Free Deep Learning Courses from Top e-learning Platforms
An extension of machine learning, deep learning is all about neural networks, which imitate the way humans think and learn. The presence of trained deep learning experts is expected to become a crucial requirement for organizations that wish to drive innovation and remain competitive.
However, it is surprising to learn that this emerging field of deep learning and artificial intelligence is still understaffed as per the McKinsey Global Institute, and neither data scientists nor software engineers could fill in the gaps because of the absence of specific skills sets.
To help you with sharpening your skills on the fundamentals as well as the advanced concepts of deep learning, we have listed some free as well as auditable deep learning courses. Take your pick from the deep learning courses as per your professional needs.
Free Deep Learning Courses
Deep Learning for Business by Yonsei University on Coursera
Course Description
With this course, you can build business strategies and plan around DL and ML services and products. The course is divided into three parts-
- DL and ML technology based future business strategy
- Core technologies of DL and ML systems, which include Neural Networks, Convolutional NN, and Recurrent NN
- Four TensorFlow Playground projects, for gaining experience on designing DL NNs
Course Details
Duration – 6 Hours
Skill Level – Beginner
Course Contents
- Deep Learning Products & Services
- Business with Deep Learning & Machine Learning
- Deep Learning Computing Systems & Software
- Basics of Deep Learning Neural Networks
- Deep Learning Project with TensorFlow Playground
Computational Neuroscience by the University of Washington on Coursera
Course Description
One of the most popular deep learning courses, Computational Neuroscience offers an introduction to basic computational methods for understanding the functioning of the human nervous system. It explores the computational principles governing various aspects of vision, sensory-motor control, learning, and memory. It uses Matlab/Octave/Python demonstrations and exercises to offer a deeper understanding of concepts and methods.
Course Details
Duration – 26 Hours
Skill Level – Beginner
Course Contents
- Introduction & Basic Neurobiology
- What do Neurons Encode? Neural Encoding Models
- Extracting Information from Neurons: Neural Decoding
- Information Theory & Neural Coding
- Computing in Carbon
- Computing with Networks
- Networks that Learn: Plasticity in the Brain & Learning
- Learning from Supervision and Rewards
Neural Networks and Deep Learning by Coursera
Course Description
The course is a part of the Deep Learning Specialization and explores the foundations of deep learning.
Course Details
Duration – 20 Hours
Skill Level – Intermediate
Course Contents
- Introduction to deep learning
- Neural Networks Basics
- Shallow neural networks
- Deep Neural Networks
Deep Neural Networks with PyTorch by IBM on Coursera
Course Description
You will learn how to develop deep learning models using Pytorch. It covers topics like Pytorch’s tensors, automatic differentiation packages, linear regression, logistic/softmax regression, feedforward deep neural networks, convolutional neural networks and other deep learning methods.
Course Details
Duration – 33 Hours
Skill Level – Intermediate
Course Contents
- Tensor and Datasets
- Linear Regression
- Linear Regression PyTorch Waye
- Multiple Input Output Linear Regression
- Logistic Regression for Classification
- Softmax Regression
- Shallow Neural Networks
- Deep Networks
- Convolutional Neural Network
Intro to TensorFlow for Deep Learning via Udacity
Course Description
This course is a practical approach to deep learning for software developers to help create their own AI applications. With this course, you will get hands-on experience building state-of-the-art image classifiers and other deep learning models, use TensorFlow models in the real world, and use advanced techniques and algorithms to work with large datasets.
Course Details
Duration – 2 Months
Skill Level – Intermediate
Course Contents
- Introduction to Machine Learning
- Your First Model: Fashion MNIST
- Introduction to Convolutional Neural Networks (“CNNs”)
- Going Further with CNNs
- Transfer Learning
- Saving and Loading Models
- Time Series Forecasting
- Introduction to TensorFlow Lite
Deep Learning with Tensorflow by IBM on edX
Course Description
You will learn how to apply Deep Learning with TensorFlow to unstructured data for solving real-world problems. This course will also cover the basic concepts of TensorFlow, its main functions, operations, and the execution pipeline.
Course Details
Duration – 5 Weeks
Skill Level – Intermediate
Course Contents
Module 1 – Introduction to TensorFlow
Module 2 – Convolutional Neural Networks (CNN)
Module 3 – Recurrent Neural Networks (RNN)
Module 4 – Restricted Boltzmann Machine
Module 5 – Autoencoders
Deep Learning with Python and PyTorch by IBM on edX
Course Description
The course will help you to use Python and its popular libraries such as NumPy and Pandas, as well as the PyTorchDeep Learning library for building Neural Networks and Deep Learning models.
Course Details
Duration – 6 Weeks
Skill Level – Intermediate
Course Contents
Module 1 – Introduction to Pytorch
Module 2 – Linear Regression
Module 3 – Classification
Module 4 – Neural Networks
Module 5 – Deep Networks
Module 6 – Computer Vision Networks
Deep Learning in Computer Vision by National Research University Higher School of Economics on Coursera
Course Description
This course is part of the Advanced Machine Learning Specialization. It will cover topics like computer vision, modern deep learning models, image recognition, video recognition, motion estimation, human action recognition, new image generation, among others.
Course Details
Duration – 17 Hours
Skill Level – Advanced
Course Contents
- Introduction to image processing and computer vision
- Convolutional features for visual recognition
- Object detection
- Object tracking and action recognition
- Image segmentation and synthesis
Applied AI with DeepLearning by IBM on Coursera
Course Description
The course will help you to understand different aspects of deep learning and models, and their usage by experts in Natural Language Processing, Computer Vision, Time Series Analysis, and many other disciplines.
Course Details
Duration – 22 Hours
Skill Level – Advanced
Course Contents
Week 1 – Introduction to deep learning
Week 2 – DeepLearning Frameworks
Week 3 – DeepLearning Applications
Week 4 – Scaling and Deployment
Introduction to Deep Learning by National Research University Higher School of Economics on Coursera
Course Description
Introduction to Deep Learning is a part of the Advanced Machine Learning Specialization. It offers a basic understanding of modern neural networks and their applications in computer vision and natural language processing.
Course Prerequisites
- Basic knowledge of Python
- Basic linear algebra and probability
Course Details
Duration – 32 Hours
Skill Level – Advanced
Course Contents
- Introduction to optimization
- Introduction to neural networks
- Deep Learning for images
- Unsupervised representation learning
- Deep learning for sequences
- Final Project
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