Natural Language Processing (NLP) with Python Training

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Assure Success

About the course

Why take Natural Language Processing (NLP) with Python Training?

Natural Language Processing (NLP) with Python teaches you how to turn unstructured text into something a model can learn from. Reviews, emails, support tickets, resumes and social posts all hold useful signals, and this course shows you how to extract them step by step.

You start with the classical pipeline: cleaning, tokenisation, stop words, stemming and lemmatisation, bag-of-words and TF-IDF, then train classifiers for sentiment and spam. Next come word embeddings, sequence ideas and named entity recognition with spaCy. The course then introduces transformer-based models and the Hugging Face ecosystem at a practical level, and shows when a pretrained model or an LLM API is a better choice than training your own.

There is no external exam. You complete a text-analytics project and receive an Assure Success course-completion certificate. For deeper work with transformers, see NLP with Transformers (Hugging Face).

Who should attend

● Data analysts and data scientists who work with text data
● Software developers building search, chatbot or document-processing features
● Graduates in computer science, linguistics or data science exploring NLP careers
● Customer-insight and market-research professionals analysing reviews and survey comments
● Learners who have completed Python and basic machine learning courses

Prerequisites

● Working knowledge of Python and pandas
● Basic machine learning concepts (train-test split, classification metrics)
● No linguistics background needed

What this certification does for you

Classical plus modern

You understand the fundamentals and also the transformer-era tools, so you can choose sensibly.

Real text data

Projects use reviews, tweets-style posts and support-ticket style data.

Cost-aware thinking

We compare a small classifier, a fine-tuned model and an LLM API on accuracy, cost and speed.

Portfolio piece

A sentiment or document-analysis project you can demonstrate.

Curriculum

10 modules, step by step

MODULE 1

Introduction to NLP

Understand tasks and the NLP pipeline.

› Common NLP applications
› Text data sources and collection
› Python text handling and regular expressions
› NLTK and spaCy setup

MODULE 2

Text Preprocessing

Prepare text for modelling.

› Tokenisation, stop words
› Stemming vs lemmatisation
› Handling emojis, URLs and spelling noise
› Basics of Unicode and Indian-language text

MODULE 3

Text Representation

Convert text to numbers.

› Bag-of-words and n-grams
› TF-IDF
› Sparse matrices
› Similarity measures

MODULE 4

Text Classification

Build sentiment and spam classifiers.

› Naive Bayes, logistic regression, linear SVM
› Pipelines with scikit-learn
› Metrics and error analysis
› Handling class imbalance

MODULE 5

Linguistic Features and Information Extraction

Extract structure from text.

› Part-of-speech tagging
› Named entity recognition
› Dependency parsing basics
› Rule-based matching with spaCy

MODULE 6

Topic Modelling and Clustering

Find themes in large text sets.

› LDA
› Embedding-based clustering
› Evaluating topics
› Visualising topics

MODULE 7

Word Embeddings and Neural Text Models

Use dense representations.

› Word2Vec and GloVe ideas
› Sentence embeddings
› Semantic search with vector similarity
› Simple neural classifiers for text

MODULE 8

Transformers and Pretrained Models

Use modern models.

› Attention and transformers at a conceptual level
› Hugging Face pipelines
› Summarisation, question answering, zero-shot classification
› Limits, bias and hallucination

MODULE 9

LLM APIs for Text Tasks

Know when to call an LLM.

› Prompting for extraction and classification
› Structured outputs
› Cost, latency and privacy trade-offs
› Evaluating LLM output against a labelled sample

MODULE 10

Project

Deliver a text-analytics solution.

› Pick a dataset and task
› Build baseline and improved model
› Report results
› Presentation and feedback

What you get

Why take Natural Language Processing (NLP) with Python Training?

Who should attend

Prerequisites

What this certification does for you

10 modules, step by step

Certified by Assure Success

Your path to certification

Skills you will gain

Where this course can take you

Why learn with Assure Success