Text Mining with Machine Learning and Python

Text Mining with Machine Learning and Python, available at $39.99, has an average rating of 3.8, with 31 lectures, based on 84 reviews, and has 453 subscribers.

You will learn about Refine and clean your text Extract important data from text Classify text into types Apply modern ML and DL techniques on the text Work on pre-trained models Important text mining processes Analyze text in the best and most effective way This course is ideal for individuals who are This course targets Data Scientists who need to obtain a basic set of skills in the field of text analysis, or a Citizen Data Scientist who wants to get up and running with text mining. It is particularly useful for This course targets Data Scientists who need to obtain a basic set of skills in the field of text analysis, or a Citizen Data Scientist who wants to get up and running with text mining.

Enroll now: Text Mining with Machine Learning and Python

Summary

Title: Text Mining with Machine Learning and Python

Price: $39.99

Average Rating: 3.8

Number of Lectures: 31

Number of Published Lectures: 31

Number of Curriculum Items: 31

Number of Published Curriculum Objects: 31

Original Price: $109.99

Quality Status: approved

Status: Live

What You Will Learn

Refine and clean your text

Extract important data from text

Classify text into types

Apply modern ML and DL techniques on the text

Work on pre-trained models

Important text mining processes

Analyze text in the best and most effective way

Who Should Attend

This course targets Data Scientists who need to obtain a basic set of skills in the field of text analysis, or a Citizen Data Scientist who wants to get up and running with text mining.

Target Audiences

This course targets Data Scientists who need to obtain a basic set of skills in the field of text analysis, or a Citizen Data Scientist who wants to get up and running with text mining.

Text is one of the most actively researched and widely spread types of data in the Data Science field today. New advances in machine learning and deep learning techniques now make it possible to build fantastic data products on text sources. New exciting text data sources pop up all the time. You’ll build your own toolbox of know-how, packages, and working code snippets so you can perform your own text mining analyses.

You’ll start by understanding the fundamentals of modern text mining and move on to some exciting processes involved in it. You’ll learn how machine learning is used to extract meaningful information from text and the different processes involved in it. You will learn to read and process text features. Then you’ll learn how to extract information from text and work on pre-trained models, while also delving into text classification, and entity extraction and classification. You will explore the process of word embedding by working on Skip-grams, CBOW, and X2Vec with some additional and important text mining processes. By the end of the course, you will have learned and understood the various aspects of text mining with ML and the important processes involved in it, and will have begun your journey as an effective text miner.

About the Author

Thomas Dehaene is a Data Scientist at FoodPairing, a Belgium-based Food Technology scale-up that uses advanced concepts in Machine Learning, Natural Language Processing, and AI in general to capture meaning and trends from food-related media. He obtained his Master of Science degree in Industrial Engineering and Operations Research at Ghent University, before moving his career into Data Analytics and Data Science, in which he has been active for the past 5 years. In addition to his day job, Thomas is also active in numerous Data Science-related activities such as Hackathons, Kaggle competitions, Meetups, and citizen Data Science projects.

Course Curriculum

Chapter 1: Getting Started with Text Mining

Lecture 1: The Course Overview

Lecture 2: Understanding Modern-Day Text Mining

Lecture 3: Exploring Your Text Mining Toolbox

Lecture 4: Setting Up Your Working Environment

Lecture 5: A Short Rundown of the Topics We Will Cover

Chapter 2: Reading and Processing Text Features

Lecture 1: Understanding Text Data Sources

Lecture 2: Cleaning Messy Text

Lecture 3: Tokenization, POS Tagging, and Lemmatization

Lecture 4: Dealing with N-Grams

Chapter 3: Extracting from Text

Lecture 1: Word Search Versus Entity Extraction

Lecture 2: Named Entity Recognition (NER)

Lecture 3: Using Pre-Trained Models

Lecture 4: Training Your Own NER

Lecture 5: Deep Learning Approach to NER

Chapter 4: Classification of Text

Lecture 1: Feature Representation

Lecture 2: Machine Learning Algorithms for Text Classification

Lecture 3: Setting Up a Basic Text Classifier

Lecture 4: Pitfalls and Rules of Thumb

Lecture 5: Putting Classifiers into Production

Lecture 6: Deep Learning Approach to Text Classification

Chapter 5: Word Embeddings

Lecture 1: What Are Word Embeddings?

Lecture 2: Main Techniques

Lecture 3: Training a Word2Vec Model

Lecture 4: Visualizing a Trained Word Embedding Model

Lecture 5: X2Vec

Chapter 6: Other ML Topics with Text

Lecture 1: Stitching It All Together

Lecture 2: Topic Modelling

Lecture 3: Text Generation

Lecture 4: Machine Translation

Lecture 5: Further Reading

Lecture 6: Closing

Instructors

Packt Publishing
Tech Knowledge in Motion

Rating Distribution

1 stars: 4 votes

2 stars: 6 votes

3 stars: 24 votes

4 stars: 20 votes

5 stars: 30 votes

Frequently Asked Questions

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