Hands On Training

Spoken Language Processing in Python

DataCamp

(3)

Course Features

Duration

4 hours

Delivery Method

Online

Available on

Limited Access

Accessibility

Mobile, Desktop, Laptop

Language

English

Subtitles

English

Level

Intermediate

Teaching Type

Self Paced

Video Content

4 hours

Course Description

Before we can read, it is important to learn how speak before we can read. In today's digital age, speech is still the primary mode of communication. Spoken Language Processing allows you to load, convert, and transcribe audio files in Python. This article will show you how Python handles raw sound. Next, we'll show you how Python handles raw audio.

Highlights

Pedagogy

Top 30 Percentile

Rating & Reviews

Top 30 Percentile

Parameters

Pedagogy

Acquire all major Python Programming skills in this course for seamless integration into your daily life. Develop a versatile skill set, allowing you to confidently apply what you've learned in various practical scenarios, enhancing your daily experiences and overall proficiency. An exceptional course in Python Programming, this stands out for its Self Paced learning approach. Learners have the flexibility to progress at their own speed, tailoring the experience to their individual needs.

Rating & Reviews

This highly acclaimed course is among the top-rated in Python Programming, boasting a rating greater than 4 and an overall rating of 5.0. Its exceptional quality sets it apart, making it an excellent choice for individuals seeking top-notch learning experience in Python Programming.

Course Overview

Virtual Labs

International Faculty

Post Course Interactions

Hands-On Training,Instructor-Moderated Discussions

Skills You Will Gain

Prerequisites/Requirements

Intermediate Python

Introduction to Natural Language Processing in Python

Supervised Learning with scikit-learn

What You Will Learn

Learn to load, transform, and transcribe human speech from raw audio files in Python

You'll start by seeing what raw audio looks like in Python. And then finish by working through an example business use case, transcribing and classifying phone call data

You'll learn the first steps to working with speech files by converting two different audio files into soundwaves and comparing them visually

In this section, you'll learn how to use the SpeechRecognition library to easily start converting the spoken language in your audio files to text

Then you'll perform sentiment analysis using NLTK, named entity recognition using spaCy and text classification using scikit-learn on the transcribed text

Course Content

Expand all sections

Module 1: Introduction to Spoken Language Processing with Python

Module 2: Using the Python SpeechRecognition library

Module 3: Manipulating Audio Files with PyDub

Module 4: Processing text transcribed from spoken language

Course Instructors

Daniel Bourke

Machine Learning Engineer and YouTube creator

Machine Learning Engineer who creates YouTube videos and writes about the intersection of health, technology and art.

Course Reviews

Average Rating Based on 3 reviews

5.0

100%