Data Science and Machine Learning
Data Analysis with Python and Pandas
Analyze data like a pro. Use Python, Pandas, and NumPy to clean, transform, and explore datasets and extract meaningful…
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Data Science and Machine Learning
Natural Language Processing Basics
Prepare, represent and classify real-world text with Python, from TF-IDF baselines to pre-trained and fine-tuned transformer models, and evaluate the results honestly.
About this course
Teach machines to understand text. Learn tokenization, embeddings, sentiment analysis, and modern NLP techniques with real datasets.
What you will learn
- Build a text preprocessing pipeline that cleans, masks personal data, tokenises and normalises text appropriately for the task
- Represent documents with bag of words, TF-IDF and embeddings, and measure document similarity
- Train and interpret text classifiers, sentiment models and topic models with scikit-learn
- Extract names, places, dates and amounts from text with spaCy and add rules for local formats
- Use and fine-tune pre-trained transformer models from the Hugging Face ecosystem
- Evaluate NLP models with per-class metrics, slice analysis and error analysis, and report results honestly
Curriculum
Module 1NLP Foundations4 lessons
- 1What Computers Do with Language Free preview
- 2Text Preprocessing Pipeline
- 3Tokenisation and Normalisation
- 4Bag of Words and TF-IDF
- QModule quiz (4 questions)
Module 2Classic NLP Tasks4 lessons
- 5Text Classification
- 6Sentiment Analysis
- 7Named Entity Recognition
- 8Topic Modelling
- QModule quiz (4 questions)
Module 3Modern NLP4 lessons
- 9Word Embeddings Intuition
- 10Transformers and Attention (Conceptual)
- 11Using Pre-Trained Models
- 12Fine-Tuning Basics
- QModule quiz (4 questions)
Module 4NLP in Practice3 lessons
- 13Building a Text Classifier End-to-End
- 14Evaluation for NLP
- 15Course Project: Sentiment Analyser
- QModule quiz (4 questions)
PFinal project: Sentiment Analyser for Customer Feedback
Who this course is for
- Learners with basic Python who want to work with text data
- Analysts and developers handling customer messages, reviews or documents
- Data science beginners ready to move from numbers to language
Requirements
- A computer with Python 3 and Jupyter (for example via Anaconda), or a Google account to use Google Colab, which also provides free GPU time for fine-tuning
- Basic Python, including lists, functions and simple pandas operations
- Some familiarity with training and testing a model is helpful but not required
What is included
Written lessons
15 lessons, each with a practical exercise and key points to remember.
Module quizzes
A short quiz at the end of each module. Pass mark 70%, and you can retake it.
Final project
Sentiment Analyser for Customer Feedback. A practical brief with steps and a way to check your own work.
Certificate
A certificate of completion from EMFAD Courses with a unique code anyone can verify.
How the certificate works
Complete every lesson and pass every module quiz with 70% or more. Your certificate then appears in your learner area, ready to print or save as PDF. It shows your name, the course title, the completion date and a unique code. It is a certificate of completion from EMFAD Courses, not an accredited qualification.
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