Natural Language Processing with Python Certificate Course
CertOcean's Natural Language Processing with Python route will take you
via the necessities of computing in texts all of the manners as much as
classifying texts by using Machine Learning Algorithms. You will master
different standards, which include Tokenization, Stemming,
Lemmatization, POS tagging, Named Entity Recognition, Syntax Tree
Passing, and so forth with the use of Python's most renowned NLTK
package. Once you research NLP, you will comprehend your very own text
classifier with the help of the Naive Bayes algorithm.
Why should you enroll in Natural Language Processing with Python Course?
* NLP will assist Al Market to be more than $60 purchase 2025- Forbes
* IBM, Microsoft Corporation, Nuance Communications, Health Fidelity have excessive calls for NLP experts.
* According to indeed.com, the mean payment of an NLP professional is $128,857
Course Curriculum
Learning Module: During this module, you find text mining and other ways of extracting and reading information from some ordinary file types, which include NLTK corpora.
Topics:
* A general run-through of text mining
* Necessary of text mining
* Natural Language Processing (NLP) in text mining
* Candidates for text mining
* OS module
* Reading, writing to text, and word files.
* NLTK Environment setting
* Accessing the NLTK corpora
Topics:
* Tokenization
* Frequency Distribution
* Various types of Tokenizers
* Bigrams, Trigrams, and Ngrams
* Stemming
* Lemmatization
* Stopwords
* POS Tagging
* Named Entity Recognition (NER) Demo/Trial
* Tokenization
* Bigrams, Trigrams, and Ngrams
* Stopwords
* POS Tagging
* Named Entity Recognition (NER)
Topics:
* Syntax Trees
* Chunking
* Chinking
* Context-Free Grammars (CFR)
* Automating Text Paraphrasing Demo/Trial
* Passing Syntax Trees
* Chunking
* Chinking
* Automate Text Phrasing with the use of CFG's
Topics:
* Machine Learning Brushup
* Bag of Words
* Countvectorizer
* Term Frequency (TF)
* Inverse Document Frequency Demo/Trial
* Exhibit Bag of Words approach
* Collaborating with Countvectorizer
* Using TF and IDF
Topics:
* Conversion of text to features and labels
* Multinomial Naive Bayes Classifier
* Leveraging Confusion Matrix Demo/Trial
* Conversion of text to features and labels
* Demonstrate text classification using Multinomial NB classifier
* Leveraging Confusion Matrix
Objective: By the end of the module, you should be capable of:
* Implement all the text process techniques beginning with tokenization
* Express your work from scratch to end on Text Mining
* Implement machine learning at the side of the text process
Course Description
It focuses on step-by-step guide to NLP and Text Analytics with in-depth active victimization using Python program language. We packed it up with plenty of real-life examples where you can apply your learnings to use. In this nlp with python course, we mentioned the features such as Semantic Analysis, Text process, Sentiment Analytics, and machine learning. This course is for anyone who works with information and text with sensible analytical background and exposure to Python Programming language. We have made this nlp with python course to teach you the most important topics & methodologies that are deployed in Natural Language Processing with Python using Python Programme Language. You will be ready to build your machine learning model for text classification. Towards the course's tip, we will be discussing numerous sensible, practical cases of NLP in the python programming language to reinforce your learning expertise.
NLP is a branch of computing that has various vital implications on how computers and humans move. Human Language developed thousands of years ago has become a shady sort of communication that carries the wealth of knowledge that transcends the words alone.
NLP can become a chief important technology in perking up the gap between human contact and digital information.
* Learn the basis of Natural Language Processing in the famous Python Library
* Learn techniques to access or modify common file types
* Mater the art of step-by-step process using python l notebooks
* Learn the insights of the roles played by NLP engineer
* Acquire knowledge about Bag of Words Modelling and Tokenization of text
* Use the n-grams model to model and analyze the bag of words from Corpus.
* Learn the conversion of text to vector using word frequency count, etc
* Work with instantaneous data.
* Detailed learning about Sentiment Analysis: one of the fascinating courses of Natural
* Language Processing
* Learn the skill of business handling for future and present living.
* From a school student having exposure to programming to a technical architect/led in an organization
* Developers ambitious to be data scientists
* Analytic Managers leading the team of analysts
* Business Analysts eager to understand and learn text mining techniques
* Python professionals wanting to design automatic prophetic models on text information
* "This is suitable for everyone."
Features
Instructor-led Sessions
18 Hours of Online Live Instructor-Led Classes. Weekend Class: 6 sessions of 3 hours each.
Real-life Case Studies
Live project based on any of the selected use cases, involving the implementation of the various NLP concepts using Python.
ASSIGNMENTS
Each class will be followed by practical assignments which will aggregate to a minimum 20 hours.
ACCESS LIFETIME
You get lifetime access to the Learning Management System (LMS) where presentations, quizzes, installation guide & class record.
24*7 SUPPORT BY EXPERTS
We have the 24x7 online support team to resolve all your technical queries, through ticket based tracking system, for the lifetime.
CERTIFICATION
Towards the end of the course, you will be working on a project. CertOcean certifies you as a "Natural Language Processing Engineer" based on the project.
FORUM
Frequently Asked Questions (FAQs):
With CertOcean's Platform, you can never miss your lecture/class. You have two options for that:
* View the recordings of the session available on LMS.
* You can attend the missed session in any other live class.
