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Natural Language Processing Fundamentals

Chapter 1: NLP Concepts and Advanced Text Processing

The Natural Language Processing Pipeline

Advanced Tokenization Methods

Stemming and Lemmatization Compared

Handling Noise in Text Data

Advanced Stop Word Customization

Text Normalization Techniques

Hands-on Practical: Building Preprocessing Pipelines

Quiz for Chapter 1

Chapter 2: Feature Engineering for Text Representation

From Bag-of-Words to TF-IDF

Calculating TF-IDF Scores

Using N-grams to Capture Context

Introduction to Feature Hashing

Dimensionality Reduction for Text Features

Comparing Different Text Representation Methods

Hands-on Practical: Generating Text Features

Quiz for Chapter 2

Chapter 3: Supervised Learning for Text Classification

Classification Algorithms Review

Applying Classifiers to Text Data

Model Evaluation Metrics for Classification

Cross-Validation Strategies

Hyperparameter Tuning for Text Models

Addressing Imbalanced Datasets

Practice: Building a Text Classifier

Quiz for Chapter 3

Chapter 4: Understanding Sequential Data with Embeddings

Limitations of Frequency-Based Models

Introduction to Distributional Semantics

Word Embedding Concepts

Word2Vec: CBOW and Skip-gram Architectures

GloVe: Global Vectors for Word Representation

Visualizing Word Embeddings

Using Pre-trained Word Embedding Models

Hands-on Practical: Working with Word Embeddings

Quiz for Chapter 4

Chapter 5: Introduction to Sequence Models for NLP

The Need for Sequence Awareness

Recurrent Neural Network (RNN) Basics

Understanding the Vanishing Gradient Problem

Long Short-Term Memory (LSTM) Networks

Gated Recurrent Units (GRUs)

Applying Sequence Models to Text

Hands-on Practical: Building a Simple Sequence Model

Quiz for Chapter 5

Natural Language Processing Fundamentals

Prerequisites Python & basic ML

Level:

Intermediate

Text Preprocessing
Apply advanced text cleaning and normalization techniques suitable for various NLP tasks.
Feature Engineering
Implement and understand feature extraction methods like TF-IDF and N-grams for text representation.
Text Classification
Build, train, and evaluate machine learning models for text classification problems.
Word Embeddings
Understand the concepts behind distributional semantics and apply techniques like Word2Vec and GloVe.
Sequence Modeling Basics
Grasp the fundamentals of sequence modeling using RNNs and LSTMs for text data.

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