161 Deep Learning courses

Kickstart your journey into deep learning and gain a strong understanding of deep neural networks through practical exercises. Develop your intuition and learn the fundamentals of artificial neural networks, activation functions, and loss functions. Gain practical experience with Python and TensorFlow 2.x, and apply your skills to build powerful deep learning models.

This course is designed around three main activities for getting better results with deep learning models: better or faster learning, better generalization to new data, and better predictions when using final models. Take this course if you're passionate about deep learning with a solid foundation in this space and want to learn how to squeeze the best performance out of your deep learning models.

Let your AI journey transcend new heights with our Deep Learning & Neural Networks Python - Keras course, your best path to mastering deep learning.

Learn to build highly sophisticated deep learning and Computer Vision applications with PyTorch

Unleash the power of deep learning neural networks with R! Learn to build and train models to solve real problems in agriculture, war deaths, and more.

You will learn Python-based deep learning and machine learning techniques through this course. With numerous real-world case studies, we will go over all the mathematics needed to master deep learning algorithms. We will study Backpropagation, Feed Forward Network, Artificial Neural Networks, CNN, RNN, Transfer Learning, and more.

Would you like to master deep learning and artificial neural networks? Then this course is for you! Learn how to build, train, and deploy ANNs to solve problems.

Use deep learning, Computer Vision, and machine learning techniques to build an autonomous car with Python

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.

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