U&P AI - Natural Language Processing (NLP) with Python

Natural Language Processing has always been a cornerstone of AI theory (AI). As AI becomes more widely used, methods to automate complex jobs are being developed.

By completing this U&P AI - natural language processing (NLP) with Python course, you will get more knowledge along with practical tips and advice, which will help you to learn every aspect of U&P AI - natural language processing (NLP) with Python course. This U&P AI - natural language processing (NLP) with python course gives you the courage to pursue a career as a python programmer or careers related to U&P AI.

This U&P AI - natural language processing (NLP) with python course is designed with many relevant video classes, PDFs, and exercises. So, after completing this U&P AI - natural language processing (NLP) with python course, you will be completely ready with all the requirements to be an AI specialist in today’s job market.

You will have the best guidelines given by our expert trainers who are experienced in U&P AI. Under the supervision of these trainers along with provided video classes and PDFs, you can unleash your dealing with corpus and wordnet skills to the top and have a strong position in the job market

What will you learn from this course:

• Topic of natural language text: What you need to know
• Creating your vocab for any NLP model: Tailoring your approach to maximize impact
• Mastering in using Google Colab.
• Building your Vocabulary: To save time and stress, make a daily, weekly, and long-term work and goal plan.

Program content:

• Section 1 - Getting an Idea of NLP and Its Applications

• Lecture 1 Note!

• Lecture 2 Introduction to NLP

• Lecture 3 By The End Of This Section

• Lecture 4 Installation

• Lecture 5 Tips

• Lecture 6 U - Tokenization Data

• Lecture 7 P - Tokenization

• Lecture 8 U - Stemming

• Lecture 9 P - Stemming

• Lecture 10 U - Lemmatization

• Lecture 11 P - Lemmatization

• Lecture 12 U - Chunks

• Lecture 13 P - Chunks

• Lecture 14 U - Bag Of Words

• Lecture 15 P - Bag Of Words

• Lecture 16 U - Category Predictor

• Lecture 17 P - Category Predictor

• Lecture 18 U - Gender Identifier

• Lecture 19 P - Gender Identifier

• Lecture 20 U - Sentiment Analyzer

• Lecture 21 P - Sentiment Analyzer

• Lecture 22 U - Topic Modeling

• Lecture 23 P - Topic Modeling

• Lecture 24 Summary

• Section 2 - Feature Engineering

• Lecture 25 Using Google Colab

• Lecture 26 Introduction

• Lecture 27 One Hot Encoding

• Lecture 28 Count Vectorizer

• Lecture 29 N-grams

• Lecture 30 Hash Vectorizing

• Lecture 31 Word Embedding

• Lecture 32 FastText

• Section 3 - Dealing with Corpus and WordNet

• Lecture 33 Introduction

• Lecture 34 In-built corpora

• Lecture 35 External Corpora

• Lecture 36 Corpuses & Frequency Distribution

• Lecture 37 Frequency Distribution

• Lecture 38 WordNet

• Lecture 39 Wordnet with Hyponyms and Hypernyms

• Lecture 40 The Average according to WordNet

• Section 4 - Create your Vocab for any NLP Model

• Lecture 41 Putting the previous knowledge together

• Lecture 42 Introduction and Challenges

• Lecture 43 Building your Vocabulary part 1

• Lecture 44 Building your Vocabulary part 2

• Lecture 45 Building your Vocabulary part 3

• Lecture 46Building your Vocabulary part 4

• Lecture 47 Building your Vocabulary part 5

• Lecture 48 Dot Product

• Lecture 49 Similarity using Dot Product

• Lecture 50 Reducing Dimensions of your Vocabulary using token improvement

• Lecture 51 Reducing Dimensions of your Vocabulary using n-grams

• Lecture 52 Reducing Dimensions of your Vocabulary using normalising

• Lecture 53 Reducing Dimensions of your Vocabulary using case normalization

• Lecture 54 When to use stemming and lemmatization?

• Lecture 55 Sentiment Analysis Overview

• Lecture 56 Two approaches for sentiment analysis

• Lecture 57 Sentiment Analysis using rule-based

• Lecture 58 Sentiment Analysis using machine learning - 1

• Lecture 59 Sentiment Analysis using machine learning - 2

• Lecture 60 Summary

• Section 5 - Word2Vec in Detail and what is going on under the hood

• Lecture 61 Introduction

• Lecture 62 Bag of words in detail

• Lecture 63 Vectorizing

• Lecture 64 Vectorizing and Cosine Similarity

• Lecture 65 Topic Modeling in Detail

• Lecture 66 Make your Vectors will more reflect on the Meaning, or Topic, of the Document

• Lecture 67 Sklearn in a short way

• Lecture 68 Summary

• Section 6 - Find and Represent the Meaning or Topic of Natural Language Text

• Lecture 70 Keyword Search VS Semantic Search

• Lecture 71 Problems in TI-IDF Leads to Semantic Search

• Lecture 72 Transform TF-IDF Vectors to Topic Vectors under the hood

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• This U&P AI - natural language processing (NLP) with python is ideal for people looking to progress their career into a python programmer.
• For those who want to become AI specialists, as well as looking to further develop their skills and knowledge.
• People who want to perform better in U&P AI - natural language processing (NLP) with Python-related careers.
• Those who are passionate about U&P AI - natural language processing (NLP) with Python-related skills.
• Learners who desire to be more efficient in U&P AI - natural language processing (NLP) with python.

No prior knowledge or experience required

• Reducing dimensions of your vocabulary using token improvement.
• Word2Vec in detail and what is going on under the hood.
• Transform TF-IDF vectors to topic vectors under the hood.

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Certificate of completion

Digital certificate - Included