Introduction to Data Signal and Image Analysis with MATLAB 
Learners: 23,830
Instructor: MATLAB Programming for Engineers and Scientists Specialization 
Duration: In the fifth course of the Deep Learning Specialization, you will become familiar with sequence models and their exciting applications such as speech recognition, music synthesis, chatbots, machine translation, natural language processing (NLP), and more. You will learn how to build and train Recurrent Neural Networks (RNNs) and commonly-used variants such as GRUs and LSTMs. You will also gain experience with natural language processing and Word Embeddings, and use HuggingFace tokenizers and transformer models to solve different NLP tasks such as NER and Question Answering. By the end of the course, you will have the skills to apply sequence models to a variety of tasks. ▼
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Course Feature
Cost:
Free
Certificate:
Paid Certification
Language:
English
Course Overview
❗The content presented here is sourced directly from platform. For comprehensive course details, including enrollment information, simply click on the 'Go to class' link on our website.
Updated in [May 30th, 2023]
Welcome to Introduction to Data, Signal, and Image Analysis with MATLAB! This course provides an introduction on how to use MATLAB for data, signal, and image analysis. After completing the course, learners will gain a comprehensive understanding of how to use MATLAB for data, signal, and image analysis. This knowledge can be applied to a variety of fields, such as engineering, computer science, and data science.
By learning this course, users can develop their career or education in many possible paths. For example, they can use MATLAB to develop machine learning models for data classification and prediction, or to perform data visualization for high dimensional datasets. They can also use MATLAB to perform image processing and analysis methods, such as image filtering and image segmentation, or to perform common signal analysis tasks, such as filter design and frequency analysis.
In addition, users can also learn related topics to further their knowledge. For example, they can learn more about machine learning algorithms, data visualization techniques, image processing methods, and signal analysis techniques. They can also learn more about MATLAB programming and how to use MATLAB for data analysis. With this knowledge, users can develop their career or education in many possible paths.
Pros & Cons
Instructor provides helpful feedback.
Comprehensive introduction to data, signal and image analysis.
Fast paced course with ample video presentations.
Videos are too long.
Lack of explanation of why certain tasks are done.
No peer reviewed assignments.
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