Natural Language Processing and Data Mining: Free Course

MetaCyberGuru Academy

Beginner to advancedEstimated learning effort: about 99 hoursFree, no sign-up requiredPublished by Muhammad AzharCourse version: August 2026
Natural Language Processing and Data Mining free practical course

One course, two connected skills.

Data Mining helps you find useful patterns in structured, transactional, sequential and graph data. Natural Language Processing helps computers work with human language. This free course teaches both through one practical path, because real projects rarely keep them separate.

You will begin with problem framing, data quality and reliable baselines. You will then build classifiers, clustering systems, association rules and anomaly detectors before moving into text processing, linguistic analysis, transformers, retrieval-augmented generation and production monitoring.

Choose a learning track or follow the complete route

If you are new to machine learning, follow the modules in order. If you already understand data preparation and model evaluation, you can enter through the NLP track. The track pages are navigation guides, not separate copies of the course.

Learning track

Data Mining

Build reliable pattern-discovery skills through KDD, model evaluation, clustering, association rules, anomaly detection, graphs and scale.

Open this track

What you will be able to build

Completing a lesson means producing evidence, not merely reading an explanation. By the end, your project folder can contain a data-quality report, a leakage-safe classifier, a customer-segmentation study, a market-basket rule miner, an anomaly investigation, a text classifier, a named-entity pipeline, a document explorer, a hybrid search service and an evaluated RAG application.

  • Frame a useful question before choosing an algorithm.
  • Collect and document data without hiding its origin, limits or licence.
  • Build reproducible Python pipelines that protect the test set.
  • Compare simple baselines with advanced models using appropriate metrics.
  • Explain clusters, rules, predictions and retrieval results to another person.
  • Find failure cases and decide whether a system is safe enough to release.
  • Deploy a small service with monitoring, version information and a rollback plan.

The complete course roadmap

The 16 modules contain 64 substantial lessons. Every fourth lesson is a project checkpoint. The early modules establish the habits that later NLP and LLM work depends on, especially provenance, leakage prevention, error analysis and honest evaluation.

Your progress on this device

Mark lessons complete as you work. Progress is stored only in this browser and the complete course remains available without it.

Module 1 · Data Mining

Data Mining and KDD Foundations

Turn a vague question into a defensible knowledge discovery project, with clear evidence, boundaries and responsible use.

4 lessons, including a practical checkpoint

Module 2 · Data Mining

Python and Data Foundations for Mining

Build a reproducible Python workspace, inspect real datasets and reason about distributions, distance and similarity.

4 lessons, including a practical checkpoint

Module 3 · Data Mining

Data Preparation, ETL and Warehousing

Repair messy records, design useful features and move reliable data into an analytical warehouse.

4 lessons, including a practical checkpoint

Module 5 · Data Mining

Classification and Ensemble Learning

Train interpretable classifiers, compare model families and diagnose the mistakes that headline scores hide.

4 lessons, including a practical checkpoint

Module 6 · Data Mining

Clustering and Dimensionality Reduction

Discover structure without labels, compare geometric assumptions and validate clusters with evidence.

4 lessons, including a practical checkpoint

Module 7 · Data Mining

Pattern, Sequence and Anomaly Mining

Find co-occurrence rules, ordered behaviour and unusual records without mistaking coincidence for knowledge.

4 lessons, including a practical checkpoint

Module 9 · Natural Language Processing

Language Foundations and Text Preprocessing

Learn how computers store language, turn raw documents into defensible tokens, and build preprocessing pipelines that preserve meaning instead of blindly deleting it.

4 lessons, including a practical checkpoint

Module 10 · Natural Language Processing

Linguistic Analysis and Information Extraction

Move beyond word counts by identifying grammar, entities and relationships, then assemble an extraction pipeline whose output can be checked by a human.

4 lessons, including a practical checkpoint

Module 11 · Natural Language Processing

Classical Text Representation and Search

Build strong, explainable text baselines with counts, TF-IDF, n-grams and cosine similarity before reaching for larger models.

4 lessons, including a practical checkpoint

Module 12 · Natural Language Processing

Text Classification, Sentiment, Topics and Clustering

Turn text into decisions with leakage-safe classifiers, honest sentiment analysis, topic exploration and clustering that is inspected rather than merely plotted.

4 lessons, including a practical checkpoint

Module 13 · Natural Language Processing

Embeddings and Neural NLP

Understand what dense representations capture, what they miss, and when sequence models or sentence embeddings justify their additional cost.

4 lessons, including a practical checkpoint

Module 14 · Natural Language Processing

Transformers, Fine-Tuning and NLP Tasks

Trace attention from its core calculation to pretrained pipelines and careful fine-tuning, with evaluation that separates a successful training run from a useful model.

4 lessons, including a practical checkpoint

Module 15 · Natural Language Processing

LLM Applications, RAG, Evaluation and Security

Design language-model applications around contracts, evidence and failure tests, then build a cited retrieval workflow with explicit security boundaries.

4 lessons, including a practical checkpoint

Module 16 · Natural Language Processing

Multilingual NLP, Production, Ethics and Capstone

Finish the course by handling language variation, production monitoring, privacy and bias, then combine NLP and knowledge discovery in a portfolio capstone.

4 lessons, including a practical checkpoint

How the practical work is organised

Python is the primary language because its data and NLP ecosystem makes the ideas visible without unnecessary setup. SQL appears where it is the clearer tool, particularly for data preparation, warehousing and PostgreSQL search. You do not need a paid cloud account. Small datasets and local models are used wherever they can teach the same principle.

Recommended course repository structure
FolderWhat belongs thereWhy it matters
data/raw/Unchanged source files plus licence notesYou can always trace a result back to its source.
data/processed/Outputs created by a documented pipelineCleaning steps remain reproducible.
notebooks/Exploration and short experimentsQuestions can be investigated quickly without hiding production logic in a notebook.
src/Reusable preprocessing, training and evaluation codeThe project can be tested and rerun.
tests/Checks for data contracts and important functionsSilent changes are caught before deployment.
reports/Metrics, error samples, model cards and decisionsA reviewer can understand what worked and what did not.

Recommended setup

The lessons use current Python 3 practices and explain version-sensitive steps. Create one virtual environment for the course, install only the packages required by the module you are studying, and record the exact versions that produced your result. Patch versions change faster than course concepts, so the pages show a course version and link to official documentation instead of claiming that one dependency set will remain current forever.

mkdir nlp-data-mining-course
cd nlp-data-mining-course
python -m venv .venv

# Windows PowerShell
.venv\Scripts\Activate.ps1

# macOS or Linux
source .venv/bin/activate

python -m pip install --upgrade pip
git init

Do not put private, employer or client data into course projects. Use public datasets with clear terms, synthetic examples or data you are authorised to process. A technically impressive model is still a failed project if its data use is unsafe or unlawful.

How to study without getting stuck

  1. Run the smallest example exactly as written.
  2. Predict what one change will do before changing the code.
  3. Record the output and one failure case.
  4. Explain the result in plain language.
  5. Complete the exercise without copying the worked solution.
  6. Keep the project artefact and a short note about what you would improve.

When code fails, read the final error message first. Check the file path, data type, shape, missing values and package version before changing the model. Guessing at random usually creates a second problem and hides the first.

What this course does not promise

This is a self-study course, not a university degree, professional certification or employment guarantee. It can help you build knowledge and portfolio evidence. It cannot verify that every learner performed the work independently. Your strongest proof is a reproducible project that another person can run, question and inspect.

Assessment and capstone

The final assessment asks you to defend choices, diagnose failures and design an end-to-end system. The capstone combines a documented dataset, a simple baseline, a justified advanced method, evaluation, error analysis, safety controls and a deployment plan. You may build a customer-feedback intelligence system, a document discovery platform or another project of comparable depth.

Read the capstone and assessment requirements

Related Academy paths

If basic Python still slows you down, complete the Python Automation for Beginners course before the modelling projects. The Full-Stack Developer course is the useful next step when you want to turn a model or search pipeline into a complete web application. You can also return to the Academy home to compare every public learning path.

How this course was built and checked

This is original MetaCyberGuru course material. The two supplied curriculum documents were used as topic guides, then expanded with official documentation, primary research and new examples written for this learning path. Their wording and diagrams were not republished. Runnable examples are executed where the local test environment supports their dependencies. Other examples receive syntax and structural checks, with any testing limitation stated plainly.

The course is published by Muhammad Azhar, a software engineer and technical educator. It is not accredited, and it does not claim independent university review. Source links let you verify technical claims and version-sensitive instructions yourself.

Technical sources and freshness

Version-sensitive lessons rely on official documentation and primary research, including Python, pandas, scikit-learn, spaCy, NLTK, PyTorch, Hugging Face, PostgreSQL, pgvector, NIST and OWASP. Each technical page links to the sources used for its claims. The course should be reviewed sooner when a major library release changes an API, rather than receiving an artificial date change.

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