English | MP4 | AVC 1920×1080 | AAC 48KHz 2ch | 2h 26m | 302 MB
Get high-quality information from your text using Machine Learning with Tensorflow, NLTK, Scikit-Learn, and Python
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.
The code bundle for this video course is available at github.com/PacktPublishing/Text-Mining-with-Machine-Learning-and-Python
A practical guide demonstrating how to extract information easily using Jupyter notebooks, Anaconda, modern packages, and tools/frameworks such as NLTK, Spacy, Gensim, Scikit-learn, Tensorflow (for CPU), and Python-CRFSuite.
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
Table of Contents
Getting Started with Text Mining
1 The Course Overview
2 Understanding Modern-Day Text Mining
3 Exploring Your Text Mining Toolbox
4 Setting Up Your Working Environment
5 A Short Rundown of the Topics We Will Cover
Reading and Processing Text Features
6 Understanding Text Data Sources
7 Cleaning Messy Text
8 Tokenization, POS Tagging, and Lemmatization
9 Dealing with N-Grams
Extracting from Text
10 Word Search Versus Entity Extraction
11 Named Entity Recognition (NER)
12 Using Pre-Trained Models
13 Training Your Own NER
14 Deep Learning Approach to NER
Classification of Text
15 Feature Representation
16 Machine Learning Algorithms for Text Classification
17 Setting Up a Basic Text Classifier
18 Pitfalls and Rules of Thumb
19 Putting Classifiers into Production
20 Deep Learning Approach to Text Classification
Word Embeddings
21 What Are Word Embeddings
22 Main Techniques
23 Training a Word2Vec Model
24 Visualizing a Trained Word Embedding Model
25 X2Vec
Other ML Topics with Text
26 Stitching It All Together
27 Topic Modelling
28 Text Generation
29 Machine Translation
30 Further Reading
31 Closing
Resolve the captcha to access the links!