neural network course

10 Best + Free Online Neural Networks Courses

Neural networks are computer programs that try to imitate the human brain. The ability of computers to process the way human minds work is at the forefront of technological progress as they become smarter. The neural model first proposed by Warren McCulloch and Walter Pitts is the foundation of deep learning. To crack Artificial Intelligence, algorithms must operate not only like, but better than, the human intellect.
Because humans are unable to analyze the vast amounts of data available today, machine learning is transforming the way we make judgments in almost every industry. The neural network isn’t a program in and of itself. Instead, it’s a framework that guides the performance of learning algorithms. Deep neural networks have real-world applications that are revolutionizing almost every aspect of our lives. Here are the top 10 neural networks courses-

List of 10 Best + Free Online Neural Networks Courses

1. Neural Networks Certification Course by deeplearning.ai (Coursera)

This course is a wonderful place to start if you want to learn the principles of this cutting-edge technology. The curriculum is meant to teach both the principles and how deep learning works in practice. Learn how to develop, train, and usefully interconnected deep neural networks by understanding the main characteristics of a neural network design. Because this is intermediate-level software, fundamental python programming skills, practical knowledge of data structures, and basic machine learning concepts are required.

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

2. Deep Learning and Neural Networks for Financial Engineering(edX)

Deep Learning forays into the area of Artificial Intelligence. This course will show how neural networks can be used to improve practice in a variety of fields, with a focus on financial engineering. Students will learn about deep learning techniques, as well as how alternative data sources like images and text can help develop finance practice.

What you will learn –

  • Utilize neural network and deep learning techniques and apply them in many domains, including Finance.
  • Make predictions based on financial data.
  • Use alternate data sources such as images and text and associated techniques such as image recognition and natural language processing for prediction.
  • Use these techniques and data for:
    • optimizing portfolios and portfolio management.
    • managing risk.
    • streamlining operations.

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 – 7 weeks approx

Instructor – Ken Perry

3. Improving Deep Neural Networks (Coursera)

You will unlock the deep learning black box in the second course of the Deep Learning Specialization to understand the processes that drive performance and deliver good results systematically.
By the end, you’ll know how to use standard neural network techniques like initialization, L2 and dropout regularisation, hyperparameter tuning, batch normalization, and gradient checking to train and develop test sets and analyze bias/variance for building deep learning applications; implement and apply a variety of optimization algorithms like mini-batch gradient; and use standard neural network techniques like initialization, L2 and dropout regularisation, hyperparameter tuning, batch normalization, and gradient checking to build deep learning applications.
The Deep Learning Specialization is a fundamental program that will enable you to participate in the creation of cutting-edge AI technology by helping you grasp the capabilities, problems, and repercussions of deep learning. It paves the road for you to get the information and abilities you need to apply machine learning to your work, advance your technical profession, and take the next step in your A.I. career.

What you will learn –

  • Discover and experiment with a variety of different initialization methods, apply L2 regularization and dropout to avoid model overfitting, then apply gradient checking to identify errors in a fraud detection model.
  • Develop your deep learning toolbox by adding more advanced optimizations, random mini batching, and learning rate decay scheduling to speed up your models.
  • Explore TensorFlow, a deep learning framework that allows you to build neural networks quickly and easily, then train a neural network on a TensorFlow dataset.

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 hours approx

Instructor – Andrew Ng

4. Convolutional Neural Networks in TensorFlow (Coursera)

If you’re a software engineer looking to create scalable AI-powered algorithms, you’ll need to know how to employ the necessary tools. This course will teach you best practices for utilizing TensorFlow, a famous open-source machine learning framework. It is part of the forthcoming Machine Learning with Tensorflow Specialization.
You’ll study further strategies to improve the computer vision model you constructed in Course 1 of the deeplearning.ai TensorFlow Specialization in Course 2. You’ll learn how to deal with real-world images of various shapes and sizes, depicting an image’s trip via convolutions to understand how a computer “sees” information, plot loss, and accuracy, and investigate overfitting tactics such as augmentation and dropout. Finally, Course 2 will teach you about transfer learning and how to extract learned features from models.

What you will learn –

  • Handle real-world image data.
  • Plot loss and accuracy.
  • Explore strategies to prevent overfitting, including augmentation and dropout.
  • Learn transfer learning and how learned features can be extracted from models.

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 – 26 hours approx

Instructor – Laurence Moroney

5. Neural Networks Course (Google)

Google has created a crash course in neural networks that consists of a series of short movies that will give you an overview of this artificial intelligence topic. You will gain insight into hidden layers, activation functions, and other topics as you progress through the classes. You will have a basic understanding of this area at the end of the lectures and will be prepared to study more advanced specializations.

What you will learn –

  • Structure
  • Playground Exercise
  • Programming Exercise

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 – Self-placed

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

Artificial intelligence is advancing at a breakneck pace. That is without a doubt the case. Self-driving cars have logged millions of miles, IBM Watson is diagnosing patients more accurately than armies of doctors, and Google Deepmind’s AlphaGo defeated the World champion in Go, a game in which intuition is crucial.
However, as AI progresses, the challenges it must address get increasingly complicated. Deep Learning is the only method that can address such complicated issues, which is why it is at the center of artificial intelligence.

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.

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, Ligency Team

7. Deep Learning Specialization by Andrew Ng and Team(Coursera)

The Deep Learning Specialization is a fundamental program that will enable you to participate in the creation of cutting-edge AI technology by helping you grasp the capabilities, problems, and repercussions of deep learning.
You’ll learn how to create and train neural network designs like Convolutional Neural Networks, Recurrent Neural Networks, LSTMs, and Transformers, as well as how to improve them with tactics like Dropout, BatchNorm, Xavier/He initialization, and more in this Specialization. Prepare to use Python and TensorFlow to learn theoretical topics and their industry applications, and to handle real-world problems like speech recognition, music synthesis, chatbots, machine translation, natural language processing, and more.
Many sectors are being transformed by artificial intelligence. The Deep Learning Specialization paves the road for you to take the next step in your AI career by assisting you in gaining the necessary knowledge and abilities. You’ll also get career advice from deep learning specialists from industry and academia along the way.

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 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.

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

8. Neural Networks in Python from Scratch: Complete guide (Udemy)

Artificial neural networks are currently widely regarded as the most effective Machine Learning approaches, with businesses such as Google, IBM, and Microsoft using them in a variety of applications. You’ve certainly heard about self-driving vehicles or apps that generate fresh music, poetry, photos, and even complete movie scripts. The most intriguing aspect about this is that the majority of these were created utilizing neural networks. Neural networks have been around for a while, but with the emergence of Deep Learning, they have come back stronger than ever, and are now considered the most powerful data analysis tool.
One of the most common issues I’ve noticed among students learning about neural networks is a lack of simply understood materials. This is due to the fact that the bulk of the resources provided are quite technical and use a lot of mathematical formulas, making the learning process quite tough for anyone who wants to start out in this sector. With this in mind, the primary goal of this course is to convey the theoretical and mathematical ideas of neural networks in a clear and concise manner, so that even if you have no prior knowledge of neural networks, you will be able to comprehend all of the processes.

What you will learn –

  • Learn all of the mathematical computations concerning artificial neural networks step by step.
  • Create neural networks from the ground up in Python and Numpy.
  • Perceptron, activation functions, backpropagation, gradient descent, learning rate, and other topics to be understood.
  • Create neural networks for classification and regression problems.
  • Use libraries like Pybrain, sklearn, TensorFlow, and PyTorch to build neural networks.

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 – 8.5 hours approx

Instructor – Jones Granatyr, IA Expert Academy, Ligency Team

9. Deep Learning Certification by IBM (edX)

Through a series of hands-on assignments and projects, you will acquire and excel at Deep Learning abilities during this professional certificate program. The course will conclude in a Deep Learning capstone project that will help you display your applied abilities to potential employers. It will be available on the renowned eLearning site edX. You’ll master the fundamentals of Deep Learning, including multiple Neural Networks for both supervised and unsupervised learning, among other things. You’ll also learn how to create and deploy Convolutional Networks, Recurrent Networks, and Autoencoders, among other Deep Architectures. Joseph Santarcangelo, Ph.D., IBM Data Scientist; Alex Aklson, Ph.D., IBM Data Scientist; and Saeed Aghabozorgi, Ph.D., IBM Sr. Data Scientists are the instructors for this program.

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.
  • 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

10. Neural Networks in Python: Deep Learning for Beginners(Udemy)

Are you seeking a comprehensive Artificial Neural Network (ANN) course that will teach you all you need to know about building a Neural Network model in Python?
You’ve come to the correct place if you’re looking for a Neural Networks course.
You will be able to: Identify the business problem that can be solved using Neural Network Models after completing this course.
Understand advanced neural network concepts like gradient descent, forward and backward propagation, and so on.
Create and evaluate neural network models in Python using the Keras and Tensorflow packages.
Practice, discuss and comprehend Deep Learning ideas with confidence. All students who complete this Neural networks course receive a verifiable Certificate of Completion.

What you will learn –

  • Get a solid understanding of Artificial Neural Networks (ANN) and Deep Learning.
  • Understand the business scenarios where Artificial Neural Networks (ANN) is applicable.
  • Building a Artificial Neural Networks (ANN) in Python.
  • Use Artificial Neural Networks (ANN) to make predictions.
  • Learn usage of Keras and Tensorflow libraries.
  • Use Pandas DataFrames to manipulate data and make statistical computations.

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 – 9.5 hours approx

Instructor – Start-Tech Company

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