Natural Language Processing (NLP) with Python

Xcel Learning

Review Questions and Assessments

Included in course price

• Chapter 1: Introduction to Natural Language Processing 07:00

  • 1: Chapter 1: Introduction to Natural Language Processing Preview 07:00

• Chapter 2: Python Fundamentals for NLP 06:00

  • 2: Chapter 2: Python Fundamentals for NLP 06:00

• Chapter 3: Text Preprocessing Techniques 06:00

  • 3: Chapter 3: Text Preprocessing Techniques 06:00

• Chapter 4: Linguistic Foundations 06:00

  • 4: Chapter 4: Linguistic Foundations 06:00

• Chapter 5: Feature Extraction Techniques 06:00

  • 5: Chapter 5: Feature Extraction Techniques 06:00

• Chapter 6: Word Embeddings 06:00

  • 6: Chapter 6: Word Embeddings 06:00

• Chapter 7: Text Classification 06:00

  • 7: Chapter 7: Text Classification 06:00

• Chapter 8: Sequence Models 06:00

  • 8: Chapter 8: Sequence Models 06:00

• Chapter 9: Deep Learning for NLP 06:00

  • 9: Chapter 9: Deep Learning for NLP 06:00

• Chapter 10: Transformers and Large Language Models 06:00

  • 10: Chapter 10: Transformers and Large Language Models 06:00

• Chapter 11: Advanced NLP Applications 06:00

  • 11: Chapter 11: Advanced NLP Applications 06:00

• Chapter 12: NLP in Production 06:00

  • 12: Chapter 12: NLP in Production 06:00

• Review Questions and Assessments 00:00

  • 13: Review Questions and Assessments -

Discover the Exciting Subjects Awaited in This Course!

Chapter 1: Introduction to Natural Language Processing

1. What is NLP? History and Evolution
2. Applications of NLP in Real World
3. NLP Pipeline Overview
4. Challenges in Human Language Processing
5. Setting Up Python Environment for NLP (Anaconda, Jupyter, VS Code)

Chapter 2: Python Fundamentals for NLP

1. Python Refresher (Data Types, Functions, OOP Basics)
2. Working with Text Data in Python
3. Regular Expressions for Text Processing
4. File Handling and Text I/O Operations
5. Introduction to NumPy and Pandas for NLP

Chapter 3: Text Preprocessing Techniques

1. Tokenization (Word & Sentence)
2. Lowercasing and Text Normalization
3. Stopword Removal
4. Stemming and Lemmatization
5. Handling Special Characters, Emojis, and Numbers

Chapter 4: Linguistic Foundations

1. Morphology and Lexical Analysis
2. Syntax and Parsing
3. Part-of-Speech (POS) Tagging
4. Named Entity Recognition (NER)
5. Dependency and Constituency Parsing

Chapter 5: Feature Extraction Techniques

1. Bag of Words (BoW)
2. N-grams Model
3. TF-IDF Vectorization
4. Feature Scaling and Normalization
5. Sparse Matrix Representation

Chapter 6: Word Embeddings

1. Introduction to Distributed Representations
2. Word2Vec (CBOW & Skip-gram)
3. GloVe Embeddings
4. FastText Model
5. Visualizing Word Embeddings

Chapter 7: Text Classification

1. Overview of Text Classification Tasks
2. Naive Bayes for Text Classification
3. Logistic Regression for NLP
4. Support Vector Machines (SVM)
5. Model Evaluation Metrics (Accuracy, Precision, Recall, F1)

Chapter 8: Sequence Models

1. Introduction to Sequential Data
2. Recurrent Neural Networks (RNN)
3. Long Short-Term Memory (LSTM)
4. Gated Recurrent Units (GRU)
5. Bidirectional RNNs

Chapter 9: Deep Learning for NLP

1. Introduction to Neural Networks in NLP
2. Convolutional Neural Networks (CNN) for Text
3. Attention Mechanism
4. Encoder-Decoder Architecture
5. Sequence-to-Sequence Models

Chapter 10: Transformers and Large Language Models

1. Transformer Architecture
2. Self-Attention Mechanism
3. BERT and Fine-Tuning Techniques
4. GPT Models and Text Generation
5. Transfer Learning in NLP

Chapter 11: Advanced NLP Applications

1. Sentiment Analysis
2. Topic Modeling (LDA)
3. Machine Translation
4. Question Answering Systems
5. Text Summarization (Extractive & Abstractive)

Chapter 12: NLP in Production

1. Building NLP Pipelines with spaCy
2. Model Deployment (Flask/FastAPI)
3. Working with REST APIs
4. Model Optimization and Scaling
5. Ethics, Bias, and Responsible AI in NLP

Don't miss out on the chance to discover your full potential. Enroll today and open the door to a world of opportunities. Receive an exclusive digital certificate upon completing the course!

This course is designed for aspiring data scientists, software developers, and AI enthusiasts who want to understand how machines interpret human language. It is ideal for beginners with basic Python knowledge, as well as professionals seeking to enhance their skills in text analysis, machine learning, and building intelligent language-based applications.

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