Description

Welcome to KGP Talkie’s Natural Language Processing (NLP) course. It’s designed to offer you an entire understanding of Textual content Processing and Mining with the usage of State-of-the-Artwork NLP algorithms in Python.

We’ll be taught Spacy in element and we will even discover the makes use of of NLP in actual life. This course covers the fundamentals of NLP to advance subjects like word2vec, GloVe, Deep Studying for NLP like CNN, ANN, and LSTM. I will even present you how one can optimize your ML code by utilizing numerous 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 least 12 NLP Tasks are lined in this course. You’ll be taught numerous methods of fixing edge-cutting NLP issues.

It’s best 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 degree 0 to the superior degree.

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’ve got prior expertise you possibly can skip these sections. The true sport of NLP will begin with Spacy Introduction the place I’ll take you thru numerous steps of NLP preprocessing. We shall be utilizing Spacy and NLTK principally for the textual content knowledge preprocessing.

Within the subsequent part, we’ll study working with Information for storing and loading the textual content knowledge. This part is the muse 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 bundle on preprocessing. It should assist us to enhance our code writing expertise. We will reuse our code systemwide with out writing codes for preprocessing each time. This part is an important part.

Then, we’ll begin the Machine studying principle part and a walkthrough of the Scikit-Study Python bundle 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 shall be additionally displaying you numerous forms 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-Study bundle.

Thereafter we’ll be taught in regards to the machine studying mannequin deployment. We will even be taught numerous different necessary instruments like word2vec, GloVe, Deep Studying, CNN, LSTM, RNN, and so forth.

On the finish of this lesson, you’ll be taught all the things which it is advisable to remedy your individual NLP drawback.

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