Natural Language Processing: Building AI That Understands Text

Natural Language Processing explores how machines analyse, interpret, and generate human language using Python and machine learning. The course examines essential NLP workflows, from text cleaning, tokenisation, and feature extraction to sentiment analysis, text classification, named entity recognition, and language-model applications. It is designed for learners who want to work with real-world text data and build intelligent systems for search, chatbots, document analysis, and automated language tasks.

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Nishant Kaushik

SAP MM

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manideepak

Manager

Career switch

Hari Shankar Jha

Senior System Admin

Upskilled

alok kumar singh

sap hcm

Placed

Jeevak

Consultant

Upskilled

Abhijit S. Getme

Consultant Engineer

Placed

Kartik Singh

SAP SD

Upskilled

Hemant Khainar

SAP MM Consultant

Placed

Neeraj Khanna

SAP MM

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Burna Vinay Kumar

SAP SD Consultant

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Shubham

SAP ABAP Consultant

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Shashikant

Structure Engineer

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Khushbu

Sap hcm

Curriculum

Natural Language Processing, NLP, Python, ZeblearnIndia, Machine Learning Course Syllabus Structure

Natural Language Processing curriculum covers tokenization, embeddings, and transformer architectures. Practical labs use Python, NLTK, spaCy, and Hugging Face libraries.

Where to Get the Code

How to Succeed in This Course

Temporary 403 Errors

Vector Models & Text Preprocessing Intro

Basic Definitions for NLP

What is a Vector?

Bag of Words

Count Vectorizer (Theory)

Tokenization

Stopwords

Stemming and Lemmatization

Stemming and Lemmatization Demo

Count Vectorizer (Code)

Vector Similarity

TF-IDF (Theory)

(Interactive) Recommender Exercise Prompt

TF-IDF (Code)

Word-to-Index Mapping

How to Build TF-IDF From Scratch

Neural Word Embeddings

Neural Word Embeddings Demo

Vector Models & Text Preprocessing Summary

Text Summarization Preview

How To Do NLP In Other Languages

Markov Models Section Introduction

The Markov Property

The Markov Model

Probability Smoothing and Log-Probabilities

Building a Text Classifier (Theory)

Building a Text Classifier (Exercise Prompt)

Building a Text Classifier

Language Model (Theory)

Language Model (Exercise Prompt)

Language Model

Markov Models Section Summary

Article Spinning - Problem Description

Article Spinning - N-Gram Approach

Article Spinner Exercise Prompt

Article Spinner in Python

Case Study: Article Spinning Gone Wrong

Spam Detection - Problem Description

Naive Bayes Intuition

Spam Detection - Exercise Prompt

Aside: Class Imbalance, ROC, AUC, and F1 Score

Spam Detection in Python

Sentiment Analysis - Problem Description

Logistic Regression Intuition (pt 1)

Multiclass Logistic Regression (pt 2)

Logistic Regression Training and Interpretation (pt 3)

Sentiment Analysis - Exercise Prompt

Sentiment Analysis in Python

Text Summarization Section Introduction

Text Summarization Using Vectors

Text Summarization Exercise Prompt

Text Summarization in Python

TextRank Intuition

TextRank - How It Really Works (Advanced)

TextRank Exercise Prompt (Advanced)

TextRank in Python (Advanced)

Text Summarization in Python - The Easy Way (Beginner)

Text Summarization Section Summary

Topic Modeling Section Introduction

Latent Dirichlet Allocation (LDA) - Essentials

LDA - Code Preparation

LDA - Maybe Useful Picture (Optional)

Latent Dirichlet Allocation (LDA) - Intuition (Advanced)

Topic Modeling with Latent Dirichlet Allocation (LDA) in Python

Non-Negative Matrix Factorization (NMF) Intuition

Topic Modeling with Non-Negative Matrix Factorization (NMF) in Python

Topic Modeling Section Summary

LSA / LSI Section Introduction

SVD (Singular Value Decomposition) Intuition

LSA / LSI: Applying SVD to NLP

Latent Semantic Analysis / Latent Semantic Indexing in Python

LSA / LSI Exercises

The Neuron - Section Introduction

Fitting a Line

Classification Code Preparation

Text Classification in Tensorflow

The Neuron

How does a model learn?

The Neuron - Section Summary

ANN - Section Introduction

Forward Propagation

The Geometrical Picture

Activation Functions

Multiclass Classification

ANN Code Preparation

Text Classification ANN in Tensorflow

Text Preprocessing Code Preparation

Text Preprocessing in Tensorflow

Embeddings

CBOW (Advanced)

CBOW Exercise Prompt

CBOW in Tensorflow (Advanced)

ANN - Section Summary

Aside: How to Choose Hyperparameters (Optional)

CNN - Section Introduction

What is Convolution?

What is Convolution? (Pattern Matching)

What is Convolution? (Weight Sharing)

Convolution on Color Images

CNN Architecture

CNNs for Text

Convolutional Neural Network for NLP in Tensorflow

CNN - Section Summary

RNN - Section Introduction

Simple RNN / Elman Unit (pt 1)

Simple RNN / Elman Unit (pt 2)

RNN Code Preparation

RNNs: Paying Attention to Shapes

GRU and LSTM (pt 1)

GRU and LSTM (pt 2)

RNN for Text Classification in Tensorflow

Parts-of-Speech (POS) Tagging in Tensorflow

Named Entity Recognition (NER) in Tensorflow

Exercise: Return to CNNs (Advanced)

RNN - Section Summary

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SOLD OUT	30 Aug 2026	Weekend	SAT - SUN (08 Weeks)	18:00 - 20:00
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FAQ

Frequently Asked Questions

Q: What is Natural Language Processing used for in real‑world applications? A: Natural Language Processing enables computers to interpret, generate, and act upon human language in tasks such as chatbots, sentiment tracking, and document summarization. Q: Which programming language is most commonly paired with NLP libraries? A: Python is the dominant language for NLP because of libraries like NLTK, spaCy, and Hugging Face Transformers. Q: How does NLP relate to Machine Learning? A: NLP leverages Machine Learning models to learn patterns from text data, allowing automated language understanding and generation.

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Natural Language Processing: Building AI That Understands Text

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