Natural language processing (NLP) is a subfield of linguistics, computer science, and artificial intelligence concerned with the interactions between computers and human language, in particular how to program computers to process and analyze large amounts of natural language data.
The benefits of natural language processing are innumerable.
Improves the efficiency and accuracy of documentation processes,and identify the most pertinent information from large databases.
Structuring a highly unstructured data source.
An approach to process, analyze and understand large amount of text data.
With the increasing amount of text data being generated every day, NLP will only become more and more important.
What you will master?
Basics of NPL,Machine learning, deep learning, artificial intelligence.
Speech recognition, Speech-to-text and text-to-speech conversion. Transforming voice commands into written text, and vice versa.
Document summarization. Automatically generating synopses of large bodies of text.
Machine translation.Automatic translation of text or speech from one language to another.
Some Sample Videos:
Class:1- Getting started with Natural Language Processing.
Class:2- Natural Language Processing Intro.
Class:3- Basics of Machine Learning.
Class:4 - An advanced tutorial on Machine Learning.
Class:5 - Introduction of ArgMax Computation.
Class: 6 - ArgMax Computation overview.
Class:7 - WordNet fundamentals.
Class: 8 - Tutorial on WSD.
Class:9 - About Wordnet application.
Class: 10 - Session on Query Expansion in Wordnet application.
Class: 11 - Semantic relatedness measures.
Class: 12 - Lesson on semantic similarity NLP.
Class: 13 - Tutorial on Measuring WordNet.
Class: 14 - Session on Measuring of WordNet Similarity.
Class: 15 - Similarity Measures Basics.
Class: 16 - Understanding the process behind Similarity Measures.
Class: 17 - What is Resnick's work?
Class: 18 - Know about Resnick's work on WordNet Similarity.
Class: 19 - Introduction of Parsing Algorithms.
Class: 20 - Parsing Algorithms tutorial.
Class:21 - Lesson on Top-Down Parsing
Class:22 - Process of Top-Down Parsing Algorithms.
Class:23 - Fundamentals of Noun Structure in NLP.
Class:24 - Understanding Noun Structure (Top-Down Parsing Algorithms)
Class:25 - Fundamentals of Non-noun Structure in NLP.
Class:26 - Understanding Non-noun Structure; (Top-Down Parsing Algorithms)
Class: 27 - Probabilistic parsing explanation.
Class: 28 - An advanced session about Probabilistic parsing.
Class: 29 - Understanding sequence labeling.
Class: 30 - A complete study about sequence labeling.
Class:31- Basics of PCFG.
Class:32- An outline of PCFG.
Class:33- About Probabilistic parsing with CFG - 1.
Class:34 - About Probabilistic parsing with CFG - 2.
Class:35 - Probabilistic parsing training issues fundamentals.
Class:36 - Understanding a little more about Training issues.
Class:37 - An advanced session about Arguments.
Class: 38 - An advanced session about Adjuncts.
Class: 39 - Tutorial on inside probabilities.
Class: 40 - Tutorial on outside probabilities.
Class: 41 -Basics of Phonetics. (Speech)
Class: 42 - Understanding the impact of Phonetics. (Speech)
Class: 43 - Overview of HMM.
Class: 44 - What is HMM used for?
Class: 45 - Know the basics of Morphology.
Class: 46 - A complete study about Morphology.
Class: 47 - Sequence Labelling fundamentals.
Class: 48 - Session on Graphical Models for Sequence Labelling in NLP Part 1.
Class: 49 - Session on Graphical Models for Sequence Labelling in NLP Part 2.
Class:50 - Know the different types of Phonetics.
Class:51 - Why Phonetics is important in NP?
Class:52 - Consonants Intro.
Class:53 - An little bit about Consonants.
Class:54 - Session on Place and manner of articulation (in Consonants)
Class:55 - Fundamentals of Vowels.
Class:56 - An advanced tutorial on Vowels -1.
Class:57 - An advanced tutorial on Vowels -2.
Class: 58 - Understand Forward probability.
Class: 59 - Understand Backward probability.
Class: 60 - Basics of Viterbi Algorithm
Class:61- Viterbi Algorithm explanation.
Class:62- Introduction of Phonology.
Class:63- An advanced session on Phonology.
Class:64- Understanding Sentiment Analysis.
Class:65 - A complete study about Sentiment Analysis.
Class: 66 - Session about Web Opinions.
Class:67 - Know about Web scraping.
Class:68 - Practical session on web scraping.
Class:69 - Machine Translation Intro.
Class: 70 - Overview of Machine Translation.
Class: 71 - Tutorial on MT Tools.
Class: 72 - Tutorial on MT Tools. (Part 2)
Class: 73 - Getting more familiar with the GIZA++ tool.
Class: 74 - Getting more familiar with the Moses tool.
Class: 75 - Tutorial on Text Entailment.
Class: 76 - POS Tagging fundamentals.
Class: 77 - Introduction of Automatic speech recognition (ASR).
Class: 78 - Lesson on Speech Synthesis algorithms.
Class:79 - How both HMM and Viterbi algorithms works?
Class:80 - How both HMM and Viterbi algorithms works? (Part 2)
Class:81 - Session on Precision in NLP.
Class:82 - Session on Recall in NLP.
Class:83 - F-score explanation.
Class:84 - About Map.
Class:85 - Tutorial on Semantic Relations.
Class:86 - Dependency Parsing explanation.
Class: 87 - Understanding Universal Networking Language.
Class: 88 - Lesson on Semantic Role Extraction.
Class: 89 - Baum Welch Algorithm tutorial.
Class: 90 - Final words about Natural language processing.
Schedule Your Classes
The duration of the Course is 3 Months (90 days)
Weekdays - Monday to Saturday.
Weekend classes - Saturday and Sunday.
Class Consist of both Theory and Practical session.
Materials will be provided.
Please note that the Training Structure may vary for every student.
Have a Question?
Please send us any questions you may have. We would love to answer it.