Advance Natural Language Processing..

Here you will learn to break down a text into its component parts for spelling correction, feature extraction, and phrase transformation. You will know simple and easy-to-comprehend programming formulae of NLP concepts. You can also be acquainted with several up-to-date and growing research topics regarding NLP. Finally, it can help you quickly master NLTK for natural language processing. <br><br> After doing this course you should be able to put together NLP projects.
  •   Certificate : by TechSim+


What are the course objectives?
The Natural Processing Language with Python course will furnish you with in-depth knowledge of the various libraries and packages required to perform data analysis, data visualization, web scraping, machine learning and natural language processing using Python.

The Python for Natural Processing Language course is packed with real-life projects focused on customer segmentation, macro calls, attrition analysis, and retail analysis, as well as demos and case studies to give you practical experience in installing and working in the Python environment.



Who should take this Python for Natural Processing Language course?
There is a booming demand for skilled data scientists across all industries that make this course suited for participants at all levels of experience. We recommend this Natural Processing Language with Python training particularly for the following professionals:

Analytics professionals who want to work with Python
Software professionals looking to get into the field of analytics
IT professionals interested in pursuing a career in analytics
Graduates looking to build a career in analytics andNatural Processing Language
Experienced professionals who would like to harness Natural Processing Language in their fields
Anyone with a genuine interest in the field of Natural Processing Language


Key Features

Master the Concept of your Training Module

20+ Real-Time Industry-Based Projects

Instructor-led training

Hands on Practical Classes

One Year Training Menbership with TechSim+

What You Will Learn
In this Journey

  • Stage 1

    1. Introduction to Natural Language Processing

    1. Why learn NLP?

    2. Let's start playing with Python!

    3. Diving into NLTK

    4. Your turn

  • Stage 2

    2. Text Wrangling and Cleansing

    1. What is text wrangling?

    2. Text cleansing

    3. Sentence splitter

    4. Tokenization

    5. Stemming

    6. Lemmatization

    7. Stop word removal

    8. Rare word removal

    9. Spell correction

  • Stage 3

    3. Part of Speech Tagging

    1.What is Part of speech tagging

    2.Named Entity Recognition (NER)

  • Stage 4

    4. Parsing Structure in Text

    1. Shallow versus deep parsing

    2. The two approaches in parsing

    3. Why we need parsing

    4. Different types of parsers

    5. Dependency parsing

    6.Chunking

    7.Information extraction

  • Stage 5

    5. NLP Applications

    1.Building your first NLP application

    2.Other NLP applications

  • Stage 6

    6. Text Classification

    1.Machine learning

    2.Text classification

    3.Sampling

    4.The Random forest algorithm

    5. Text clustering

    6. Topic modeling in text

    7. References

  • Stage 7

    7. Web Crawling

    1. Web crawlers

    2.Writing your first crawler

    3.Data flow in Scrapy

    4.The Sitemap spider

    5.The item pipeline

    6.External references

  • Stage 8

    8. Using NLTK with Other Python Libraries

    1.NumPy

    2.SciPy

    3.pandas

    4.matplotlib

    5.External references

  • Stage 9

    9. Social Media Mining in Python

    1.Data collection

    2.Data extraction

    3.Geovisualization

  • Stage 10

    10. Text Mining at Scale

    1.Different ways of using Python on Hadoop

    2.NLTK on Hadoop

    3.Scikit-learn on Hadoop

    4.PySpark

  • Stage 11

    11. Tokenizing Text and WordNet Basics

    1.Introduction

    2.Tokenizing text into sentences

    3.Tokenizing sentences into words

    4.Tokenizing sentences using regular expressions

    5.Training a sentence tokenizer

    6.Filtering stopwords in a tokenized sentence

    7.Looking up Synsets for a word in WordNet

    8.Looking up lemmas and synonyms in WordNet

    9.Calculating WordNet Synset similarity

    10.Discovering word collocations

  • Stage 12

    12. Replacing and Correcting Words

    1.Introduction

    2.Stemming words

    3.Lemmatizing words with WordNet

    4.Replacing words matching regular expressions

    5.Removing repeating characters

    6.Spelling correction with Enchant

    7.Replacing synonyms

    8.Replacing negations with antonyms

  • Stage 13

    13. Creating Custom Corpora

    1.Introduction

    2.Setting up a custom corpus

    3.Creating a wordlist corpus

    4.Creating a part-of-speech tagged word corpus

    5.Creating a chunked phrase corpus

    6.Creating a categorized text corpus

    7.Creating a categorized chunk corpus reader

    8.Corpus editing with file locking

  • Stage 14

    14. Part-of-speech Tagging

    1.Introduction

    2.Default tagging

    3.Training a unigram part-of-speech tagger

    4.Combining taggers with backoff tagging

    5.Training and combining ngram taggers

    6.Creating a model of likely word tags

    7.Tagging with regular expressions

    8.Affix tagging

    9.Training a Brill tagger

    10.Training the TnT tagger

    11.Using WordNet for tagging

    12.Tagging proper names

    13.Classifier-based tagging

    14.Training a tagger with NLTK-Trainer

  • Stage 15

    15. Extracting Chunks

    1.Introduction

    2.Chunking and chinking with regular expressions

    3.Merging and splitting chunks with regular expressions

    4.Expanding and removing chunks with regular expressions

    5.Partial parsing with regular expressions

    6.Training a tagger-based chunker

    7.Classification-based chunking

    8.Extracting named entities

    9.Extracting proper noun chunks

    10.Extracting location chunks

    11.Training a named entity chunker

    12. Training a chunker with NLTK-Trainer

  • Stage 16

    16. Transforming Chunks and Trees

    1.Introduction

    2.Filtering insignificant words from a sentence

    3.Correcting verb forms

    4. Swapping verb phrases

    5.Swapping noun cardinals

    6.Swapping infinitive phrases

    7.Singularizing plural nouns

    8.Chaining chunk transformations

    9.Converting a chunk tree to text

    10.Flattening a deep tree

    11.Creating a shallow tree

    12.Converting tree labels

  • Stage 17

    17. Text Classification

    1.Introduction

    2.Bag of words feature extraction

    3.Training a Naive Bayes classifier

    4.Training a decision tree classifier

    5.Training a maximum entropy classifier

    6.Training scikit-learn classifiers

    7.Measuring precision and recall of a classifier

    8.Calculating high information words

    9.Combining classifiers with voting

    10.Classifying with multiple binary classifiers

    11.Training a classifier with NLTK-Trainer

  • Stage 18

    18. Distributed Processing and Handling Large Datasets

    1.Introduction

    2.Distributed tagging with execnet

    3.Distributed chunking with execnet

    4.Parallel list processing with execnet

    5.Storing a frequency distribution in Redis

    6.Storing a conditional frequency distribution in Redis

    7.Storing an ordered dictionary in Redis

    8.Distributed word scoring with Redis and execnet

  • Stage 19

    19. Parsing Specific Data Types

    1.Introduction

    2.Parsing dates and times with dateutil

    3.Timezone lookup and conversion

    4. Extracting URLs from HTML with lxml

    5.Cleaning and stripping HTML

    6.Converting HTML entities with BeautifulSoup

    7.Detecting and converting character encodings

  • Stage 20

    20. Working with Strings

    1.Tokenization

    2.Normalization

    3.Substituting and correcting tokens

    4. Applying Zipf's law to text

    5.Similarity measures

  • Stage 21

    21. Statistical Language Modeling

    1.Understanding word frequency

    2.Applying smoothing on the MLE model

    3.Develop a back-off mechanism for MLE

    4.Applying interpolation on data to get mix and match

    5.Evaluate a language model through perplexity

    6.Applying metropolis hastings in modeling languages

    7.Applying Gibbs sampling in language processing

  • Stage 22

    22. Morphology _Getting Our Feet Wet

    1.Introducing morphology

    2.Understanding stemmer

    3.Understanding lemmatization

    4. Developing a stemmer for non-English language

    5.Morphological analyzer

    6.Morphological generator

    7.Search engine

  • Stage 23

    23. Parts_of_Speech Tagging Identifying Words

    1.Introducing parts-of-speech tagging

    2.Creating POS-tagged corpora

    3.Selecting a machine learning algorithm

    4.Statistical modeling involving the n-gram approach

    5.Developing a chunker using pos-tagged corpora

  • Stage 24

    24. Parsing _ Analyzing Training Data

    1.Introducing parsing

    2.Treebank construction

    3.Extracting Context Free Grammar (CFG) rules from Treebank

    4.Creating a probabilistic Context Free Grammar from CFG

    5.CYK chart parsing algorithm

    6.Earley chart parsing algorithm

  • Stage 25

    25. Semantic Analysis _ Meaning Matters

    1.Introducing semantic analysis

    2.Generation of the synset id from Wordnet

    3.Disambiguating senses using Wordnet

  • Stage 26

    26. Sentiment Analysis _ I Am Happy

    1.Introducing sentiment analysis

  • Stage 27

    27. Information Retrieval _ Accessing Information

    1.Introducing information retrieval

    2.Vector space scoring and query operator interaction

    3.Developing an IR system using latent semantic indexing

    4. Text summarization

    5.Question-answering system

  • Stage 28

    28. Discourse Analysis _ Knowing Is Believing

    1.Introducing discourse analysis

  • Stage 29

    29. Evaluation of NLP Systems _ Analyzing Performance

    1.The need for evaluation of NLP systems

    2.Evaluation of IR system

    3.Metrics for error identification

    4.Metrics based on lexical matching

    5.Metrics based on syntactic matching