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Build smart AI-driven linguistic applications using deep learning and NLP techniques What you'll learn Attain a strong foundation in Python for deep learning and NLP Build applications with Python, using the Natural Language Toolkit via NLP Get to grips on various NLP techniques to build an intelligent Chatbot Classify text and speech using the Naive Bayes Algorithm Use various tools and algorithms to build real-world applications Build solutions such as text similarity, summarization, sennt analysis and anaphora resolution to get up to speed with new trends in NLP Write your own POS taggers and grammars so that any syntactic analyses can be performed easily Use the inbuilt chunker and create your own chunker to evaluate trained models Create your own named entities using dictionaries to use inbuilt text classification algorithms Requirements Basic knowledge of NLP and some prior programming experience in Python is assumed. Familiarity with deep learning will be helpful. Description Natural Language Processing (NLP) is the most interesting subfield of data science. It offers powerful ways to interpret and act on spoken and written language. It’s used to help deal with customer support enquiries, analyse how customers feel about a product, and provide intuitive user interfaces. If you wish to build high perfog day-to-day apps by leveraging NLP, then go for this course.This course teaches you to write applications using one of the popular data science concepts, NLP. You will b with learning various concepts of natural language understanding, Natural Language Processing, and syntactic analysis. You will learn how to implement text classification, identify parts of speech, tag words, and more. You will also learn how to analyze sentence structures and master syntactic and semantic analysis. You will learn all of these through practical demonstrations, clear explanations, and interesting real-world examples. This course will give you a versatile range of NLP skills, which you will put to work in your own applications.Contents and OverviewThis training program includes 2 complete courses, carefully chosen to give you the most comprehensive training possible.The first course, Natural Language Processing in Practice, will help you gain NLP skills by practical demonstrations, clear explanations, and interesting real-world examples. It will give you a versatile range of deep learning and NLP skills that you can put to work in your own applications.The second course, Developing NLP Applications Using NLTK in Python, course is designed with advanced solutions that will take you from newbie to pro in perfog natural language processing with NLTK. You will come across various concepts covering natural language understanding, natural language processing, and syntactic analysis. It consists of everything you need to efficiently use NLTK to implement text classification, identify parts of speech, tag words, and more. You will also learn how to analyze sentence structures and master syntactic and semantic analysis.By the end of this course, you will be all ready to bring deep learning and NLP techniques to build intelligent systems using NLTK in Python.Meet Your Expert(s):We have the best work of the following esteemed author(s) to ensure that your learning journey is smooth:Smail Oubaalla is a talented Software Eeer with an interest in building the most effective, beautiful, and correct piece of software possible. He has helped companies build excellent programs. He also manages projects and has experience in designing and managing new ones. When not on the job, he loves hag out with friends, hiking, and playing sports (football, basketball, rugby, and more). He also loves working his way through every recipe he can find in the family cookbook or elsewhere, and indulging his love for seeing new places.Krishna Bhavsar has spent around 10 years working on natural language processing, social media analytics, and text mining in various industry domains such as hospitality, banking, healthcare, and more. He has worked on many different NLP libraries such as Stanford CoreNLP, IBM's SystemText and BigInsights, GATE, and NLTK to solve industry problems related to textual analysis. He has also worked on analyzing social media responses for popular television shows and popular retail brands and products. He has also published a paper on sennt analysis augmentation techniques in 2010 NAACL. he recently created an NLP pipeline/toolset and open sourced it for public use. Apart from acads and technology, Krishna has a passion for motorcycles and football. In his free , he likes to travel and explore. He has gone on pan-India road trips on his motorcycle and backpacking trips across most of the countries in South East Asia and Europe.Naresh Kumar has more than a decade of professional experience in designing, implementing, and running very-large-scale Internet applications in Fortune Top 500 companies. He is a full-stack architect with hands-on experience in domains such as ecommerce, web hosting, healthcare, big data and analytics, data streaming, advertising, and databases. He believes in open source and contributes to it actively. Naresh keeps himself up-to-date with emeg technologies, from Linux systems internals to frontend technologies. He studied in BITS-Pilani, Rajasthan with dual degree in computer science and economics.Pratap Dangeti develops machine learning and deep learning solutions for structured, image, and text data at TCS, in its research and innovation lab in Bangalore. He has acquired a lot of experience in both analytics and data science. He received his master's degree from IIT Bombay in its industrial eeering and operations research program. Pratap is an artificial intelligence enthusiast. When not working, he likes to read about nextgen technologies and innovative methodologies. He is also the author of the book Statistics for Machine Learning by Packt. Overview Section 1: Natural Language Processing in Practice Lecture 1 Course Overview Lecture 2 Setup and Installation Lecture 3 Understanding NLP and Its Benefits Lecture 4 Exploring NLP Tools and Libraries Lecture 5 Tokenization Lecture 6 Stop Words Lecture 7 Part of Speech Tagging Lecture 8 Stemming and Lemmatization Lecture 9 Named Entity Recognition Lecture 10 TF-IDF Lecture 11 Introduction to Sennt Analysis Lecture 12 Pre-Processing the Dataset Lecture 13 Word Embeddings Lecture 14 Build the Network Lecture 15 Train the Model Lecture 16 Test the Model Lecture 17 Apply to a Single Input Lecture 18 Machine Learning Lecture 19 Classification Lecture 20 Pre-Processing the Dataset Lecture 21 Naive Bayes and SVM Lecture 22 Train the Classifier Lecture 23 Test the Classifier Lecture 24 Chatbots Lecture 25 Simple NLTK Bot Lecture 26 Create a ChatterBot Lecture 27 Enhancing the Chabot Lecture 28 Training the Chabot Section 2: Developing NLP Applications Using NLTK in Python Lecture 29 The Course Overview Lecture 30 Exploring the In-Built Tagger Lecture 31 Writing Your Own Tagger Lecture 32 Training Your Own Tagger Lecture 33 Learning to Write Your Own Grammar Lecture 34 Writing a Probabilistic CFG Lecture 35 Writing a Recursive CFG Lecture 36 Using the Built-In Chunker Lecture 37 Writing Your Own Simple Chunker Lecture 38 Training a Chunker Lecture 39 Parsing Recursive Descent Lecture 40 Parsing Shift-Reduce Lecture 41 Parsing Dependency Grammar and Projective Dependency Lecture 42 Parsing a Chart Lecture 43 Using Inbuilt NERs Lecture 44 Creating, Inversing, and Using Dictionaries Lecture 45 Choosing the Feature Set Lecture 46 Snting Sentences Using Classification Lecture 47 Writing a POS Tagger with Context Lecture 48 Creating an NLP Pipeline Lecture 49 Solving the Text Similarity Problem Lecture 50 Resolving Anaphora Lecture 51 Disambiguating Word Sense Lecture 52 Perfog Sennt Analysis Lecture 53 Exploring Advanced Sennt Analysis Lecture 54 Creating a Conversational Assistant or Chatbot This course is for data science professionals who would like to expand their knowledge from traditional NLP techniques to state-of-the-art techniques in the application of NLP. 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