Are you looking for the Best Neural Network Courses?… If yes, you are in the right place. In this article, I have listed the 15 Best Neural Network Courses & Certifications.
So, give a few minutes and find the Best Neural Network Courses. You can bookmark this article so that you can refer to this article later.
Now without any further ado, let’s get started-
Best Neural Network Courses
- 1. Neural Networks and Deep Learning
- 2. Deep Learning– Udacity
- 3. Deep Learning A-Z™: Hands-On Artificial Neural Networks– Udemy
- 4. Intro to Deep Learning with PyTorch– Udacity FREE Courses
- 5. Introduction to Deep Learning & Neural Networks with Keras– Coursera
- 6. Deep Neural Networks with PyTorch– Coursera
- 7. Intro to TensorFlow for Deep Learning– Udacity FREE Course
- 8. Introduction to Deep Learning-edX
- 9. Deep Learning in Python– Datacamp
- 10. Deep Learning: Convolutional Neural Networks in Python– Udemy
- 11. Convolutional Neural Networks- Coursera
- 12. Complete Guide to TensorFlow for Deep Learning with Python– Udemy
- 13. Deep Learning with Python and PyTorch- edX
- 14. Introduction to Deep Learning- Coursera
- 15. Introduction to Deep Learning with PyTorch– DataCamp
- FAQ
1. Neural Networks and Deep Learning
Provider- deeplearning.ai
Instructor- Andrew Ng, Younes Bensouda Mourri, Kian Katanforoosh
Rating- 4.9/5
Time to Complete- 20 hours
This course is the part of Deep Learning Specialization program. And I think this is the best course to begin your deep learning journey.
In this course, you will understand the major technology trends driving Deep Learning, key parameters in a neural network’s architecture, how to build, train, and apply fully connected deep neural networks, etc.
Extra Benefits-
- You will get a Shareable Certificate.
- Along with that, you will get Course Videos & Readings, Practice Quizzes, Graded Assignments with Peer Feedback, Graded Quizzes with Feedback, and Graded Programming Assignments.
Who Should Enroll?
- Those who have a basic understanding of machine learning.
Interested to Enroll?
If yes, you can enroll here-> Neural Networks and Deep Learning
2. Deep Learning– Udacity
Rating- 4.7/5
Time to Complete- 4 months (If you spend 12 hours per week)
This Nano-Degree program from Udacity will give you a complete understanding of Deep Learning. In this program, you will build convolutional networks for image recognition, recurrent networks for sequence generation, generative adversarial networks for image generation.
You will also learn how to implement gradient descent and backpropagation using NumPy matrix multiplication, how to prevent overfitting of training data and minimize the error of a network, how to define and train neural networks for sentiment analysis, etc.
This Nanodegree program will also teach you how to use Amazon’s GPUs to train neural networks faster. The instructor Sebastian Thrun will explain about detecting skin cancer with CNN. There are 5 courses in this Nanodegree program. Now let’s see the details of the courses-
Courses Include-
- Neural Networks
- Convolutional Neural Networks
- Recurrent Neural Networks
- Generative Adversarial Networks
- Updating a Model
Extra Benefits-
- You will get a chance to work on Real-world projects.
- You will get Technical mentor support.
- Along with that, you will get Resume services, GitHub review, and LinkedIn profile review.
Who Should Enroll?
- Those who have intermediate-level Python programming knowledge and experience with NumPy and pandas.
- And those who have math knowledge, including- algebra and some calculus.
Interested to Enroll?
If yes, then check it out here– Deep Learning (Udacity)
3. Deep Learning A-Z™: Hands-On Artificial Neural Networks– Udemy
Rating- 4.5/5
Time to Complete- 22.5 hours
This is another best courses to learn deep learning. In this course, you will learn Artificial neural networks, convolutional neural networks, recurrent neural networks, self-organizing maps, Boltzmann machines, AutoEncoders, and the basics of regression and classification.
Throughout this course, you will work on Real-World datasets, to solve 6 Real-World business problems- Customer Churn problem, Image Recognition, Stock Price Prediction, Fraud Detection, and Recommender Systems. The instructor of this course, Kirill is an amazing instructor and explains each topic very clearly.
Extra Benefits-
- You will get a Certificate of Completion.
- You will also get 37 articles and 5 downloadable resources.
- Along with that, you will get lifetime access to the course material.
Who Should Enroll?
- Those who have basic Python programming knowledge and High school mathematics.
Interested to Enroll?
If yes, then check it out here– Deep Learning A-Z™: Hands-On Artificial Neural Networks
4. Intro to Deep Learning with PyTorch– Udacity FREE Courses
Time to Complete- 2 Months
Rating- NA
This is a free deep-learning online course. In this course, there are 9 lessons. The course begins with the neural network and PyTorch basics.
After that, you will learn convolutional neural network basics such as Applications of CNNs, Loss & Optimization, Defining a Network in PyTorch, Training the Network, Convolutional Layer, etc.
Next, you will learn the Recurrent Neural Networks and the Basics of LSTM. At the end of this course, you will build a model that can read some text and make a prediction about the sentiment of that text, whether it is positive or negative using RNN.
Overall, this course is a very in-depth course to learn deep learning using PyTorch. The course doesn’t cover only theory, which is the best part of this course. There are various quizzes and exercises in this course.
Who Should Enroll?
- Those who are comfortable with Python and data processing libraries such as NumPy and Matplotlib.
Interested to Enroll?
If yes, then start learning- Intro to Deep Learning with PyTorch
5. Introduction to Deep Learning & Neural Networks with Keras– Coursera
Rating- 4.7/5
Provider- IBM
Time to Complete- 8 hours
This course is best for beginners who are planning to start their careers in deep learning. In this course, you will learn the basics of deep learning and get to know about deep learning and artificial neural networks.
This course will teach you how to build your first deep-learning model with the Keras library. If you are an absolute beginner, then you should begin your deep learning journey with this course.
Now, let’s see the syllabus of the course-
Syllabus of the Course-
- Introduction to Neural Networks and Deep Learning
- Artificial Neural Networks
- Keras and Deep Learning Libraries
- Deep Learning Models
- Course Project
Extra Benefits-
- You will get Course Certificates upon completion.
- Along with this, you will get Course Videos & Readings, Practice Quizzes, Graded Assignments with Peer Feedback, Graded Quizzes with Feedback, and Graded Programming Assignments.
Who Should Enroll?
- Those who are absolute beginners in deep learning.
Interested to Enroll?
If yes, then start learning- Introduction to Deep Learning & Neural Networks with Keras
6. Deep Neural Networks with PyTorch– Coursera
Rating- 4.4/5
Provider- IBM
Time to Complete- 31 hours
This is a very informative course offered by IBM. In this course, you will learn how to build deep learning models by using PyTorch. This course contains a lot of content that is simple and easy to understand.
At the beginning of the course, you will learn Pytorch’s tensors and Automatic differentiation package. As the course move, you will learn fundamentals of deep learning with PyTorch such as Linear Regression, logistic regression, Feedforward deep neural networks, different activation function roles, normalization, dropout layers, convolutional Neural Networks, Transfer learning, etc.
In short, this is the best course for those who want to learn PyTorch for deep learning. Now let’s see the syllabus of the course-
Syllabus of the Course-
- Tensor and Datasets
- Linear Regression
- Linear Regression PyTorch Way
- Multiple Input Output Linear Regression
- Logistic Regression for Classification
- Softmax Rergresstion
- Shallow Neural Networks
- Deep Networks
- Convolutional Neural Network
- Peer Review
Extra Benefits-
- You will get a Shareable Certificate upon completion.
- Along with this, you will get Course Videos & Readings, Practice Quizzes, Graded Assignments with Peer Feedback, Graded Quizzes with Feedback, and Graded Programming Assignments.
Who Should Enroll?
- Those who have previous knowledge of Python and machine learning.
Interested to Enroll?
If yes, then check out all details here- Deep Neural Networks with PyTorch
7. Intro to TensorFlow for Deep Learning– Udacity FREE Course
Time to Complete- 2 months
Rating- NA
In this course, first, you will learn machine learning basics. After that, you will be introduced to the Fashion MNIST dataset and understand the neural network.
This course will also cover convolutional neural networks in detail. Next, you will learn transfer learning, time-series forecasting, NLP, and TensorFlow Lite. You will also learn how to use TensorFlow lite to build machine learning apps on Android, iOS, and IoT devices.
Who Should Enroll?
- Those who know Python programming and basic algebra.
Interested to Enroll?
If yes, then start learning- Intro to TensorFlow for Deep Learning
8. Introduction to Deep Learning-edX
Time to Complete- 16 weeks
Rating- NA
The course material this course is freely available, but for a certificate, you have to pay. Which I think is not required. In this course, you will learn the fundamental concepts of deep learning.
You will learn the types of neural networks, Convolutional Neural Networks (CNN), Recurrent Neural Networks, Bias, and Discrimination in Machine Learning.
Overall, this is not a very detailed course but good for understanding the basics of deep learning.
Who Should Enroll?
- Those who have programming knowledge and mathematics (linear algebra, statistics) knowledge.
Interested to Enroll?
If yes, then start learning- Introduction to Deep Learning
9. Deep Learning in Python– Datacamp
Time to Complete- 20 hours
Type- Skill Track
This is a skill track offered by Datacamp. In this skill track, there are 5 courses. In these 5 courses, you will learn the fundamentals of neural networks, and how to use deep learning with Keras 2.0, TensorFlow 2.4, and PyTorch.
You will also learn about convolutional neural networks and how to use them to build much more powerful models which give more accurate results. Throughout these courses, you will learn how to accurately predict housing prices, credit card borrower defaults, and images of sign language gestures.
In the last course, you will learn some advanced topics such as category embeddings and multiple-output networks.
Who Should Enroll?
- Those who have previous knowledge in Machine Learning and Python Programming.
Interested to Enroll?
If yes, then check it out here– Deep Learning in Python
10. Deep Learning: Convolutional Neural Networks in Python– Udemy
Rating- 4.7/5
Time to Complete- 12 hours
This course is focused on Convolutional Neural networks (CNN), a powerful algorithm of Deep Learning. In this course, you will learn the fundamentals of CNN and how to build a CNN using Tensorflow 2.
This course will also teach how to do image classification in Tensorflow 2 and how to use Embeddings in Tensorflow 2 for NLP.
Throughout this course, you will work in Numpy, Matplotlib, and Tensorflow libraries. This course is based on a practical approach.
Extra Benefits-
- You will get a Certificate of Completion.
- Along with that, you will get lifetime access to the course material.
Who Should Enroll?
- Those who have an understanding of basic math (taking derivatives, matrix arithmetic, probability).
Interested to Enroll?
If yes, then check it out here– Deep Learning: Convolutional Neural Networks in Python
11. Convolutional Neural Networks– Coursera
Rating- 4.9/5
Provider- deeplearning.ai
Time to Complete- 20 hours
This course will teach you the key features and concepts that are required to build CNN(convolutional neural network). In this course, you will learn how to apply convolutional networks to visual detection and recognition tasks.
This course is combined with small assessments or questions which makes it easier to follow along. In a nutshell, this course is best to start out in the world of CNN.
Now, let’s see the syllabus of the course-
Syllabus of the Course-
- Foundations of Convolutional Neural Networks
- Deep convolutional models: case studies
- Object detection
- Special applications: Face recognition & Neural style transfer
Extra Benefits-
- You will get Course Certificates upon completion.
- Along with that, you will get Course Videos & Readings, Practice Quizzes, Graded Assignments with Peer Feedback, Graded Quizzes with Feedback, and Graded Programming Assignments.
Who Should Enroll?
- Those who have some prior knowledge in Python and Machine Learning.
Interested to Enroll?
If yes, then check out all details here- Convolutional Neural Networks
12. Complete Guide to TensorFlow for Deep Learning with Python– Udemy
Rating- 4.5/5
Time to Complete- 14 hours
In this course, you will learn how to use Google’s TensorFlow framework to create artificial neural networks for deep learning. This course will also provide complete Jupyter Notebook guides of code.
Throughout this course, you will learn neural network and TensorFlow basics, convolutional neural network, recurrent neural network, reinforcement Learning, AutoEncoders, etc. But the drawback of this course is that they use Tensorflow 1.
Extra Benefits-
- You will get a Certificate of Completion.
- You will also get 7 articles and 5 downloadable resources.
- Along with that, you will get lifetime access to the course material.
Who Should Enroll?
- Those who have some knowledge of Python programming and basic knowledge of math (mean, standard deviation, etc).
Interested to Enroll?
If yes, then check it out here– Complete Guide to TensorFlow for Deep Learning with Python
13. Deep Learning with Python and PyTorch- edX
Provider- IBM
Time to Complete- 6 Weeks
After gaining PyTorch basics from PyTorch Basics for Machine Learning course, this course will teach you how to build deep neural networks in PyTorch. And how to apply methods such as dropout, initialization, different types of optimizers, and batch normalization.
Then you will learn Convolutional Neural Networks, how to train the model on a GPU, Transfer Learning, dimensionality reduction techniques, and autoencoders. Throughout this course, you will learn from expert instructors of IBM.
Extra Benefits-
- You will get a Certificate of completion.
Who Should Enroll?
- Those who have previous knowledge in Python Programming and familiar with machine learning concepts.
Interested to Enroll?
If yes, then check out the program details here- Deep Learning with Python and PyTorch
14. Introduction to Deep Learning– Coursera
Provider- National Research University Higher School of Economics
Rating- 4.6/5
Time to Complete- 34 hours
This course is the part of Advanced Machine Learning Specialization program. In this course, you will learn the fundamentals of modern neural networks and their applications in computer vision and natural language understanding.
This course will teach you all the important building blocks of neural networks including fully connected layers, convolutional and recurrent layers.
There is a project associated with this course, where you will implement a deep neural network for image captioning which solves the problem of giving a text description for an input image.
Extra Benefits-
- You will get a Shareable Certificate.
- Along with that, you will get Course Videos & Readings, Practice Quizzes, Graded Assignments with Peer Feedback, Graded Quizzes with Feedback, and Graded Programming Assignments.
Who Should Enroll?
- Those who have basic knowledge of Python, linear algebra, and probability.
Interested to Enroll?
If yes, then check out the program details here- Introduction to Deep Learning
15. Introduction to Deep Learning with PyTorch– DataCamp
Time to Complete- 4 hours
This course will teach how you can use PyTorch to learn deep learning basics, then you will build your first neural network to predict digits from the MNIST dataset. After this, you will learn about convolutional neural networks. This course will also teach you how to use CNN to build more powerful models that give more accurate results.
The first chapter of this course is free so you can check the quality and content of the course. There are total 4 chapters in this course-
- Introduction to PyTorch (FREE)
- Artificial Neural Networks
- Convolutional Neural Networks (CNNs)
- Using Convolutional Neural Networks
Who Should Enroll?
- Those who know Python programming and familiar with supervised learning.
Interested to Enroll?
If yes, then check out the course details here- Introduction to Deep Learning with PyTorch
And here the list of “Best Neural Network Courses” ends. I hope these Best Neural Network Courses will definitely help you. I would suggest you bookmark this article “Best Neural Network Courses” for future referrals. Now it’s time to wrap up.
Conclusion
In this article, I tried to cover the 15 Best Neural Network Courses. If you have any doubts or questions regarding these Best Neural Network Courses, feel free to ask me in the comment section.
All the Best!
Enjoy Learning!
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– Henry Ford
Written By Aqsa Zafar
Founder of MLTUT, Machine Learning Ph.D. scholar at Dayananda Sagar University. Research on social media depression detection. Create tutorials on ML and data science for diverse applications. Passionate about sharing knowledge through website and social media.