Intro To Natural Language Processing In Python For Ai

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