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Updated in [September 19th, 2023]

What does this course tell?
(Please note that the following overview content is from the original platform)

This course is part of the Advanced and Applied AI on Microsoft Azure ExpertTrack helping you develop AI and machine learning skills and prepare you for the relevant Microsoft microcredentials

This course will teach you the fundamentals of the components of a modern Automatic Speech Recognition (ASR) system You'll then put this knowledge into practice by building your own speech recognition system almost entirely out of Python code a powerful tool used across AI and data science practices

Delve into Automatic Speech Recognition

When a human speaks a word they cause their voice to make time-varying patterns of sounds and waves of pressure that spread through the air

During this course you'll understand how these sounds are captured by a sensor turned into a sequence of numbers and how an automatic speech recognition system converts this into a textural representation of what was said

You'll delve into the components of ASR as well as the fundamental theory and background of speech recognition

Build your own speech recognition system

You'll identify the different models and problems when designing Speech Recognition Systems as you build your own This hands-on approach will help you identify the different components of speech decoding and demonstrate your knowledge and understanding of techniques such as Advanced Acoustic Modelling

During each lab you'll build a different functioning block of the system and by the end of the course you will have built a speech recognition system almost entirely out of Python code a powerful tool that you can use across your data science practices

This course is aimed at anyone with an understanding of data analysis who has created models using machine learning Individuals who will benefit from this course include:


Data Analysts

Machine Learning Engineers

Deep Learning Engineers

Many more professionals involved in the development of AI-based technologies

We consider the value of this course from multiple aspects, and finally summarize it for you from three aspects: personal skills, career development, and further study:
(Kindly be aware that our content is optimized by AI tools while also undergoing moderation carefully from our editorial staff.)

What skills and knowledge will you acquire during this course?
During this course, the learner will acquire the skills and knowledge necessary to understand and build a modern Automatic Speech Recognition (ASR) system. They will learn about the components of ASR and the theory and background of speech recognition. The learner will also gain hands-on experience by building their own speech recognition system using Python code, a powerful tool in AI and data science practices. They will learn about different models and problems in designing Speech Recognition Systems, and will demonstrate their understanding of techniques such as Advanced Acoustic Modelling. By the end of the course, the learner will have developed a speech recognition system almost entirely out of Python code, which they can apply in their data science practices. This course is suitable for individuals with a background in data analysis and experience in creating models using machine learning, including Data Analysts, Machine Learning Engineers, Deep Learning Engineers, and other professionals involved in the development of AI-based technologies.

How does this course contribute to professional growth?
This course on Applied Artificial Intelligence: Speech Recognition Systems contributes significantly to professional growth. By enrolling in this course, individuals can enhance their AI and machine learning skills, which are highly sought after in the professional world.

Through this course, individuals will gain a deep understanding of the components of a modern Automatic Speech Recognition (ASR) system. They will learn how to capture sounds, convert them into numerical representations, and ultimately convert them into textual representations of what was said. This knowledge is crucial in the field of AI and data science, as speech recognition systems are widely used in various applications.

Moreover, the course offers a hands-on approach, allowing individuals to build their own speech recognition system using Python code. This practical experience not only reinforces the theoretical concepts learned but also demonstrates the individual's ability to apply their knowledge in real-world scenarios. Building a speech recognition system from scratch showcases their proficiency in techniques such as Advanced Acoustic Modelling and their understanding of the different components of speech decoding.

By completing this course, professionals can expand their skill set and become proficient in an essential tool like Python, which is widely used in data science practices. This proficiency opens up new opportunities for individuals in their careers, as Python is a powerful tool in the development of AI-based technologies.

Overall, this course on Applied Artificial Intelligence: Speech Recognition Systems contributes to professional growth by providing individuals with the necessary knowledge, skills, and practical experience to excel in the field of AI and data science. It equips them with the expertise required to develop and implement speech recognition systems, making them valuable assets in the development of AI-based technologies.

Is this course suitable for preparing further education?
Yes, this course is suitable for preparing further education. It is part of the Advanced and Applied AI on Microsoft Azure ExpertTrack, which helps individuals develop AI and machine learning skills and prepares them for relevant Microsoft microcredentials. The course teaches the fundamentals of Automatic Speech Recognition (ASR) systems and allows learners to build their own speech recognition system using Python code. This hands-on approach helps learners understand the different components of speech decoding and demonstrates their knowledge and understanding of techniques such as Advanced Acoustic Modelling. The course is aimed at individuals with an understanding of data analysis who have created models using machine learning, making it suitable for professionals involved in the development of AI-based technologies.