Natural Language Processing with Python Course

Learning Objectives: In this module, you will learn about text mining and the ways of extracting and reading data from some common file types including NLTK corpora

Topics:

• Overview of Text Mining
• Need of Text Mining
• Natural Language Processing (NLP) in Text Mining
• Applications of Text Mining
• OS Module
• Reading, Writing to text and word files
• Setting the NLTK Environment
• Accessing the NLTK Corpora

Hands-On:

• Install NLTK Packages using NLTK Downloader
• Accessing your operating system using the OS Module in Python
• Reading & Writing .txt Files from/to your Local
• Reading & Writing .docx Files from/to your Local
• Working with the NLTK Corpora

Learning Objectives: This module will help you understand some ways of text extraction and cleaning using NLTK.

Topics:

• Tokenization
• Frequency Distribution
• Different Types of Tokenizers
• Bigrams, Trigrams & Ngrams
• Stemming
• Lemmatization
• Stopwords
• POS Tagging
• Named Entity Recognition

Hands-On:

• Tokenization: Regex, Word, Blank line, Sentence Tokenizers
• Bigrams, Trigrams & Ngrams
• Stopword Removal
• POS Tagging
• Named Entity Recognition (NER)

Learning Objectives: In this Module, you will learn how to analyse a sentence structure using a group of words to create phrases and sentences using NLP and the rules of English grammar

Topics:

• Syntax Trees
• Chunking
• Chinking
• Context Free Grammars (CFG)
• Automating Text Paraphrasing

Hands-On:

• Parsing Syntax Trees
• Chunking
• Chinking
• Automate Text Paraphrasing using CFG’s

Learning Objectives: In this module, you will explore text classification, vectorization techniques and processing using scikit-learn

Topics:

• Machine Learning: Brush Up
• Bag of Words
• Count Vectorizer
• Term Frequency (TF)
• Inverse Document Frequency (IDF)

Hands-On:

• Demonstrate Bag of Words Approach
• Working with CountVectorizer()
• Using TF & IDF

Learning Objectives: In this module, you will learn to build a Machine Learning classifier for text classification

Topics:

• Converting text to features and labels
• Multinomial Naive Bayes Classifier
• Leveraging Confusion Matrix

Hands-On:

• Converting text to features and labels
• Demonstrate text classification using Multinomial NB Classifier
• Leveraging Confusion Matrix

Learning Objectives: In this module, you will learn Sentiment Classification on Movie Rating Dataset

Topics:

• Implement all the text processing techniques starting with tokenization
• Express your end to end work on Text Mining
• Implement Machine Learning along with Text Processing

Hands-On:

• Sentiment Analysis

CertAdda’s Natural Language Processing using Python Training focuses on step by step guide to NLP and Text Analytics with extensive hands-on using Python Programming Language. It has been packed up with a lot of real-life examples, where you can apply the learned content to use. Features such as Semantic Analysis, Text Processing, Sentiment Analytics, and Machine Learning have been discussed. This course is for anyone who works with data and text– with good analytical background and little exposure to Python Programming Language. It is designed to help you understand the important concepts and techniques used in Natural Language Processing using Python Programming Language. You will be able to build your own machine learning model for text classification. Towards the end of the course, we will be discussing various practical use cases of NLP in the python programming language to enhance your learning experience.

Natural Language Processing (or Text Analytics/Text Mining) applies analytic tools to learn from collections of text data, like social media, books, newspapers, emails, etc. The goal can be considered to be similar to humans learning by reading such material. However, using automated algorithms we can learn from massive amounts of text, very much more than a human can. It is bringing a new revolution by giving rise to chatbots and virtual assistants to help one system address queries of millions of users. NLP is a branch of artificial intelligence that has many important implications on the ways that computers and humans interact. Human language, developed over thousands and thousands of years, has become a nuanced form of communication that carries a wealth of information that often transcends the words alone. NLP will become an important technology in bridging the gap between human communication and digital data.

After completing this NLP training in Python, you will be able to:

• Learn basics of Natural Language Processing in the most popular Python Library: NLTK
• Learn techniques to access or modify some of the most common file types
• Using I python notebooks, master the art of step by step text processing
• Gain insight into the ‘Roles’ played by an NLP Engineer
• Learn about Bag of Words Modelling and Tokenization of Text.
• Use n-Gram Models to model and analyze the Bag of words from Corpus
• Learn about converting text to vector using word frequency count, tf-idf etc.
• Learn about Latent Semantic Analysis and its usage in the processing of context-aware Semantic Content.
• Work with real-time data
• Learn in detail about Sentiment Analysis one of the most interesting applications of Natural Language Processing
• Gain expertise to handle business in future, living the present

CertAdda’s NLP Training is a good fit for the below professionals:

• From a college student having exposure to programming to a technical architect/lead in an organisation
• Developers aspiring to be a ‘Data Scientist’
• Analytics Managers who are leading a team of analysts
• Business Analysts who want to understand Text Mining Techniques
• ‘Python’ professionals who want to design automatic predictive models on text data
• “This is apt for everyone”

The prerequisites for this NLP course is Python programming and a good understanding of Machine Learning concepts. As a goodwill gesture, CertAdda offers a complimentary self-paced course in your LMS on Python to brush up on your Python Skills.

You don’t have to worry about the System Requirements as you will be doing your Practical on a Cloud LAB environment. This environment already contains all the necessary software that will be required to execute your practicals.

You will do your Assignments/Case Studies using Jupyter Notebook that is already installed on your Cloud LAB environment whose access details will be available on your LMS. You will be accessing your Cloud LAB environment from a browser. For any doubt, the 24*7 support team will promptly assist you.

You will never miss a lecture at CertAdda You can choose either of the two options: View the recorded session of the class available in your LMS or You can attend the missed session, in any other live batch

Your access to the Support Team is for lifetime and will be available 24/7. The team will help you in resolving queries, during and after the course.

Post-enrolment, the LMS access will be instantly provided to you and will be available for lifetime. You will be able to access the complete set of previous class recordings, PPTs, PDFs, assignments. Moreover the access to our 24×7 support team will be granted instantly as well. You can start learning right away.

Yes, the access to the course material will be available for lifetime once you have enrolled into the course.

Just give us a CALL at +91 8178510474 / +91 9967920486 OR email at admin@certadda.com