Convolutional Neural Networks (CNN) Courses and Certifications

Classify Images with TensorFlow Convolutional Neural Networks

This is a self-paced lab that takes place in the Google Cloud console. In this lab, you'll learn about how to use convolutional neural networks to improve your image classification models.

• Coursera
• 1 hour 15 minutes
• Self-Paced
• Paid Course

Learning Deep Learning: Unit 2

Master advanced deep learning with CNNs, RNNs, and Transformers for image classification, NLP, and machine translation using TensorFlow and PyTorch.

• Coursera
• 7 hours 40 minutes
• Self-Paced
• Paid Course

Packt

Deep Learning: Convolutional Neural Networks with TensorFlow

Unlock the potential of deep learning by mastering Convolutional Neural Networks and Transfer Learning with hands-on TensorFlow and Keras experience for image and text classification.

• Coursera
• 5 hours 14 minutes
• Self-Paced
• Paid Course

Packt

Advanced Machine Learning, Big Data, and Deep Learning

Master advanced ML techniques, big data processing with Apache Spark, and deep learning using TensorFlow/Keras for real-world AI applications and model deployment.

• Coursera
• 10 hours 45 minutes
• Self-Paced
• Paid Course

Programming Generative AI: Unit 2

Explore advanced generative AI techniques for images and text using CNNs, autoencoders, diffusion models, and transformers with hands-on PyTorch and Hugging Face implementation.

• Coursera
• 8 hours 26 minutes
• Self-Paced
• Paid Course

Packt

Deep Learning with Keras and Practical Applications

Master deep learning with Keras: from wine quality prediction to advanced image classification using CNNs, transfer learning, and GPU acceleration. Optimize models, implement data augmentation, and deploy in real-world scenarios.

• Coursera
• 9 hours 44 minutes
• Self-Paced
• Paid Course

Packt

Mastering Image Segmentation with PyTorch

Explore image segmentation with PyTorch, from basics to advanced techniques. Learn CNNs, semantic segmentation, and tackle real-world projects to master this crucial aspect of computer vision.

• Coursera
• 6 hours 29 minutes
• Self-Paced
• Paid Course

Advanced Healthcare Analytics

Master neural networks, deep learning imaging, and clinical NLP to solve high-value healthcare problems while ensuring responsible AI deployment in regulated environments.

• Coursera
• 8 hours 7 minutes
• Self-Paced
• Paid Course

Packt

AI Engineering and Deployment

Master TensorFlow, CNNs, RNNs, and AI agent frameworks like AutoGPT to build, deploy, and scale ML models with ethical considerations for real-world applications.

• Coursera
• 8 hours 58 minutes
• Self-Paced
• Paid Course

Packt

Deep Learning, NLP, and AI Applications

Explore neural networks, CNNs, RNNs, and transformers to build AI applications for image classification, NLP tasks, and real-world projects using TensorFlow and PyTorch.

• Coursera
• 18 hours 11 minutes
• Self-Paced
• Paid Course

Whizlabs

Fundamentals of Deep Learning

Master neural networks, CNNs, and transfer learning through hands-on training and evaluation of deep learning models for real-world image classification problems.

• Coursera
• 5 hours 30 minutes
• Self-Paced
• Paid Course

Introduction to CNN Training

Discover CNN fundamentals and build practical image recognition skills through hands-on labs covering convolution, ReLU, pooling layers, and real-world classification projects.

• Coursera
• 2 hours 32 minutes
• Self-Paced
• Paid Course

EDUCBA

Face Recognition with Keras: Detect & Classify

Discover how to build advanced face recognition systems using Keras, from CNN foundations and MTCNN detection to FaceNet embeddings and real-world deployment strategies.

• Coursera
• 5 hours 35 minutes
• Self-Paced
• Paid Course

Packt

Deep Learning & Modern AI Architectures

Master cutting-edge neural networks, CNNs, RNNs, and Transformers with hands-on projects in TensorFlow, Keras, and PyTorch for real-world AI applications.

• Coursera
• 15 hours 19 minutes
• Self-Paced
• Paid Course

Coursera

Deep Learn Imagery

Master deep learning for satellite imagery—fine-tune CNNs, apply transfer learning, boost performance with data augmentation, and interpret predictions using Grad-CAM for land cover classification.

• Coursera
• 2 hours 13 minutes
• Self-Paced
• Paid Course