This NLP Basics Course is designed to help beginners understand how Natural Language Processing works step by step, starting from raw text and ending with modern semantic AI systems.
You will begin with foundational text preprocessing techniques such as tokenization, normalization, and regular expressions (Regex) to clean and structure raw text data. The course then moves into core linguistic representations including n-grams, part-of-speech tagging (POS tagging), and byte-pair encoding (BPE).
Next, you will learn how machines convert text into numerical representations using Bag of Words (BoW) and TF-IDF, which are essential for traditional NLP and machine learning models. After that, the course introduces classical machine learning algorithms for text classification, including Naive Bayes and Logistic Regression.
You will also explore important NLP concepts such as minimum edit distance, optimization methods, and how they are used in spell checking and text similarity.
Finally, the course introduces modern NLP ideas like semantic retrieval and visual grounding, showing how today’s AI systems go beyond simple keyword search to understand meaning and context.
By the end of this course, learners will have a strong foundation in both traditional NLP techniques and the basics of modern semantic AI systems.
