AI - Natural Language Processing (NLP) with Python

AI - Natural Language Processing (NLP) with Python

• Understand every detail and build real stuff in NLP

• Learn how some plugins use semantic search to generate source code

• Building your vocabulary for any NLP model

• Reducing Dimensions of your Vocabulary for Machine Learning Models

• Feature Engineering and convert text to numerical values for machine learning models

• Keyword search VS Semantic search

• Similarity between documents

• Dealing with WordNet

• Search engines under the hood

• Tokenizing text data

• Converting words to their base forms using stemming

• Converting words to their base forms using lemmatization

• Dividing text data into chunks

• Dealing with corpuses

• Extracting document term matrix using the Bag of Words model

• Building a category predictor

• Constructing a gender identifier

• Building a sentiment analyzer

• Topic modeling using Latent Dirichlet Allocation

• Getting an Idea of NLP and Its Applications 1:17:20

  • 1: 2 - Introduction 02:59
  • 2: 3 - By The End Of This Course 01:19
  • 3: 4 - Installation 03:30
  • 4: 5 - Tips 00:30
  • 5: 6 - U - Tokenizing Data 01:15
  • 6: 7 - P - Tokenization 02:21
  • 7: 8 - U - Stemming 01:56
  • 8: 9 - P - Stemming 04:51
  • 9: 10 - U - Lemmatization 01:47
  • 10: 11 - P - Lemmatization 03:06
  • 11: 12 - U - Chunks 01:45
  • 12: 13 - P - Chunks 05:05
  • 13: 14 - U - Bag of Words 04:16
  • 14: 15 - P - Bag Of Words 04:21
  • 15: 16 - U - Category Predictor 04:30
  • 16: 17 - P - Category Predictor 05:49
  • 17: 18 - U - Gender Identification 01:08
  • 18: 19 - P - Gender Identifier 07:38
  • 19: 20 - U - Sentement Analysis 02:22
  • 20: 21 - P - Sentement Analyzer 06:58
  • 21: 22 - U - Topic Modeling 02:46
  • 22: 23 - P - Topic Modeling 05:55
  • 23: 24 - Summary 01:13

• Feature Engineering 27:17

  • 24: 1 - Introduction 01:37
  • 25: 2 - One Hot Encoding 02:27
  • 26: 3 - Count Vectorizer 03:31
  • 27: 4 - N-grams 03:56
  • 28: 5 - Hash 01:35
  • 29: 6 - Word Embedding 10:40
  • 30: 7 - FastText 03:31

• Dealing with Corpus and WordNet 47:32

  • 31: Corpuses_WordNet - 1 - Introduction 01:07
  • 32: Corpuses_WordNet - 2 - in-built 05:46
  • 33: Corpuses_WordNet - 3 - external corpora 07:30
  • 34: Corpuses_WordNet - 4 - corpuses_FD 07:19
  • 35: Corpuses_WordNet - 5 - FD 05:58
  • 36: Corpuses_WordNet - 6 - WordNet 05:44
  • 37: Corpuses_WordNet - 7 - WordNet H_H 07:23
  • 38: Corpuses_WordNet - 8 - WordNet - The Average 06:45

• Create your Vocab for any NLP Model 1:43:12

  • 39: 1 - Introduction and Challenges 08:11
  • 40: 2-0Tokenization for Building your Vocabulary 02:26
  • 41: 2-1Tokenization for Building your Vocabulary 03:03
  • 42: 2-2Tokenization for Building your Vocabulary 07:12
  • 43: 2-3Tokenization for Building your Vocabulary 11:40
  • 44: 2-4Tokenization for Building your Vocabulary 06:13
  • 45: 2-5Tokenization DotProduct 03:12
  • 46: 2-6Measuring similarity using dotproduct 02:50
  • 47: 2-7-reducing dim of vocabA token improvement 02:03
  • 48: 2-8 N-grams for your vocabulary 09:30
  • 49: 2-9 Normalizing for your vocabulary 09:37
  • 50: 2-10 Case normalization for your vocab 05:10
  • 51: 2-11 When to use stemming and lemmatization 03:36
  • 52: 3 - 0Sentiment Analysis in details 05:01
  • 53: 3 - 1Approaches for SA 02:41
  • 54: vocab-17 - rule based 05:13
  • 55: vocab-18 - naive baies 10:22
  • 56: vocab-19 04:23
  • 57: vocab-20 - summary 00:49

• Word2Vec in Detail and what is going on under the hood 1:08:53

  • 58: word2vec-1 04:14
  • 59: word2vec-2 13:59
  • 60: word2vec-3-4 08:22
  • 61: word2vec-5 10:30
  • 62: word2vec-6 16:19
  • 63: word2vec-7 10:26
  • 64: word2vec-8 03:03
  • 65: word2vec-9 02:00

• Find and Represent the Meaning or Topic of Natural Language Text 25:30

  • 66: 1-keywordsearchVSss 04:05
  • 67: 2-problems in tfidf 10:01
  • 68: 3-TF-I~1 11:24

THIS COURSE IS FOR BEGINERS OR INTERMEDIATES, IT IS NOT FOR EXPERTS

This course is focusing on the NLP:

• Learn key NLP concepts and intuition training to get you quickly up to speed with all things NLP.

• I will give you the information in an optimal way, I will explain in the first video for example what is the concept, and why is it important, what is the problem that led to thinking about this concept and how can I use it (Understand the concept). In the next video, you will go to practice in a real-world project or in a simple problem using python (Practice).

• The first thing you will see in the video is the input and the output of the practical section so you can understand everything and you can get a clear picture!

• You will have all the resources at the end of this course, the full code, and some other useful links and articles.

In this course, we are going to learn about natural language processing. We will discuss various concepts such as tokenization, stemming, and lemmatization to process text. We will then discuss how to build a Bag of Words model and use it to classify text. We will see how to use machine learning to analyse the sentiment of a given sentence. We will then discuss topic modeling and implement a system to identify topics in a given document. We will start with simple problems in NLP such as Tokenization Text, Stemming, Lemmatization, Chunks, Bag of Words model. and we will build some real stuff such as :

1. Learning How to Represent the Meaning of Natural Language Text

2. Building a category predictor to predict the category of a given text document.

3. Constructing a gender identifier based on the name.

4. Building a sentiment analyzer used to determine whether a movie review is positive or negative.

5. Topic modeling using Latent Dirichlet Allocation

6. Feature Engineering

7. Dealing with corpora and WordNet

8. Dealing With your Vocabulary for any NLP and ML model

Curriculum

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
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

Top reasons to Study Online at Frontier Education

• Tailor made: Course adapted to market needs and interests

• Flexible programs: study and work at your own pace on easy-to-use web platforms

• Online education: progressive teaching methods with video or easy to understand medium

• Multicultural: connect with classmates from all corners of the globe

• Anyone who wants to understand NLP concepts and build some projects
• Beginner python developers curios about NLP, this course is not for experienced data scientists

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Reed courses certificate of completion

Digital certificate - Included