In this article, you will find the Best Deep Learning Courses on Coursera & advanced like Beginner courses, and Practice test courses. So, check these Best Deep Learning Courses on Coursera and find the Best Deep Learning Courses on Coursera for Beginners to Advanced according to your need. In the previous article, I have shared the Best Free Data Analysis Courses to know in 2022, you can go through the list and enjoy reading.
Here we have covered the Best Deep Learning Courses on Coursera. Let’s go through the list of Coursera Deep Learning Courses one by one.
Best Deep Learning Courses on Coursera 2022
Deep Learning and Reinforcement Learning – IBM
This course introduces you to two of the most sought-after disciplines in Machine Learning: Deep Learning and Reinforcement Learning. Deep Learning is a subset of Machine Learning that has applications in both Supervised and Unsupervised Learning, and is frequently used to power most of the AI applications that we use on a daily basis.
First you will learn about the theory behind Neural Networks, which are the basis of Deep Learning, as well as several modern architectures of Deep Learning. Once you have developed a few Deep Learning models, the course will focus on Reinforcement Learning, a type of Machine Learning that has caught up more attention recently.
Although currently Reinforcement Learning has only a few practical applications, it is a promising area of research in AI that might become relevant in the near future.
After this course, if you have followed the courses of the IBM Specialization in order, you will have considerable practice and a solid understanding in the main types of Machine Learning which are: Supervised Learning, Unsupervised Learning, Deep Learning, and Reinforcement Learning.
Skills you will gain:
- Deep Learning
- Artificial Neural networks
- Machine Learning
- Reinforcement Learning
- Keras
Best for: Intermediate Level | Course Duration: 14hrs | Provider: IBM
Pricing: Audit free Course, $35/month
Info: Visit this course and get amazing offers on this course
Deep Learning Specialization
The Deep Learning Specialization is a foundational program that will help you understand the capabilities, challenges, and consequences of deep learning and prepare you to participate in the development of leading-edge AI technology.
In this Specialization, you will build and train neural network architectures such as Convolutional Neural Networks, Recurrent Neural Networks, LSTMs, Transformers, and learn how to make them better with strategies such as Dropout, BatchNorm, Xavier/He initialization, and more.
Get ready to master theoretical concepts and their industry applications using Python and TensorFlow and tackle real-world cases such as speech recognition, music synthesis, chatbots, machine translation, natural language processing, and more.
- 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 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 its 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
Best for: Intermediate Level | Course Duration: 3 – 5 months | Provider: Deeplearning.ai
Pricing: Audit free Course, $35/month
Info: View this course and get amazing offers on this Specialization
Neural Networks and Deep Learning
In the first course of the Deep Learning Specialization, you will study the foundational concept of neural networks and deep learning.
By the end, you will be familiar with the significant technological trends driving the rise of deep learning; build, train, and apply fully connected deep neural networks; implement efficient (vectorized) neural networks; identify key parameters in a neural network’s architecture; and apply deep learning to your own applications.
It provides a pathway for you to gain the knowledge and skills to apply machine learning to your work, level up your technical career, and take the definitive step in the world of AI.
Skills you will gain:
- Deep Learning
- Artificial neural networks
- Baackpropagation
- Python Programming
- Neural Network Architectrure
Best for: Intermediate Level | Course Duration: 29 hours | Provider: Deeplearning.ai
Pricing: Audit free Course, $35/month
Info: Visit this course and get amazing offers on this Course
Improving Deep Neural Networks: Hyperparameter Tuning, Regularization and Optimization
In the second course of the Deep Learning Specialization, you will open the deep learning black box to understand the processes that drive performance and generate good results systematically.
By the end, you will learn the best practices to train and develop test sets and analyze bias/variance for building deep learning applications; be able to use standard neural network techniques such as initialization, L2 and dropout regularization, hyperparameter tuning, batch normalization, and gradient checking; implement and apply a variety of optimization algorithms, such as mini-batch gradient descent, Momentum, RMSprop and Adam, and check for their convergence; and implement a neural network in TensorFlow.
Skills you will gain:
- Tensorflow
- Deep Learning
- Mathematical Optimization
- Hyperparameter tuning
Best for: Intermediate Level | Course Duration: 27 hours | Provider: Deeplearning.ai
Pricing: Audit free Course, $35/month
Info: Visit this course and get amazing offers on this course
TensorFlow 2 for Deep Learning Specialization
The first course of this Specialization will guide you through the fundamental concepts required to successfully build, train, evaluate and make predictions from deep learning models, validating your models and including regularisation, implementing callbacks, and saving and loading models.
The second course will deepen your knowledge and skills with TensorFlow, in order to develop fully customised deep learning models and workflows for any application. You will use lower level APIs in TensorFlow to develop complex model architectures, fully customised layers, and a flexible data workflow. You will also expand your knowledge of the TensorFlow APIs to include sequence models.
The final course specialises in the increasingly important probabilistic approach to deep learning. You will learn how to develop probabilistic models with TensorFlow, making particular use of the TensorFlow Probability library, which is designed to make it easy to combine probabilistic models with deep learning. As such, this course can also be viewed as an introduction to the TensorFlow Probability library
Best for: Intermediate Level | Course Duration: 4 months | Provider: Imperial College London
Pricing: Audit free Course, $35/month
Info: Visit this Course and Get amazing offers on this course
Probabilistic Deep Learning with TensorFlow 2
You will learn how to develop probabilistic models with TensorFlow, making particular use of the TensorFlow Probability library, which is designed to make it easy to combine probabilistic models with deep learning. As such, this course can also be viewed as an introduction to the TensorFlow Probability library.
You will learn how probability distributions can be represented and incorporated into deep learning models in TensorFlow, including Bayesian neural networks, normalising flows and variational autoencoders.
You will learn how to develop models for uncertainty quantification, as well as generative models that can create new samples similar to those in the dataset, such as images of celebrity faces.
You will put concepts that you learn about into practice straight away in practical, hands-on coding tutorials, which you will be guided through by a graduate teaching assistant. In addition there is a series of automatically graded programming assignments for you to consolidate your skills.
Best for: Intermediate Level | Course Duration: 53hours | Provider: Imperial College London
Pricing: Audit free Course, $35/month
Info: Visit this course and get amazing offers on this course
And here the list ends. So, these are the Best Deep Learning Courses on Coursera for Beginners to Advanced. I will keep adding more Best Deep Learning on Coursera to this list.
Conclusion
I hope these Best Deep Learning Courses on Coursera for Beginners to Advanced will definitely help you to enhance your skills. If you have any doubts or questions, feel free to ask me in the comment section.