Learn the Technology Behind AI Tools Like ChatGPT: Understanding, Generating, and Classifying Human Language
What you'll learn
Natural Language Processing for AI
Text preprocessing techniques
Text tagging and entity extraction
Sentiment analysis
Uncovering topics in the text
Text classification
Vectorizing text for machine learning
Requirements
Basic Python programming skills
Description
Are you passionate about Artificial Intelligence and Natural Language Processing?Do you want to pursue a career as a data scientist or as an AI engineer?If that's the case, then this is the perfect course for you!In this Intro to Natural Language Processing in Python course you will explore essential topics for working with text data. Whether you want to create custom text classifiers, analyze sentiment, or explore concealed topics, you'll learn how NLP works and obtain the tools and concepts necessary to tackle these challenges.Natural language processing is an exciting and rapidly evolving field that fundamentally impacts how we interact with technology. In this course, you'll learn to unlock the power of natural language processing and will be equipped with the knowledge and skills to start working on your own NLP projects.The training offers you access to high quality Full HD videos and practical coding exercises. This is a format that facilitates easy comprehension and interactive learning. One of the biggest advantages of all trainings produced by 365 Data Science is their structure. This course makes no exception. The well-organized curriculum ensures you will have an amazing experience.You won't need prior natural language processing training to get started—just basic Python skills and familiarity with machine learning.This introduction to NLP guides you step-by-step through the entire process of completing a project. We'll cover models and analysis and the fundamentals, such as processing and cleaning text data and how to get data in the correct format for NLP with machine learning.We'll utilize algorithms like Latent Dirichlet Allocation, Transformer models, Logistic Regression, Naive Bayes, and Linear SVM, along with such techniques as part-of-speech (POS) tagging and Named Entity Recognition (NER).You'll get the opportunity to apply your newly acquired skills through a comprehensive case study, where we'll guide you through the entire project, covering the following stages:Text cleansingIn-depth content analysisSentiment analysisUncovering hidden themesUltimately crafting a customized text classification modelBy completing the course, you'll receive а verifiable NLP certificate and will add an excellent project to your portfolio to show off your ability to analyze text like a pro.So, what are you waiting for?Click Buy Now and start your AI journey today!
Overview
Section 1: Introduction
Lecture 1 Introduction to the course
Lecture 2 Introduction to NLP
Lecture 3 NLP in everyday life
Lecture 4 Supervised vs Unsupervised NLP
Section 2: Text Preprocessing
Lecture 5 The importance of data preparation
Lecture 6 Lowercase
Lecture 7 Removing stop words
Lecture 8 Regular expressions
Lecture 9 Tokenization
Lecture 10 Stemming
Lecture 11 Lemmatization
Lecture 12 N-grams
Lecture 13 Practical task
Section 3: Identifying Parts of Speech and Named Entities
Lecture 14 Text tagging
Lecture 15 Parts of speech (POS) tagging
Lecture 16 Named entity recognition (NER)
Lecture 17 Practical task
Section 4: Sentiment Analysis
Lecture 18 What is sentiment analysis?
Lecture 19 Rule-based sentiment analysis
Lecture 20 Pre-trained transformer models
Lecture 21 Practical task
Section 5: Vectorizing Text
Lecture 22 Numerical representation of text
Lecture 23 Bag of Words model
Lecture 24 TF-IDF
Section 6: Topic Modelling
Lecture 25 What is topic modelling?
Lecture 26 When to use topic modelling?
Lecture 27 Latent Dirichlet Allocation
Lecture 28 LDA in Python
Lecture 29 Latent Semantic Analysis
Lecture 30 LSA in Python
Section 7: Builing Your Own Text Classifier
Lecture 31 Building a custom text classifier
Lecture 32 Logistic regression
Lecture 33 Naive Bayes
Lecture 34 Linear Support Vector Machine
Section 8: Case Study: Categorizing Fake News
Lecture 35 Introducing the project
Lecture 36 Exploring our data through POS tags
Lecture 37 Extracting named entities
Lecture 38 Processing the text
Lecture 39 Does sentiment differ between news types?
Lecture 40 What topics appear in fake news? (Part 1)
Lecture 41 What topics appear in fake news? (Part 2)
Lecture 42 Categorizing fake news with a custom classifier
Section 9: The Future of NLP
Lecture 43 What is deep learning?
Lecture 44 Deep learning for NLP
Lecture 45 Non-English NLP
Lecture 46 What's next for NLP?
Aspiring data scientists and AI engineers,AI and LLM students,Data science students,Data scientists,Anyone interested to learn how to work with Natural Language Processing
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