Book Image

Artificial Intelligence with Python - Sequence Learning [Video]

By : Prateek Joshi
Book Image

Artificial Intelligence with Python - Sequence Learning [Video]

By: Prateek Joshi

Overview of this book

<p>Enter and explore the fascinating world of intelligent apps with Artificial Intelligence with Python. Artificial Intelligence is becoming increasingly relevant in the modern world. By harnessing the power of algorithms, you can create apps that intelligently interact with the world around you, automatic speech recognition systems, and more.</p> <h2>Style and Approach</h2> <p>This course takes a concept-based, explanation-focused approach. Each concept is explained and then the exercise or example is implemented.</p>

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Artificial Intelligence with Python - Sequence Learning [Video]
Prateek Joshi
Dec, 2017 | 1 hour 14 minutes
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Table of Contents (3 chapters)
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1
Natural Language Processing
The Course Overview
Introduction and Installation of Packages
Tokenizing Text Data
Converting Words to Their Base Forms
Dividing Text Data into Chunks
Extracting the Frequency of Terms Using a Bag of Words Model
Building a Category Predictor
Constructing a Gender Identifier
Building a Sentiment Analyzer
Topic Modeling Using Latent Dirichlet Allocation
2
Probabilistic Reasoning for Sequential Data
Understanding Sequential Data
Handling Time-Series Data with Pandas
Slicing Time-Series Data
Operating on Time-Series Data
Extracting Statistics from Time-Series Data
Generating Data Using Hidden Markov Models
Identifying Alphabet Sequences with Conditional Random Fields
Stock Market Analysis
3
Building a Speech Recognizer
Working with Speech Signals
Visualizing Audio Signals
Transforming Audio Signals to the Frequency Domain
Generating Audio Signals
Synthesizing Tones to Generate Music
Extracting Speech Features
Recognizing Spoken Words
Chapter 3
Building a Speech Recognizer
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Section 7
Recognizing Spoken Words
Now that we have learnt all the ways to analyze speech signals, let’s build a system that can recognize spoken words. - Train the HMMs and store the models for each word - Train and test the models - Evaluate score and compare with maximum score
Previous Section
End of Chapter 3
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