What programming languages and tools are used in this course?

You’ll primarily use Python with libraries and machine learning frameworks for computer vision and speech tasks.

Do I need prior experience in AI or machine learning?

No prior AI or ML experience is required. Basic programming skills in Python are recommended to follow along effectively.

What career opportunities can AI skills in vision and speech open up?

These skills prepare you for roles in AI engineering, computer vision development, speech technology, and data science.

Will I earn a certificate after completing the course?

Yes, you’ll receive a Coursera certificate that validates your AI skills and can be showcased on LinkedIn or to employers.

What will I get if I subscribe to this Specialization?

When you enroll in the course, you get access to all of the courses in the Specialization, and you earn a certificate when you complete the work. Your electronic Certificate will be added to your Accomplishments page - from there, you can print your Certificate or add it to your LinkedIn profile.

Is financial aid available?

Yes. In select learning programs, you can apply for financial aid or a scholarship if you can’t afford the enrollment fee. If fin aid or scholarship is available for your learning program selection, you’ll find a link to apply on the description page.

AI Applications: Computer Vision and Speech Recognition

本课程是 Mastering AI: Neural Nets, Vision System, Speech Recognition 专项课程的一部分

位教师：Edureka

包含在 Coursera Plus 中

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4个模块

深入了解一个主题并学习基础知识。

中级等级

推荐体验

1 周完成

在 10 小时一周

灵活的计划

自行安排学习进度

4个模块

深入了解一个主题并学习基础知识。

中级等级

推荐体验

1 周完成

在 10 小时一周

灵活的计划

自行安排学习进度

您将学到什么

Analyze speech waveforms and apply audio signal processing techniques.
Develop and implement computer vision algorithms using OpenCV.
Perform morphological operations on images and videos for data manipulation.
Apply speech recognition techniques for digitizing and analyzing audio signals.

您将获得的技能

要了解的详细信息

可分享的证书

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作业

12 项作业

授课语言：英语（English）

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本课程是 Mastering AI: Neural Nets, Vision System, Speech Recognition 专项课程专项课程的一部分

在注册此课程时，您还会同时注册此专项课程。

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该课程共有4个模块

Welcome to AI Applications: Computer Vision and Speech Recognition, where you will gain hands-on expertise in using cutting-edge technologies to process visual data and interpret human speech. This course equips you with practical skills to address real-world challenges in computer vision and speech analysis.

By the end of this course, you will be able to: - Analyze speech waveforms using advanced audio signal processing techniques. - Develop a strong understanding of computer vision principles and applications. - Perform morphological operations on images and videos within a customized environment. - Implement advanced audio and video processing techniques. - Apply OpenCV functionalities to build robust solutions for image and video analysis. This course is ideal for AI enthusiasts, data scientists, and developers aiming to expand their skills in computer vision and speech recognition. Prior experience with Python programming and a basic understanding of machine learning concepts is recommended for optimal learning. Master the skills required to build intelligent systems in the evolving field of artificial intelligence with this focused course.

This module is designed to help learners understand the history of AI and how its ongoing development has led to the creation of image and video processing tools like OpenCV. Learn to perform various image processing such as morphological operations.

涵盖的内容

32个视频5篇阅读材料5个作业2个讨论话题1个插件

32个视频总计165分钟

Course Introduction3分钟
Industrial Breakthrough in Audio and Speech Recognition 2分钟
Speech Recognition Technology5分钟
Computer Vision Application5分钟
Computer Vision Applications: Medical and Plant Disease6分钟
AI Responsibility Pyramid6分钟
Evolution of Computer Vision and Speech Analysis4分钟
Evolution of Speech Analysis4分钟
What is OpenCV?6分钟
Installing OpenCV on Windows 3分钟
Installing OpenCV on Windows: Handling Libraries in Jupyter4分钟
Installing Integrated Libraries - NumPy, Matplotlib, SciPy, and Pillow6分钟
Installing Integrated Libraries - Dlib, Scikit, and Pytorch4分钟
Operations on OpenCV3分钟
Demonstration: Loading the Image and Encoding Image to RGB6分钟
Demonstration: Resizing, Rotating and Flipping the Image6分钟
Demonstration: Gaussian Blur6分钟
Demonstration: Edge Detection and Conversion6分钟
Demonstration: Image Thresholding - Binary Image5分钟
Demonstration: Different Methods of Thresholding6分钟
Demonstration: Practical Use Cases2分钟
What is Adaptive Thresholding4分钟
Demonstration of Global Adaptive Threshold5分钟
Demonstration: Implementing Adaptive Thresholding Methods6分钟
Morphological Operations4分钟
Morphological Operations in OpenCV3分钟
Demonstration: Opening (Dilation and Erosion)5分钟
Demonstration: Closing and Morphological Gradient 5分钟
Blackhat and Whitehat Transformations4分钟
Demonstration: Whitehat/Tophat4分钟
Demonstration: Blackhat5分钟
Summary of Computer Vision with OpenCV6分钟

5篇阅读材料总计40分钟

Welcome to AI Applications: Computer Vision and Speech Recognition10分钟
Exploring Technologies for Computer Vision10分钟
Ethical Considerations in Computer Vision10分钟
LBPH Algorithm: Local Binary Patterns Histogram5分钟
Watershed Algorithm for Image Processing5分钟

5个作业总计54分钟

Practice Quiz : Evolution of AI and Computer Vision6分钟
Practice Quiz : Setting Up Environment6分钟
Practice Quiz : Image Processing6分钟
Practice Quiz : Morphological Operations6分钟
Knowledge Check : Computer Vision with OpenCV30分钟

2个讨论话题总计20分钟

Introduce Yourself10分钟
Simplifying OpenCV Environment Setup10分钟

1个插件总计10分钟

Your Experience with AI for Vision and Speech10分钟

In the second module of this course, you'll delve deeper into OpenCV functionalities for video preprocessing. You'll learn how to play videos using OpenCV, extract and combine frames, and demonstrate the use of Haar cascades and their integration with OpenCV.

涵盖的内容

28个视频2篇阅读材料3个作业1个讨论话题

28个视频总计136分钟

Video Processing3分钟
Demonstration: Implementing Frame by Frame Video Processing4分钟
Demonstration: Exiting the Processing Operation3分钟
Demonstration: Initializing the Video Frames5分钟
Demonstration: Saving the Frames6分钟
Demonstration: Loading the Data 5分钟
Demonstration: Reading and Writing Operations 6分钟
Demonstration: Histogram Matching6分钟
Demonstration: Matching Source and Reference Images4分钟
Demonstration: Cumulative Distribution Function5分钟
Demonstration: Differences in Images4分钟
Haar Cascade1分钟
Haar Cascade: Algorithm Overview5分钟
Haar Cascade Application and Limitation2分钟
Demonstration: Implementation of Haar Cascade Algorithm6分钟
Demonstration: Face Detection Code for Static Image4分钟
Demonstration: Implementing Boundary Box for Face Detection5分钟
Demonstration: Applying Face Detection on Images5分钟
Introduction to Face Recognition5分钟
Demonstration: Setting Up Pre-requisite Libraries and Loading the Image2分钟
Demonstration: Face Recognition and Detection4分钟
Demonstration: Facial Landmark Detection with File Loading and Library Setup5分钟
Demonstration: Adjusting Video details through OpenCV5分钟
Demonstration: Face Recognition6分钟
Demonstration: Encoding Facial Landmarks 3分钟
Demonstration: Implementing Facial Landmarks on Images3分钟
Demonstration: Implementing Facial Landmarks on Videos5分钟
Summary of Video Processing with OpenCV6分钟

2篇阅读材料总计20分钟

Marker-Based Augmented Reality (AR)10分钟
Pros and Cons of OpenCV’s Haar cascade Face Detector10分钟

3个作业总计42分钟

Practice Quiz : Video Processing Using OpenCV6分钟
Practice Quiz : Exploring Various Techniques for Face Detection and Recognition6分钟
Knowledge Check : Video Processing with OpenCV30分钟

1个讨论话题总计10分钟

The Future of Face Recognition and Security: Opportunities and Challenges10分钟

In the third module of this course, you will learn the fundamental structure of speech and how it is organized. Process speech by analyzing waveforms and applying various techniques to manipulate them effectively.

涵盖的内容

27个视频2篇阅读材料3个作业1个讨论话题

27个视频总计136分钟

Introduction to Speech: Audio Data 3分钟
Introduction to Speech: Human Computer Interaction and Applications5分钟
Processing Speech 6分钟
Speech Production7分钟
Difficulties in Analyzing Speech6分钟
Working of Sound Waves4分钟
ADC and Sample Rate, Bit Rate3分钟
Conversion of ADC (Analog to Digital Converter) to DAC (Digital to Analog Converter)4分钟
Demonstration: Generating Sound7分钟
Demonstration: Spectrogram6分钟
Demonstration: Signal Frequencies Over Time4分钟
Summary of Audio File Analysis1分钟
Demonstration: Converting a Sound File into Waveform7分钟
Human Speech6分钟
Speech Waveform4分钟
Digital Signal Processing6分钟
MFCC (Mel Frequency Cepstral Coefficient)6分钟
Windowing Formula and Cepstrum4分钟
Demonstration: Computing the Spectrogram3分钟
Demonstration: Digitizing the Audio Data4分钟
Demonstration: Converting Fragmented Parts of Audio File for Speech Recognition3分钟
Voice Onset, Voice Offset, Tremor, and Noise Detection6分钟
Understanding the Concepts of Voice Onset and Offset3分钟
Tremor Detection1分钟
Demonstration: ZCR, Pitch Detection, Voice Activity Detection4分钟
Demonstration: Tremor Detection4分钟
Summary of Speech Recognition and Audio Analysis5分钟

2篇阅读材料总计20分钟

Speech Analysis In Cyber Security10分钟
Speech Processing - Interactive Creation and Evaluation (SPICE) Toolkit10分钟

3个作业总计42分钟

Practice Quiz : Speech and it's Variation6分钟
Practice Quiz : Digitizing and Analyzing Speech6分钟
Knowledge Check : Speech Recognition and Audio Analysis30分钟

1个讨论话题总计10分钟

The Role of Speech Analysis in Modern AI Applications10分钟

This module is designed to assess an individual on the various concepts and teachings covered in this course. Answer a comprehensive quiz which marks you as a learner who is confident in working with Computer Vision and OpenCV.

涵盖的内容

1个视频1篇阅读材料1个作业1个讨论话题1个插件

1个视频总计3分钟

Summary for AI Applications: Computer Vision and Speech Recognition3分钟

1篇阅读材料总计10分钟

Practice Project - Vehicle Tracking and Detection10分钟

1个作业总计30分钟

Knowledge Check : AI Applications: Computer Vision and Speech Recognition30分钟

1个讨论话题总计10分钟

Describe Your Learning Journey10分钟

1个插件总计10分钟

Reflecting on AI Applications in Vision & Speech10分钟

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常见问题

A basic understanding of Python programming and machine learning concepts is recommended.

Yes, you'll need to set up Python, OpenCV, and other relevant libraries for image, video, and speech processing.

While the course is beginner-friendly, prior Python knowledge and basic understanding of machine learning concepts will enhance your learning experience.

To access the course materials, assignments and to earn a Certificate, you will need to purchase the Certificate experience when you enroll in a course. You can try a Free Trial instead, or apply for Financial Aid. The course may offer 'Full Course, No Certificate' instead. This option lets you see all course materials, submit required assessments, and get a final grade. This also means that you will not be able to purchase a Certificate experience.