Slide 1: Title Slide
Title: Natural Language Processing (NLP) - Simplified
Subtitle: Introduction to Concepts, Techniques, and Applications
Visual: Relevant image or graphic (e.g., AI interacting with text).
Slide 2: Agenda
Introduction to NLP
NLP Engineer Role & Roadmap
Concepts: AI vs. Machine Learning vs. Deep Learning
Techniques and Pipelines
Real-World Applications
Career Insights and Salaries
Slide 3: What is NLP?
Definition: Branch of AI focusing on human-computer interaction using natural language.
Goals: Enable machines to read, understand, and process text like humans.
Slide 4: Importance of NLP
Bridge between human communication and machine processing.
Examples: Sentiment analysis, chatbots, and language translation.
Slide 5: NLP Engineer: Role and Responsibilities
Develop and implement NLP solutions.
Key Skills: Programming (Python, Java), Machine Learning, Data Science.
Example tasks: Designing NLP pipelines, sentiment analysis tools.
Slide 6: Roadmap to Becoming an NLP Engineer
Education: Degrees in Computer Science, Linguistics, or AI.
Skills: NLP techniques, libraries (NLTK, SpaCy).
Projects: Chatbots, sentiment analyzers, translation models.
Slide 7: AI, Machine Learning, and Deep Learning: Overview
AI: Broad domain of intelligent systems.
Machine Learning: Subset of AI that learns from data.
Deep Learning: Subset of ML focusing on neural networks.
Slide 8: NLP Pipeline: Overview
Sequential processes for text processing.
Components: Segmentation, Tokenization, Stemming, Lemmatization, POS Tagging, Named Entity Recognition.
Slide 9: Step 1: Sentence Segmentation
Dividing text into manageable units.
Example: Splitting sentences at punctuation marks.
Slide 10: Step 2: Tokenization
Breaking sentences into words or tokens.
Importance: Enables syntax and semantic analysis.
Slide 11: Step 3: Stemming
Definition: Stripping affixes to find word stems.
Example: "Jumping" → "Jump".
Slide 12: Step 4: Lemmatization
Process of reducing words to dictionary root forms.
Difference from stemming: Always produces valid dictionary words.
Slide 13: Step 5: Part of Speech (POS) Tagging
Assigning grammatical roles to words (e.g., noun, verb).
Importance: Understanding sentence structure and context.
Slide 14: Step 6: Named Entity Recognition (NER)
Identifying entities like names, locations, and monetary values.
Applications: Sorting data, summarizing content.
Slide 15: Text Mining in NLP
Exploring and analyzing unstructured text.
Techniques: Information extraction, clustering, summarization.