF-IDF are commonly used for text representation.

 Helps businesses with content analysis, summarization, and automating text-based tasks such as filtering spam emails or auto-generating responses.

''Text Representation | NLP Lecture 4 | Bag of Words | Tf-Idf | N-grams, Bi-grams and Uni-grams'' by ''CampusX'' on YouTube

 Learn how deep learning models like RNNs (Recurrent Neural Networks) and transformers (e.g., BERT) are used in advanced NLP applications.

These models power state-of-the-art applications like language translation, making businesses more competitive with better, more intuitive AI systems.

''Day 6-Recurrent Neural Network Indepth Intuition And NLP Application|Krish Naik'' by ''Krish Naik'' on YouTube

Students will dive into how NLP is used to detect emotions, opinions, or attitudes expressed in text data (e.g., reviews, social media posts).

Sentiment analysis helps businesses gauge customer satisfaction and adjust marketing strategies based on public sentiment.

''Stock Sentiment Analysis using News Headlines'' by ''Krish Naik'' on YouTube

This lesson covers how AI can generate human-like text based on data inputs, including chatbots and automatic text summarization tools.

NLG can save time in creating automated responses or generating summaries for reports, improving efficiency in business processes.

''Natural Language Processing (NLP) & Text Mining Tutorial Using NLTK | NLP Training | Edureka'' by ''edureka!'' on YouTube

 Explore how NLP is used to process spoken language, converting speech into text (e.g., Siri, Alexa).

Companies can deploy speech-based systems for hands-free customer service or voice-activated devices, enhancing user experiences.

''How Does Speech Recognition Work? Learn about Speech to Text, Voice Recognition and Speech Synthesis'' by ''Acadaimy'' on YouTube

A detailed look at how NLP is applied in industries like e-commerce (product recommendations), healthcare (medical record analysis), and legal (contract analysis).

 Knowing these applications allows businesses to optimize customer interactions, process large-scale text data, and improve decision-making.

''20 Natural Language Processing Examples For Business - PART 1'' by ''Wonderflow'' on YouTube

Discuss ethical considerations around privacy, bias, and fairness in NLP models.

Understanding the ethical side helps businesses build fairer, more inclusive AI systems, which is crucial for brand reputation.

''Stanford CS224N: NLP with Deep Learning | Winter 2019 | Lecture 19 – Bias in AI'' by ''Stanford Online'' on YouTube

Welcome to the quiz section. Before you begin, please make sure you have watched the lesson videos thoroughly, as the questions are designed to test your understanding of key concepts discussed.

Here are a few things to keep in mind:

1. This is a timed quiz, so you’ll need to manage your time effectively.
2. Read each question carefully and choose the best answer based on the material covered in the lesson.
3. The quiz is an excellent opportunity to test your knowledge and reinforce what you’ve learned, so try your best!