NLP with Python for Machine Learning Essential Training

NLP with Python for Machine Learning Essential Training is a tightly-scoped, hands-on intro to classical text-classification by Derek Jedamski (a senior data scientist who now leads Copilot prompt engineering at GitHub), and it is worth taking if your goal is to learn the end-to-end NLP-to-machine-learning pipeline with NLTK and scikit-learn. Across roughly four hours it walks you from raw text through tokenization, stemming/lemmatization, count and TF-IDF vectorization, feature engineering, and finally training and tuning Random Forest and Gradient Boosting classifiers on an SMS spam dataset. The course holds a strong 4.8/5 from about 1,406 ratings on the platform (per Class Central), and learners consistently praise its clarity and practical, code-along format. The major caveat is its age: the material was originally released in March 2018 and teaches only classical machine-learning NLP -- there is no coverage of word embeddings, transformers, or large language models, and the exercise code uses scikit-learn APIs old enough that the community exercise repo had to patch them to run on modern versions. Treat it as a solid foundations course for the preprocessing-plus-bag-of-words pipeline, not as a current guide to state-of-the-art NLP.

It is an efficient, well-rated foundations course for the classical NLP-to-ML pipeline (NLTK + scikit-learn, TF-IDF, tree-based classifiers), but its 2018 content covers no embeddings, transformers, or LLMs, so it is only the right pick if you specifically want classical text-classification fundamentals and already accept the LinkedIn Learning subscription.

Best for: Learners who already know basic Python and want a fast, practical walkthrough of the full classical NLP machine-learning pipeline -- text cleaning, tokenization, stemming/lemmatization, vectorization (count, n-gram, TF-IDF), feature engineering, and training/tuning tree-based classifiers. It suits analysts, data-science beginners, and software engineers who want to build and evaluate a working spam-style text classifier end to end, and anyone who already has LinkedIn Learning (or LinkedIn Premium) access and wants a low-commitment ~4-hour course with a shareable certificate.

Skip if: Anyone who needs modern NLP -- word embeddings (word2vec/GloVe), deep learning, transformers, or large language models -- since none of that is covered. Complete programming beginners will struggle because Python fundamentals are assumed and not taught. It is also a poor fit for people who want to work with large or varied datasets (the entire course uses one small SMS dataset) or who object to paying a recurring subscription for content first published in 2018.