Welcome to KGP Talkie’s Natural Language Processing (NLP) course. It’s designed to present you an entire understanding of Textual content Processing and Mining with using State-of-the-Artwork NLP algorithms in Python.
We’ll be taught Spacy in element and we may also discover the makes use of of NLP in actual life. This course covers the fundamentals of NLP to advance matters like word2vec, GloVe, Deep Studying for NLP like CNN, ANN, and LSTM. I may also present you how one can optimize your ML code by utilizing varied instruments of sklean in python. On the finish a part of this course, you’ll learn to generate poetry by utilizing LSTM. Multi-Label and Multi-class classification is defined. At the very least 12 NLP Initiatives are lined in this course. You’ll be taught varied methods of fixing edge-cutting NLP issues.
You need to have an introductory information of Python and Machine Studying earlier than enrolling in this course in any other case please don’t enroll in this course.
On this course, we’ll begin from stage 0 to the superior stage.
We’ll begin with fundamentals like what’s machine studying and the way it works. Thereafter I’ll take you to Python, Numpy, and Pandas crash course. When you have prior expertise you’ll be able to skip these sections. The actual recreation of NLP will begin with Spacy Introduction the place I’ll take you thru varied steps of NLP preprocessing. We will likely be utilizing Spacy and NLTK largely for the textual content knowledge preprocessing.
Within the subsequent part, we’ll find out about working with Recordsdata for storing and loading the textual content knowledge. This part is the inspiration of one other part on Full Textual content Preprocessing. I’ll present you some ways of textual content preprocessing utilizing Spacy and Common Expressions. Lastly, I’ll present you how one can create your individual python package deal on preprocessing. It’ll assist us to enhance our code writing expertise. We can reuse our code systemwide with out writing codes for preprocessing each time. This part is crucial part.
Then, we’ll begin the Machine studying idea part and a walkthrough of the Scikit-Be taught Python package deal the place we’ll learn to write clear ML code. Thereafter, we’ll develop our first textual content classifier for SPAM and HAM message classification. I will likely be additionally exhibiting you varied sorts of phrase embeddings used in NLP like Bag of Phrases, Time period Frequency, IDF, and TF-IDF. I’ll present you how one can estimate these options from scratch in addition to with the assistance of the Scikit-Be taught package deal.
Thereafter we’ll be taught concerning the machine studying mannequin deployment. We may also be taught varied different essential instruments like word2vec, GloVe, Deep Studying, CNN, LSTM, RNN, and many others.
On the finish of this lesson, you’ll be taught all the things which you must remedy your individual NLP drawback.