English | MP4 | AVC 1920×1080 | AAC 48KHz 2ch | 1h 44m | 505 MB
Turn practical hands-on projects such as language processing, computer vision, sentiment analysis, and text processing into useful application in Python to take your skills to another level!
Scientists have been increasingly using Python for data analysis tasks such as natural language processing and computer vision, and a new wave of modules and packages, make programming these tasks easier than ever. In this course, you’ll dive into Natural Language Processing and get familiar with the NLTK package. This video course is filled with real-world, practical examples that show you Python’s true power as a programming language for data analysis.
You’ll learn to read text in documents using different models, and employ sentiment analysis to predict the author’s intent. You’ll also see how to employ Python to read images and for computer vision. Once you’ve learned to employ specific Python packages and syntax for these tasks, you’ll explore case studies that put forth solid real-world examples on spam filtering and analyzing human emotions through a dictionary of images.
This course contains in-depth content balanced with tutorials that put theory into practice. This course will give you both a theoretical understanding and practical exp with examples that will allow you indulge in the art of statistical modeling and analysis using the Python programming language.
What You Will Learn
- Look for specific signs and intents using natural language processing
- Find specific points and figures within images using computer vision
- Detect spam by analyzing data within emails
- Detect emotion by reading patterns within images
- Employ different filters and transforms to analyze and manipulate data
Table of Contents
What Did You Say Natural Language Processing
1 The Course Overview
2 Diving into NLP
3 Getting Familiar with NLTK
4 Text Preprocessing
5 Using N-Grams and Bag-of-Words Models
6 Sentiment Analysis
7 Classification with Markov Models
Spotting the Right One! Images and Computer Vision
8 Diving into Computer Vision
9 Working with OpenCV
10 Filters and Transforms
11 Finding Interesting Points
12 Learning to SIFT – Scale Invariant Feature Transform
13 Image Search
14 OCR with Tesseract
Case Study One – I Don’t Like Spam
15 Filtering Spam
16 Bring Us the Data
17 Preparing the E-Mails
18 Detecting Spam
Case Study Two – Tell Me How You Are Feeling
19 Learning to See Emotion
20 Looking at the Pictures
21 Learning to Feel – Training the Model
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