Signal Processing in AI
INTERMEDIATE COURSE
Expand your skills in AI and explore Neural Networks! Our course will take you from beginner to intermediate knowledge of AI, Deep Learning and Signal Processing.
You’ll learn about time series analytics, how to use them in AI applications and see the application of some Deep Learning techniques and how to apply Deep Learning theoretical principles.
💰 Price: £120
🎓 Certificate included
🚀 13 – 19 age range
💻 100% online
📆 Flexible, self-paced learning
🎯 Practical challenges and quizzes
⏰ 12+ Hours to complete (6 weeks, 2 hrs per week)
Learning grants available
We are committed to widening access to tech and AI. If course funding is difficult please complete the form below and we will be in touch soon.
Course Overview
Tools You Need
Notebook Environment
Laptop/Computer
Internet Connection
STAY AHEAD OF THE CURVE
What you will learn
Signal Transformation
Learn how to analyse and transform signals using Fourier Transform, Wavelet Transform and Chirplet Transform.
Unsupervised Learning
Learn about unsupervised learning and see how this can be applied to cluster different time series.
Skill Development
Fine-tune your problem-solving and critical thinking skills.
Weather Forecasting
Use a Climate Time Series to forecast the weather with ARMA, SARMA and SARIMA models. A theoretical definition of the models will be given and their applications will be shown.
Deep Learning
Learn what Deep Learning is and will understand its basic principles. Use Deep Learning methods to denoise a Time Series and compress its information.
Project Building
Apply your skills by building a machine learning project using real-world scenarios.
Meet Your Instructor
Piero Paialunga
Machine Learning Engineer, Gen Nine Inc.
Artificial Intelligence and Climate Change Writer
Machine Learning + Python | Aerospace Engineering | Climate Change
A PhD student in Aerospace Engineering at the University of Cincinnati with a Master’s Degree in Physics and Data Scientist, Piero Paialunga is an expert in his field who now works as a Machine Learning Engineer at Gen Nine Inc.
Skilled in Machine Learning and Data Science, Piero takes complex systems and unpacks them easily. Among his numerous accolades, Piero is one of six students to have been selected as a UC Space Research Institute Fellow.
Course Outline
EXPLORE EACH MODULE
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INTRODUCTION
In this course, we will start with a more rigorous definition of a Fourier Transform. And then explore the concept of Wavelet Transform and Chirplet Transform.
CLIMATE TIME SERIES
We will use a Climate Time Series model to forecast the weather. We will use ARMA, SARMA, and SARIMA models to do so.
A theoretical definition of these models will be given, along with their applications.
UNSUPERVISED LEARNING AND TIME SERIES
In this part of the course, we will explore an application of unsupervised learning to time series. This is a chance for you to learn about unsupervised learning and use it to cluster different time series.
TOOLS FOR WORKING WITH SIGNALS
When dealing with data, there are essential tools and concepts you will need to know and use. You will learn the basic ideas of a Fourier Transform, denoising techniques, and enveloping.
DEEP LEARNING AND TIME SERIES
Deep learning is a branch of machine learning that builds neural networks. It works by finding patterns in data, which can be used to make decisions.
You will learn about deep learning and its basic principles. Then, you will use it to denoise time series and compress its information.
PROJECT BUILDING
Get ready to take the skills you’ve learned and put them to the test in an exciting real-world project! You’ll be diving into the world of Signal Processing and using cutting-edge techniques to analyse and transform signals in ways you never thought possible!
Revolutionise your Skills with Deep Learning
EMBRACE THE FUTURE OF DATA ANALYSIS
Neural networks have been shown to be a powerful tool of predicting the next event in a signal.
For the first time, signal processing can use neural networks which learn from signal examples and make predictions even if they have no previous experience.
They have proven extremely useful – serving as a base for traditional signal processing and a revolutionary new tool for the next generation of signal processing applications.
“It was an amazing opportunity to develop new skills, learn from experts in the field and connect with peers from all over the world.”
“The course was very well-structured and the instructor was knowledgeable, supportive, and engaging. He made learning these complex topics enjoyable and approachable.”
“The practical exercises and hands-on projects helped me to understand the concepts I learned in real-world situations.”
Frequently asked questions
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HOW DO YOUR PAYMENTS AND REFUNDS WORK?
Our payment and refund process is designed to be straightforward and customer-friendly through a secure payment gateway. All payments made are the country specific equivalent of the Great Britain Pound (GBP). For the most accurate and up-to-date details on our refund policy, refer to our “Terms & Conditions of Business.”
WHAT SKILLS ARE REQUIRED TO START THIS COURSE?
Basic understanding of AI and Machine Learning, some familiarity of Python and working with IDE’s and how to use Python Libraries.
CAN I ATTEND THIS COURSE IF I HAVEN’T COMPLETED THE BEGINNER COURSE?
Yes, you can! However, we recommend you start with the ‘Introduction to Signal Processing and AI Course’ as it will give you all the skills you need to comfortably complete the intermediate course.
WILL THERE BE ANY PROJECTS OR ASSESSMENTS?
The course will put the theory into practice, and provides various activities to do, including building a signal processing project using all the techniques covered in the course.
WHO SHOULD I CONTACT IF I HAVE MORE QUESTIONS
For more information, please contact Alanna at [email protected]
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