Learn to use Machine Learning, Spacy, NLTK, SciKit-Learn, Deep Learning, and more to conduct Natural Language Processing
InstructorJose PortillaThe curriculum covers a wide range of essential NLP tasks including tokenization, part-of-speech tagging, and named entity recognition using Spacy.
One sampled review suggests that instructional delivery is noted for being well-articulated, making fundamental NLP concepts accessible to learners.
Editorial course preview
This view highlights a concrete, legible example from the course presentation.
This graphic illustrates the professional credentialing aspect of the course, showing a certificate of completion for NLP suitable for sharing on platforms like LinkedIn.
Multiple signals highlight deprecated Python libraries and version mismatches, specifically regarding PyPDF2 and Spacy 3.x compatibility.
The deep learning and neural network sections lack sufficient detail and mathematical depth for complex applications.
The curriculum aligns well with the target of Python developers interested in NLP basics.
