Natural Language Processing with Python Training

RexGalaxy Academy's Natural Language Processing with Python Training is a practical, project-based program designed for learners who want to work with text data, language patterns and AI-powered language applications using Python. The course is placed under the Python category because it focuses on Python-based NLP workflows, libraries, text preprocessing, machine learning models and real-world language projects.

This training starts with NLP fundamentals and gradually moves into text cleaning, tokenization, regular expressions, NLTK, spaCy, feature extraction, sentiment analysis, text classification, topic modelling, word embeddings, sequence modelling awareness and transformer-based NLP concepts. Learners work with real text datasets such as reviews, social media posts, support tickets, documents, messages and customer feedback.

The course focuses on hands-on learning instead of only theory. Students clean raw text, prepare features, build models, evaluate results, visualize insights and document NLP projects professionally. The training also explains practical challenges in language data such as noise, spelling variations, ambiguity, context, bias and responsible AI usage.

Students should be able to:

• Use Python for text cleaning, preprocessing, tokenization and NLP workflow development.

• Work with NLTK, spaCy and common NLP libraries for real text processing tasks.

• Build sentiment analysis, text classification and topic modelling projects.

• Understand TF-IDF, bag-of-words, embeddings and transformer awareness at a practical level.

• Evaluate NLP models using suitable metrics and interpret results clearly.

• Build portfolio-ready NLP projects with notebooks, datasets, model results and explanations.

Start with NLP fundamentals and understand how machines process human language.

• Understand what Natural Language Processing is and why it is important in modern AI applications.

• Learn common NLP use cases such as chatbots, sentiment analysis, spam detection, document search and text summarization awareness.

• Understand the difference between structured data and unstructured text data.

• Learn the NLP workflow: collect text, clean text, tokenize, transform, train models, evaluate and interpret results.

• Understand common text sources such as reviews, emails, support tickets, social media posts, articles and documents.

• Learn challenges in language data such as ambiguity, slang, spelling mistakes, sarcasm, context and multiple languages.

• Understand rule-based NLP, machine learning based NLP and deep learning based NLP at a beginner level.

• Practice reading text datasets and identifying useful labels, categories and business questions.

• Learn how NLP projects are documented using notebooks, examples, metrics and observations.

• Outcome: build a strong foundation for practical Python-based NLP projects.

Refresh Python skills needed for handling text, files, datasets and NLP libraries.

• Review Python variables, strings, lists, dictionaries, functions, loops and conditions.

• Work deeply with string operations such as lowercasing, splitting, joining, replacing and searching text.

• Understand list comprehensions and reusable helper functions for text preprocessing.

• Read text files, CSV files and dataset columns containing user-generated text.

• Use Pandas to load text datasets, inspect columns, filter records and manage labels.

• Handle missing values, duplicate rows and incorrect text formats in datasets.

• Use notebooks for step-by-step NLP experiments, markdown explanations and output review.

• Learn how to install and import libraries such as NLTK, spaCy, scikit-learn and visualization tools.

• Practice mini tasks such as word counting, sentence counting, keyword search and text summaries.

• Outcome: use Python confidently for text data preparation and NLP workflows.

Clean noisy text data and use regular expressions for pattern-based processing.

• Understand why raw text must be cleaned before model training or analysis.

• Remove extra spaces, punctuation noise, special symbols, HTML tags, URLs and unnecessary characters.

• Convert text to lowercase and standardize inconsistent formats.

• Learn regular expression basics for matching emails, numbers, hashtags, mentions, dates and patterns.

• Use regex for extracting useful information from documents, messages and user inputs.

• Handle emojis, contractions, spelling variations and domain-specific noise awareness.

• Remove duplicate text records and identify empty or low-quality text entries.

• Create reusable cleaning functions for repeatable preprocessing.

• Practice cleaning reviews, tweets, support tickets and product feedback datasets.

• Outcome: transform messy raw text into clean and analysis-ready text data.

Learn the most important preprocessing steps used in NLP pipelines.

• Understand tokenization and how text is split into words, sentences or meaningful units.

• Learn stopwords and when removing them is helpful or harmful.

• Understand stemming and how words are reduced to rough root forms.

• Learn lemmatization and why it gives more meaningful base word forms than stemming.

• Practice removing punctuation, numbers and unwanted tokens based on project requirements.

• Understand vocabulary creation and token frequency analysis.

• Learn how preprocessing choices affect sentiment analysis, classification and search results.

• Compare raw text, cleaned text, tokenized text and lemmatized text outputs.

• Build a preprocessing pipeline that can be reused across NLP projects.

• Outcome: prepare text correctly for feature extraction and model training.

Use NLTK for foundational NLP processing, language analysis and educational NLP workflows.

• Understand NLTK and why it is useful for learning NLP concepts clearly.

• Work with NLTK tokenizers for words and sentences.

• Use stopword lists, stemming tools and lemmatization tools from NLTK.

• Perform part-of-speech tagging awareness to understand grammatical roles of words.

• Explore frequency distributions, word counts and common term analysis.

• Use NLTK corpora awareness and sample datasets for practice.

• Build simple text preprocessing pipelines using NLTK functions.

• Practice keyword extraction and basic language pattern analysis.

• Understand limitations of rule-based and classic NLP approaches.

• Outcome: apply NLTK for core NLP preprocessing and language analysis tasks.

Use spaCy for faster, production-friendly NLP workflows and linguistic processing.

• Understand spaCy and how it differs from NLTK in practical NLP workflows.

• Load spaCy language models and process text documents efficiently.

• Perform tokenization, lemmatization, sentence segmentation and part-of-speech tagging.

• Understand named entity recognition for extracting people, places, organizations, dates and more.

• Use dependency parsing awareness to understand relationships between words.

• Process batches of text and inspect tokens, lemmas, entities and sentence boundaries.

• Build entity extraction examples for resumes, invoices, articles or customer messages.

• Learn how spaCy pipelines are organized and how components process text.

• Practice comparing NLTK and spaCy outputs for the same text.

• Outcome: use spaCy for practical NLP preprocessing and entity extraction workflows.

Convert text into numerical features that machine learning models can understand.

• Understand why text must be converted into numbers before model training.

• Learn bag-of-words representation and how word counts become feature vectors.

• Understand vocabulary size, sparse matrices and limitations of count-based features.

• Learn n-grams and how word combinations capture more context than single words.

• Use TF-IDF to measure term importance across documents.

• Build feature matrices using scikit-learn vectorizers.

• Understand how preprocessing affects vocabulary and model quality.

• Compare count vectorization and TF-IDF for classification problems.

• Practice feature extraction on reviews, emails and support ticket datasets.

• Outcome: convert text into useful machine learning features for NLP models.

Build sentiment analysis projects to classify opinions and emotions in text.

• Understand sentiment analysis and how it is used for reviews, feedback and social media monitoring.

• Learn positive, negative and neutral sentiment classification workflows.

• Prepare labelled sentiment datasets with cleaning, preprocessing and feature extraction.

• Train baseline models using TF-IDF features and machine learning classifiers.

• Evaluate sentiment models using accuracy, precision, recall, F1-score and confusion matrix.

• Inspect wrong predictions to understand ambiguity, sarcasm and unclear text.

• Compare rule-based sentiment awareness with machine learning based sentiment classification.

• Create charts showing sentiment distribution and category-level sentiment insights.

• Build a mini project using product reviews, movie reviews or customer feedback.

• Outcome: create practical sentiment analysis models and explain their results.

Train and evaluate text classification models for real business and AI use cases.

• Understand text classification use cases such as spam detection, ticket routing, topic labeling and complaint categorization.

• Prepare datasets with text columns and target labels.

• Build pipelines using preprocessing, vectorization and classification algorithms.

• Train models such as Naive Bayes, Logistic Regression or similar classifiers for text tasks.

• Evaluate models using confusion matrix, accuracy, precision, recall and F1-score.

• Understand class imbalance and why some labels may perform worse than others.

• Tune preprocessing and vectorization settings to improve model quality.

• Save model workflow awareness and document input-output behavior clearly.

• Practice projects such as spam classifier, news category classifier or support ticket classifier.

• Outcome: build supervised NLP classification projects with proper evaluation.

Discover hidden themes and insights from large text datasets using unsupervised NLP methods.

• Understand topic modelling and how it helps summarize large collections of documents.

• Learn text clustering awareness for grouping similar documents or messages.

• Prepare text for topic modelling using cleaning, stopword removal and vectorization.

• Understand LDA topic modelling awareness and how topics are interpreted.

• Extract top words from topics and assign meaningful topic names.

• Use visualization and summary tables to explain discovered themes.

• Apply topic analysis to reviews, complaints, articles, survey responses or support tickets.

• Understand limitations of topic modelling and the need for human interpretation.

• Practice turning text clusters/topics into business insights and recommendations.

• Outcome: analyze large text datasets and discover meaningful themes.

Understand modern NLP concepts including embeddings, sequence models and transformer awareness.

• Learn word embeddings and why dense vector representations capture more meaning than simple counts.

• Understand Word2Vec, GloVe and embedding awareness at a conceptual level.

• Learn sequence models such as RNN and LSTM awareness for ordered text data.

• Understand limitations of classic NLP and why transformers became important.

• Learn transformer concepts such as attention, contextual embeddings and pretrained language models at a beginner level.

• Understand BERT/GPT-style model awareness without going too deep into advanced math.

• Discuss how modern NLP powers chatbots, search, summarization, translation and question answering.

• Understand responsible AI concerns such as bias, hallucination, privacy and misuse of language models.

• Explore how classic NLP skills connect with modern GenAI and LLM workflows.

• Outcome: understand the evolution from classic NLP to modern transformer-based NLP.

Complete NLP projects and prepare for interviews, portfolios and AI learning growth.

• Build a sentiment analysis project with cleaning, feature extraction, model training and evaluation.

• Build a text classification project such as spam detection, ticket routing or news category prediction.

• Build an entity extraction or topic modelling project using real text datasets.

• Document each project with problem statement, dataset source, preprocessing, model choice, metrics and results.

• Create visual outputs such as word frequency charts, sentiment charts, confusion matrices and topic summaries.

• Prepare GitHub repositories with notebooks, requirements, README files, screenshots and project summaries.

• Practice interview questions on tokenization, stemming, lemmatization, TF-IDF, embeddings, classification and transformers.

• Learn how to explain model limitations, wrong predictions and improvement ideas.

• Prepare resume bullet points for Python, NLP, NLTK, spaCy, sentiment analysis and text classification projects.

• Outcome: build a practical NLP portfolio and present language AI projects confidently.