Become a Pro in Natural Language Processing
About This Video
In Detail
You will learn how machine can be trained to make sense of language humans use to interact. You will come across many NLP algorithms that teach the computational models about Lexical processing, basic syntactic processing. …
Become a Pro in Natural Language Processing
About This Video
In Detail
You will learn how machine can be trained to make sense of language humans use to interact. You will come across many NLP algorithms that teach the computational models about Lexical processing, basic syntactic processing. You will learn the mechanism Google translator uses, to understand the context of language and converts to a different language. You will build a chat-bot using an open-source tool Rasa, which is a text and voice-based conversations, understand messages, hold conversations, and connect to messaging channels and APIs. You will also learn to train the model you have created on NLU.
The machine cannot be trained to understand or process data by traditional hand coded programs that rely heavily on very specific conditions. The moment there is a change in input, the hand coded program is rendered useless. So, rather than having to code possible conversations, we require a model that enables the system to make sense of context. By the end of the course you will be able to build NLP models that can summarize blocks of text to extract most important ideas, sentiment analysis to extract the sentiments from given block of text, identification of type entity extracted. All the projects included in this course are Real-World projects.
Chapter 1 : Introduction to NLP and Regex
Introduction to NLP 00:06:26
Text data Part 1 00:05:13
Text data Part 2 00:12:32
Text Encoding 00:07:46
Regular Expressions: Part 1 00:09:00
Regular Expressions: Part 2 00:15:21
Regular Expressions: Part 3 00:14:18
Regular Expressions: Part 4 00:11:36
Regular Expressions: Part 5 00:06:27
Regular Expressions: Part 6 00:07:32
Regular Expressions: Use case 00:06:51
Chapter 2 : Introduction to Lexical Processing
Stopwords 00:24:39
Splitting Words 00:10:59
Bag-of-Words 00:16:26
Handling Similar text Words Part 1 00:05:51
Handling Similar text Words Part 2 00:12:15
Case-Study Part 1 00:06:23
If-IDF 00:08:08
Case-Study Part 2 00:03:21
Case study 00:12:16
Chapter 3 : Advanced Lexical Processing
Spelling mistakes 00:12:10
Soundex Algorithm 00:13:34
Case study 00:05:52
Dealing with spelling mistake 00:11:26
Case-Study 2 00:05:19
Case study: Spell Corrector-1 00:04:37
Case study: Spell Corrector-2 00:11:57
Case study: Spell Corrector-3 00:03:44
Handling combined word like New Delhi (Part-1) 00:10:34
Handling combined word like New Delhi (Part-2) 00:02:24
Chapter 4 : Basic Syntactic Processing
What is Syntactic Processing 00:13:50
Parsing 00:11:33
Grammar for English sentence Part 1 00:13:49
Grammar for English sentence Part 2 00:11:28
Case Study: Assign grammar to English sentences Part 1 00:12:42
Case Study: Assign grammar to English sentence Part 2 00:07:10
Chapter 5 : Intermediate Syntactic Processing
Stochastic Parsing 00:13:23
Viterbi Algorithm 00:06:47
Hidden Markov Model 00:14:22
Decoding Problem Part 1 00:05:50
Decoding problem Part 2 00:06:17
Learning Hidden Markov Model 00:04:23
Case study on Syntactic Processing Part 1 00:14:52
Case study on Syntactic Processing Part 2 00:06:19
Chapter 6 : Advanced Syntactic Processing
Introduction 00:05:29
Issue with Shallow parsing 00:02:03
CFG grammar Part 1 00:14:20
CFG grammar Part 2 00:10:32
Top-down parsing 00:20:58
Case study on advance syntactic processing Part 1 00:04:58
Case study on advance syntactic processing Part 2 00:18:24
Case study on advance syntactic processing Part 3 00:04:01
Practical issues with above approach 00:02:51
Chapter 7 : Probabilistic Approach
Probabilistic CFG grammar 00:08:11
Why Shallow Parsing is Not Sufficient 00:03:22
Chomsky Normal Form 00:05:22
Dependency parsing Part 1 00:09:51
Dependency parsing Part 2 00:12:06
Chapter 8 : Syntactic processing using Real world project
Introduction to Information Extraction project Part 1 00:08:34
Case study Part 2 00:17:58
Case study Part 3 00:17:38
Case study Part 4 00:06:55
Case study Part 5 00:39:58
Case study Part 6 00:07:31
Case study Part 7 00:09:50
Chapter 9 : Introduction to Semantic Processing
Introduction 00:04:31
Concepts 00:16:24
Entity 00:12:01
Arity 00:07:32
Reification 00:04:32
Schema 00:06:16
Semantic Associations Part1 00:09:20
Semantic Associations Part2 00:06:00
Terms and concept 00:10:22
Principle of composition 00:02:54
Wordnet 00:13:02
Word Sense Disambiguation 00:07:59
Case study on WSD 00:12:23
Chapter 10 : Advanced Semantic Processing Part-1
Introduction to Distributional Semantics 00:03:18
Distributional Semantics 00:07:48
Occurrence Matrix Part 1 00:10:21
Occurrence Matrix Part 2 00:07:12
Co-occurrence Matrix 00:06:27
Word Vectors Part 1 00:06:34
Distance Metric 00:06:35
Word Vectors Part 2 00:05:09
Understanding Word Embeddings 00:11:21
Chapter 11 : Advanced Semantic Processing Part-2
LSA- Latent Semantic Analysis 00:10:27
Case study with LSA 00:03:07
Word2vec Part 1 00:09:36
Word2vec Part 2 00:07:12
Case study: LSA 00:01:58
Case study: Word2vec Part 1 00:05:26
Case study: Word2vec Part 2 00:02:46
Case study: Word2vec Part 3 00:03:38
Case study: Word2vec Part 4 00:03:08
Case study: Classification Part 1 00:08:33
Case study: Classification Part 2 00:03:43