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The Best Deep Learning Certification and Online Courses

Deep Learning Certification Courses

Top 3 Deep Learning Certification Programs

Deep learning is a subfield of artificial intelligence modeled after the human brain to recognize patterns and extract useful information from data. If you have an interest in advancing your career in AI and ML, here are some online deep learning certification programs.

Our Top 3 Picks

Udacity Logo
Deep Learning Nanodegree
Best project-based learning certification
Professional Certificate in Deep Learning
Best for a variety of skills learned
Deep Learning Skill Track
Best starter-level courses

1. Deep Learning Certification (Udacity)

Udacity Logo

With the Udacity Deep Learning Nanodegree program, you can learn how to build and deploy deep neural networks. Gain the skills and knowledge you need to tackle complex AI challenges like image recognition, natural language processing, and prediction.

In collaboration with AWS and Facebook, you’ll learn how to implement and deploy deep learning models using PyTorch for convolutional networks.  Overall, this program provides unique deep learning training that will teach you everything you need to know about this cutting-edge technology.


  1. Introduction to Deep Learning – Take your first steps in deep learning with this comprehensive course. Learn about the basics of machine learning by implementing your own style transfers. You will also use an array of development tools to analyze data like Anaconda and Jupyter notebooks. Get started with deep learning in this introductory course.
  2. Neural Networks – In this course, you’ll learn how to build neural networks using the latest deep learning frameworks, Python and NumPy. You’ll also build your first multi-layer neural network and analyze real data from a production system.
  3. Convolutional Neural Networks – Take the first steps in learning how to use a Convolutional Neural Network (CNN) to classify images. This course will teach you how to use a CNN to learn patterns and objects in images and how to denoise images. In the project, you will train a CNN to automatically tag photos with the most relevant captions.
  4. Recurrent Neural Networks – This course will teach you how to use PyTorch to perform sentiment analysis. But using recurrent neural networks, your model will write a script for an existing television show.
  5. Generative Adversarial Networks – Learn the basics of Generative Adversarial Networks (GANs), which is a machine learning framework that can generate new data from an existing data set. In this case, you will create realistic and new images using a GAN trained on photographs.
  6. Deploying a Sentiment Analysis Model – This course provides you with the capabilities to build a sentiment analysis model using PyTorch. The model will analyze the polarity of positive and negative sentiment. Afterward, you will deploy it, and access it from a website.

Skills Acquired

  • Deep Convolutional GAN
  • PyTorch
  • Image Recognition
  • Anaconda
  • Jupyter Notebook
  • Numpy
  • Neural Networks
  • Convolutional Networks (CNN)
  • TensorFlow
  • Generative Adversarial Network
  • Sentiment Analysis

For more information, read our review of the Udacity Deep Learning Nanodegree.

PREREQUISITES: This program requires working knowledge of Python and familiarity with statistics, calculus, and linear algebra.

2. Professional Certificate in Deep Learning (IBM)


Get certified in deep learning from the experts at IBM so you can be an expert in your field. In this program, you will take advantage of an online program where you can learn all the basics of this cutting-edge technology.

Throughout the Professional Certificate in Deep Learning program, you will learn how to use the latest deep learning libraries like Keras, PyTorch, and Tensorflow. By the end of the program, you will have the skills to apply deep learning to your own projects.


  1. Deep Learning Fundamentals with Keras – This course will teach you how to build deep learning models using the popular Keras library. By the end, you will have the skills necessary to create a drop learning model that can learn and process large amounts of data.
  2. Computer Vision and Image Processing Fundamentals – Learn about the basics of computer vision, including how to create and process images. This course provides a foundation for learning more about artificial intelligence, machine learning, and computer vision.
  3. PyTorch Basics for Machine Learning – This course is designed to get you started with PyTorch and deep learning. First, you will learn the basics of PyTorch, and how to create models and optimize them. Finally, you will learn how to apply Python libraries like pandas and Numpy with PyTorch.
  4. Deep Learning with Python and PyTorch – Understand how to create and train deep learning models with Python and PyTorch. This course is the second part of a two-part course on how to develop Deep Learning models using PyTorch.
  5. Deep Learning with Tensorflow – Learn how to apply Deep Learning to unstructured data to solve real-world problems.
  6. Applied Deep Learning Capstone Project – First, you’ll build a successful deep learning model with Keras or PyTorch. Finally, you will apply it to real-world data and validate it.

Skills Acquired

  • Deep Learning
  • Python
  • Keras
  • PyTorch
  • Tensorflow
  • Neural Networks
  • Convolutional Networks
  • Recurrent Networks
  • Computer Vision
  • Natural Language Processing

INFORMATION: This self-paced program contains 6 skill-building courses over a period of seven months.

3. Deep Learning Skill Track (DataCamp)


Get a taste of how deep learning works, and learn how to apply it in your own work. The expert trainers at DataCamp will guide you through the basics of neural networks and deep learning so you can build your own models and applications.

Throughout the Deep Learning Skill Track, you’ll work with the most popular deep learning libraries like Keras, PyTorch, and TensorFlow. By the end of the program, you will have a solid understanding of how to optimize deep learning models.

Simplified Courses

  1. Introduction to Deep Learning in Python – This course is designed to teach you the basics of deep learning in Python. You will learn how to train and deploy deep learning models, as well as how to use different deep learning libraries and frameworks – but mainly Keras.
  2. Introduction to TensorFlow in Python – Get started with TensorFlow and learn the basics of neural networks in a course that is easy to understand and learn from.
  3. Introduction to Deep Learning with PyTorch – If you want to learn how to create deep learning models with PyTorch, this course is the perfect place to start.
  4. Introduction to Deep Learning with Keras –  Learn how to build deep learning models with Keras, including improving model performance and advanced architectures.
  5. Advanced Deep Learning with Keras – Learn how to build models using the powerful frameworks of Keras. You’ll learn data flows, neural networks, regression, and more.

Skills Acquired

  • Deep Learning
  • Keras 2.0
  • TensorFlow
  • PyTorch
  • Python
  • Backward Propagation
  • MNIST Dataset
  • Convolutional Neural Networks (CNN)
  • Artificial Neural Networks
  • Model Architectures
  • Model Performance

INFORMATION: This program contains 5 courses in a timeframe of 20 hours.

The Best Deep Learning Certification and Online Programs

Deep learning is comprised of digital systems that involve training artificial neural networks. Although deep learning provides a lot of advantages over traditional machine learning models, it also poses a lot of challenges.

In this article, we discussed what deep learning certifications are, how you get them, and the benefits of each one. We hope you enjoyed and if you’ve ever tried any of them, we’d love to hear from you with a comment below.

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