Ever wondered how machines recognise faces, detect traffic signs, or even tag photos with uncanny accuracy? This course dives straight into the heart of Convolutional Neural Networks (CNNs) – the very engine behind image recognition and deep learning breakthroughs. With a clear focus on project-based learning, you’ll explore how CNNs work, how they’re built, and how they’re trained to see and interpret the world digitally. The content flows logically and stays rooted in clarity, making even the most complex architectures feel almost polite.
This is not just a sequence of slides and jargon. It’s a well-structured digital journey tailored for learners who want to confidently grasp how deep learning models behave and evolve. Whether you're brushing up on your neural network knowledge or aiming to reinforce your AI expertise, the course serves up algorithms, code walkthroughs and layered insights with a tone that’s informative, direct, and occasionally dry-witted. If you fancy turning raw data into pixel-level predictions using nothing but code, logic, and neural layers — you’re exactly where you need to be.
Learning Outcomes:
Gain a solid understanding of convolutional neural networks and their applications in deep learning.
Learn how to install the necessary packages and set up a dataset structure for deep learning projects.
Discover how to create your own convolutional neural network model and layers using Python.
Understand how to preprocess and augment data for advanced image recognition tasks.
Learn how to evaluate the accuracy of your models and understand the different models available for deep learning projects.
The Deep Learning Projects - Convolutional Neural Network course is designed to provide you with the skills and knowledge you need to build your own advanced deep learning projects. Using Python, you'll learn how to install the necessary packages, set up a dataset structure, and create your own convolutional neural network model and layers. You'll also learn how to preprocess and augment data to enhance the accuracy of your models and evaluate the performance of your models using data generators.
Deep Learning Projects - Convolutional Neural Network Course Curriculum
Section 01: Introduction
Section 02: Installations
Section 03: Getting Started
Section 04: Accuracy
How is the course assessed?
Upon completing an online module, you will immediately be given access to a specifically crafted MCQ test. For each test, the pass mark will be set to 60%.
Exam & Retakes:
It is to inform our learners that the initial exam for this online course is provided at no additional cost. In the event of needing a retake, a nominal fee of £9.99 will be applicable.
Certification
Upon successful completion of the assessment procedure, learners can obtain their certification by placing an order and remitting a fee of __ GBP. £9 for PDF Certificate and £15 for the Hardcopy Certificate within the UK ( An additional £10 postal charge will be applicable for international delivery).
CPD
10 CPD hours / points Accredited by CPD Quality Standards
Who is this course for?
Data analysts who want to expand their skills in deep learning and convolutional neural networks.
Programmers who want to learn how to build advanced models for image recognition.
Entrepreneurs who want to develop their own deep learning-based applications for image recognition.
Students who want to enhance their skills in deep learning and prepare for a career in the field.
Anyone who wants to explore the world of convolutional neural networks and deep learning projects.
Career path
Data Analyst: £24,000 - £45,000
Machine Learning Engineer: £28,000 - £65,000
Computer Vision Engineer: £30,000 - £70,000
Technical Lead: £40,000 - £90,000
Chief Technology Officer: £90,000 - £250,000
Certificates
Certificate of completion
Digital certificate - £9
You can apply for a CPD Accredited PDF Certificate at the cost of £9.
Certificate of completion
Hard copy certificate - £15
Hard copy can be sent to you via post at the expense of £15.