deep learning course

10 Best + Free Online Deep Learning Courses

While deep learning is a subset of AI, it is now a discipline that is quickly spreading outside of the AI area. Deep learning is the evolution of neural networks, which are “thinking” computers, and its use involves coding procedures that are unfamiliar to traditional programmers. We can educate our computers to learn for themselves and deliver intriguing outcomes via deep learning. Furthermore, you will have the opportunity to be on the cutting edge, as profound learning professionals are in higher demand than ever before.
If you’re interested in learning deep learning but don’t know where to start, we’ve compiled a selection of free online courses that can help.

List of Free Online Deep Learning Courses

1. Best Deep Learning Course (Coursera)

This is clearly one of the most sought-after deep learning certificates, as Andrew Ng himself teaches the course. Andrew co-founded Coursera, a global learning platform, and formerly managed the Google Brain and Baidu AI teams. Younes Bensouda Mourri of Stanford University’s Mathematical & Computational Sciences department and Kian Katanforoosh, an Adjunct Lecturer at Stanford University, will join him as Teaching Assistants. Overall, we have no concerns in calling this the Best Deep Learning Certification currently available. The principles of deep learning, how to create neural networks, and how to manage machine learning projects will all be covered in this certification course. Sign language reading, music production, and natural language processing will be among the real-time case studies. You’ll learn how to put it all together with Python and TensorFlow in addition to the theory.

What you will learn –

  • Build and train deep neural networks, implement vectorized neural networks, identify architecture parameters, and apply DL to your applications.
  • Use best practices to train and develop test sets and analyze bias/variance for building DL applications, use standard NN techniques, apply optimization algorithms, and implement a neural network in TensorFlow.
  • Use strategies for reducing errors in ML systems, understand complex ML settings, and apply for end-to-end, transfer, and multi-task learning.
  • Build a Convolutional Neural Network, apply it to visual detection and recognition tasks, use neural style transfer to generate art, and apply these algorithms to image, video, and other 2D/3D data.
  •  Build and train Recurrent Neural Networks and their variants (GRUs, LSTMs), apply RNNs to character-level language modeling, work with NLP and Word Embeddings, and use HuggingFace tokenizers and transformers to perform Named Entity Recognition and Question Answering.

Pre-requirements –

No prerequisites are needed.

Who can take this course –

This course is open to anyone who wants to learn more and make better use of their time.

Duration – 20 weeks approx

Instructor – Andrew Ng 

2. Deep Learning Certification by IBM (edX)

During this professional certificate program, you will gain and excel at Deep Learning abilities through a series of hands-on tasks and projects. The course will culminate in a Deep Learning capstone project that will allow you to demonstrate your practical skills to potential employers. It will be available on edX, a well-known online learning platform. You’ll study the principles of Deep Learning, such as how to use several Neural Networks for supervised and unsupervised learning. Among other Deep Architectures, you’ll learn how to develop and deploy Convolutional Networks, Recurrent Networks, and Autoencoders. The lecturers for this program are Joseph Santarcangelo, Ph.D., IBM Data Scientist; Alex Aklson, Ph.D., IBM Data Scientist; and Saeed Aghabozorgi, Ph.D., IBM Sr. Data Scientists.

What you will learn –

  • Fundamental concepts of Deep Learning, including various Neural Networks for supervised and unsupervised learning.
  • Build, train, and deploy different types of Deep Architectures, including Convolutional Networks, Recurrent Networks, and Autoencoders.
  • Application of Deep Learning to real-world scenarios such as object recognition and Computer Vision, image and video processing, text analytics, Natural Language Processing, recommender systems, and other types of classifiers.
  • Master Deep Learning at scale with accelerated hardware and GPUs.
  • The use of popular Deep Learning libraries such as Keras, PyTorch, and Tensorflow applied to industry problems.

Pre-requirements –

No prerequisites are needed.

Who can take this course –

This course is open to anyone who wants to learn more and make better use of their time.

Duration – 32 weeks approx

Instructor – Romeo Kienzler,Samaya Madhavan,Saeed Aghabozorgi,Joseph Santarcangelo,Alex Aklson

3. Neural Networks and Deep Learning Certification (Coursera)              

The first course in the Deep Learning Specialization will explore the fundamentals of neural networks and deep learning.
You’ll learn how to construct, train, and implement fully connected deep neural networks, as well as how to develop efficient (vectorized) neural networks, identify critical parameters in a neural network’s architecture, and apply deep learning to your own applications at the end of the course.
The Deep Learning Specialization is a foundational program that will prepare you to contribute to the development of cutting-edge AI technology by helping you understand deep learning’s capabilities, issues, and consequences. It lays the way for you to get the knowledge and skills you’ll need to apply machine learning to your work, progress your technical career, and take your first steps into the world of artificial intelligence.

What you will learn –

  • Analyze the major trends driving the rise of deep learning, and give examples of where and how it is applied today.
  • Set up a machine learning problem with a neural network mindset and use vectorization to speed up your models.
  • Build a neural network with one hidden layer, using forward propagation and backpropagation.
  • Analyze the key computations underlying deep learning, then use them to build and train deep neural networks for computer vision tasks.

Pre-requirements –

No prerequisites are needed.

Who can take this course –

This course is open to anyone who wants to learn more and make better use of their time.

Duration –   23 hours approx

Instructor – Andrew Ng

4. Complete Guide to TensorFlow for Deep Learning Training with Python (Udemy)

This course will teach you how to use Google’s TensorFlow framework to create artificial neural networks for deep learning. This course attempts to give you an easy-to-understand introduction to Google’s TensorFlow framework’s complexity. Other tutorials and courses have avoided pure TensorFlow in favor of abstractions that provide less freedom to the user. Here, we provide a comprehensive lesson on how to use the TensorFlow framework as intended, as well as demonstrations of cutting-edge deep learning techniques!
This course combines theory and practical application with complete jupyter notebook code instructions and easy-to-reference slides and notes. We’ll have plenty of exercises to put your new skills to the test along the road!

What you will learn –

  • Understand how Neural Networks Work.
  • Build your own Neural Network from Scratch with Python.
  • Use TensorFlow for Classification and Regression Tasks.
  • Use TensorFlow for Image Classification with Convolutional Neural Networks.
  • Use TensorFlow for Time Series Analysis with Recurrent Neural Networks.
  • Use TensorFlow for solving Unsupervised Learning Problems with AutoEncoders.
  • Learn how to conduct Reinforcement Learning with OpenAI Gym.
  • Create Generative Adversarial Networks with TensorFlow.
  • Become a Deep Learning Guru!

Pre-requirements –

No prerequisites are needed.

Who can take this course –

This course is open to anyone who wants to learn more and make better use of their time.

Duration – 14  hours approx

Instructor – Jose Portilla

5. Deep Learning Nanodegree Program by aws (Udacity)

This nanodegree program can assist anyone interested in learning how to create and implement deep neural networks to handle challenges such as picture categorization and generation, time-series prediction, and model deployment. This program is tailored to students interested in pursuing careers in machine learning, artificial intelligence, or deep learning. This course will cover Deep Learning modules, AI and machine learning techniques, neural networks, and how to build a sentiment analysis model. After finishing the program with the specified assignments and projects, you will receive a certificate of completion, which you may add to your resume and share with potential employers.

What you will learn –

  • Learn neural networks basics, and build your first network with Python and NumPy. Use the modern deep learning framework PyTorch to build multi-layer neural networks, and analyze real data.
  • Learn how to build convolutional networks and use them to classify images (faces, melanomas, etc.) based on patterns and objects that appear in them. Use these networks to learn data compression and image denoising.
  • Build your own recurrent networks and long short-term memory networks with PyTorch; perform sentiment analysis and use recurrent networks to generate new text from TV scripts.
  • Learn to understand and implement a Deep Convolutional GAN (generative adversarial network) to generate realistic images, with Ian Goodfellow, the inventor of GANs, and Jun-Yan Zhu, the creator of CycleGANs.
  • Train and deploy your own PyTorch sentiment analysis model. Deployment gives you the ability to use a trained model to analyze new user input. Build a model, deploy it, and create a gateway for accessing it from a website.

Pre-requirements –

No prerequisites are needed.

Who can take this course –

This course is open to anyone who wants to learn more and make better use of their time.

Duration – 16 weeks approx

Instructor – Mat Leonard,Luis Serrano,Cezanne Camacho,Alexis Cook

6. Deep Learning Course A-Z™: Hands-On Artificial Neural Networks (Udemy)

Kirill Eremenko, Hadelin de Ponteves, and the SuperDataScience Team are professionals in deep learning, data science, and machine learning. Even basic high school mathematics will get you started with this course, and the specialists will lead you through all of the necessary knowledge and facts to become exceptional in deep learning in the 23 hours of on-demand video. You’ll learn how to utilize Artificial Neural Networks and Convolutional Networks in practice, as well as a lot more about Recurrent Neural Networks, Self-Organizing Maps, and Boltzmann Machines. Without a question, this is one of the top deep learning courses on the market.

What you will learn –

  • Understand the intuition behind Artificial Neural Networks.
  • Apply Artificial Neural Networks in practice.
  • Understand the intuition behind Convolutional Neural Networks.
  • Apply Convolutional Neural Networks in practice.
  • Understand the intuition behind Recurrent Neural Networks.
  • Apply Recurrent Neural Networks in practice.
  • Understand the intuition behind Self-Organizing Maps.
  • Apply Self-Organizing Maps in practice.
  • Understand the intuition behind Boltzmann Machines.
  • Apply Boltzmann Machines in practice.
  • Understand the intuition behind AutoEncoders.
  • Apply AutoEncoders in practice.

Pre-requirements –

No prerequisites are needed.

Who can take this course –

This course is open to anyone who wants to learn more and make better use of their time.

Duration – 22.5 hours approx

Instructor – Kirill Eremenko, Hadelin de Ponteves

7. Natural Language Processing with Deep Learning in Python(Udemy)

In this course, we’ll look at NLP (natural language processing) and deep learning.
You already know how to use simple, practical methodologies like bag-of-words and term-document matrices, as well as how many NLP challenges are simply basic machine learning and data science problems disguised.
We were able to use them to detect spam emails, compose poetry, spin articles, and group relevant phrases together, among other things.
In this course, I’ll show you how to perform even more amazing things. Not one, but four new architectures will be taught in this course.

What you will learn –

  • Understand and implement word2vec.
  • Understand the CBOW method in word2vec.
  • Understand the skip-gram method in word2vec.
  • Understand the negative sampling optimization in word2vec.
  • Understand and implement GloVe using gradient descent and alternating least squares.
  • Use recurrent neural networks for parts-of-speech tagging.
  • Use recurrent neural networks for named entity recognition.
  • Understand and implement recursive neural networks for sentiment analysis.
  • Understand and implement recursive neural tensor networks for sentiment analysis.
  • Use Gensim to obtain pre-trained word vectors and compute similarities and analogies.

Pre-requirements –

No prerequisites are needed.

Who can take this course –

This course is open to anyone who wants to learn more and make better use of their time.

Duration – 12 hours approx

Instructor – Lazy Programmer Team

8. Data Science: Deep Learning and Neural Networks in Python(Udemy)

This course will show you how to build your first artificial neural network using deep learning techniques. We take this essential building block and utilize Python and Numpy to design full-fledged non-linear neural networks right immediately, following up on my previous course on logistic regression. The materials for this course are absolutely free.
We employ the softmax function to expand the previous binary classification model to multiple classes, and we build the critical training method known as “backpropagation” using basic principles. I show how to program backpropagation in Numpy using Numpy features, first “slowly” and then “fast.”
We then construct a neural network using Google’s new TensorFlow toolkit.
You should take this course if you want to begin your journey toward becoming a deep learning specialist, or if you’re interested in machine learning and data science in general. I demonstrate something that learns properties automatically and goes beyond basic models such as logistic regression and linear regression.

What you will learn –

  • Learn how Deep Learning REALLY works (not just some diagrams and magical black box code).
  • Learn how a neural network is built from basic building blocks (the neuron).
  • Code a neural network from scratch in Python and numpy.
  • Code a neural network using Google’s TensorFlow.
  • Describe different types of neural networks and the different types of problems they are used for.
  • Derive the backpropagation rule from the first principles.
  • Create a neural network with an output that has K > 2 classes using softmax.
  • Describe the various terms related to neural networks, such as “activation”, “backpropagation” and “feedforward.

Pre-requirements –

No prerequisites are needed.

Who can take this course –

This course is open to anyone who wants to learn more and make better use of their time.

Duration – 11 hours approx

Instructor – Lazy Programmer Team

9. Natural Language Processing with Deep Learning (Stanford University)

One of the most important information-age technologies is natural language processing (NLP), often known as computational linguistics. NLP applications are widespread since people communicate practically everything through language: web search, advertising, emails, customer service, language translation, virtual agents, medical reports, and so forth. In recent years, deep learning (or neural network) techniques have achieved extremely high performance across a wide range of NLP tasks, utilizing single end-to-end neural models that do not require task-specific feature architecture. This course will teach students about cutting-edge research in Deep Learning for Natural Language Processing. Through lectures, assignments, and a final project, students will gain the required skills to build, construct, and analyze their own neural network models.

What you will learn –

  • Understand the CBOW approach.
  • Learn how to use the skip-gram approach.
  • Learn how to use word2vec’s negative sampling optimization.
  • Gradient descent and alternating least squares are used to understand and implement.
  • For parts-of-speech tagging, use recurrent neural networks.
  • For named entity recognition, use recurrent neural networks.
  • Understand how to use recursive neural networks for sentiment analysis and how to put them into practise.

Pre-requirements –

No prerequisites are needed.

Who can take this course –

This course is open to anyone who wants to learn more and make better use of their time.

Duration – 16 weeks approx

Instructor – Chris Manning

10. Deep Learning for Business by Yonsei University(Coursera)

Artificial Intelligence (AI) is built into your smartphone, smartwatch, and car (if it’s a newer model) and serves you every day. In the not-too-distant future, more advanced “self-learning” capable DL (Deep Learning) and ML (Machine Learning) technologies will be deployed in nearly every element of your organization and industry. So now is the time to learn about DL and ML and how to utilize them to the benefit of your firm. The first half of the course focuses on future business strategies based on deep learning and machine learning technology, including specifics on new cutting-edge products/services and open source deep learning software, which are the future enablers. The second section focuses on the key technologies of DL and ML systems, such as the NN (Neural Network), CNN (Convolutional NN), and RNN (Recurrent NN) systems. The third segment focuses on four TensorFlow Playground projects, where you can learn how to create DL NNs using the TensorFlow Playground, a simple, amusing, and powerful tool. This course was designed to help you develop business strategies and plan technical implementations for new DL and ML services and products.

What you will learn –

  • Deep Learning fundamentals, covering several Neural Networks for supervised and unsupervised learning.
  • Convolutional Networks, Recurrent Networks, and Autoencoders are among the Deep Architectures that can be built, trained, and deployed.
  • Object recognition and Computer Vision, image and video processing, text analytics, Natural Language Processing, recommender systems, and other types of classifiers are examples of real-world applications where Deep Learning is used.
  • Using accelerated hardware and GPUs, master Deep Learning at scale.
  • Popular Deep Learning libraries like Keras, PyTorch, and Tensorflow are used to solve real-world challenges.

Pre-requirements –

No prerequisites are needed.

Who can take this course –

This course is open to anyone who wants to learn more and make better use of their time.

Duration – 8 hours approx

Instructor – Jong-Moon Chung 

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