Best Neural Networks Courses Online & Certification (April 2024)

  • Post last modified:14 December 2023
  • Reading time:38 mins read
  • Post category:Best Online Course
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After the launch of AI tools such as Chat GPT, companies are spending more and more on AI products and services. The adoption of AI and machine learning technology has increased, which results in high demand for professionals who understand Neural Networks.

Neural networks are powerful tools for solving complex problems, from image recognition and natural language processing to autonomous driving and robotics. It is a part of deep learning, which allows machines to learn from generated data and create AI-powered solutions. If you are knowledgeable in Neural Networks, you can gain a competitive edge in the job market. Take a plunge and get the Best Neural Network Courses.

Why is it important to learn Neural Networks?

Neural Networks are widely used for pattern recognition, image processing, and many other applications. You can learn how to use Neural Networks for developing new algorithms and models to automate various tasks.

According to the Bureau of Labor Statistics, the job outlook for Computer and Information Research Scientists, which includes experts in Neural Networks, is expected to grow 23% from 2023 to 2032, much faster than the average for all occupations. Thus, there is a growing need for professionals with knowledge of Neural Networks. According to Glassdoor, the average income for a neural network engineer is $115K per annum in the US.

The list of Best Neural Networks Courses can help you build the skills required to have a successful career in neural networks.

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Best Operational Research Courses, Certification, Tutorials, Training, Classes Online

Learn Neural Networks and Deep Learning [Coursera]

The Neural Network Specialization helps you build a strong foundation of deep learning by explaining capabilities, challenges, and consequences. You will understand the importance of deep learning in developing AI technology. The Neural Network Course is the pathway to develop the skills required to implement machine learning techniques in your projects.

Course Instructor

Andrew Ng is the creator and instructor of this Coursera Neural Networks Course. He is a chairman and Co-Founder of Coursera and an Adjunct Professor at Stanford University. His experience in machine learning and online education has helped many people who want to work in the AI industry. Kian Katanforoosh and Younes Bensouda Mouri are the other two top instructors and curriculum developers at DeepLearning.AI.

What you’ll learn 

The Coursera Neural Network Course is divided into 4 modules, including:

  1. Introduction to Deep Learning: This module will discuss how to analyze the major trends and provide practical examples of where and how deep learning concepts are applied today.

  2. Neural Networks Basic: In this module, you will develop machine learning problems with a neural network mindset and learn to speed up your models using vectorization techniques.

  3. Shallow Networks: This module will teach how to use forward and backward propagation for building a neural network with one hidden layer.

  4. Deep Neural Networks: This module is all about analyzing the key computations and using them for developing and training deep neural networks for computer vision.

Pros & Cons

Pros

  • Fantastic Introduction 
  • Very simple quizzes
  • Very clear 
  • Coding exercises and examples

Cons

  • Need to be comfortable with mathematics

Key Highlights & Learning Objectives

  • Get the introduction to deep learning and use it to analyze the major deep learning trends. 

  • Gain practical examples of where and how it is applied to build neural networks. 

  • Learn how to develop vectorization models and create machine-learning problems with a neural network. 

  • Able to build a neural network with forward and backward propagation. 

  • Build and train deep neural networks to complete the computer vision tasks. 

  • Full access to 39 videos, 15 videos, 4 quizzes, 5 programming assignments, and 2 app items.

Who is it for?

The Neural Network Course is ideal for those who want to learn neural networks and deep learning. Upon completion, you will build a foundational understanding of neural networks and develop job-relevant skills. Also, it is the best way to earn a professional career certificate and level up your technical career. Take one of the Best Deep Learning Courses Online to improve your deep learning skills.

Rating: 4.9/5
Students Enrolled: 1,358,914
Duration: 24 hours

Convolution Neural Networks with TensorFlow [Coursera]

The Convolutional Neural Network Course covers advanced techniques to improve the computer vision model. You will learn how to use real-world images in various sizes and shapes. In this Neural Network Course, you will discover how to visualize an image through convolutions and understand how a computer “sees” information.

Course Instructor

Laurence Moroney is the instructor of the Neural Network Online Course who is leading AI Advocacy at Google. He has written several programming books and is an active member of the Science Fiction Writers of America. Laurence published 15 courses on Coursera and led many towards AI and machine learning.

What you’ll learn

The Neural Networks Coursera Course is a part of the DeepLearning.AI TensorFlow Professional Certificate. It is divided into 4 modules, which are:

  1. Exploring a Larger Dataset: This module will get you started by preparing a large dataset using image classification.

  2. Augmentation: In this week’s module, you will learn about the technique to avoid overfitting and be able to train samples using Image augmentation. 

  3. Transfer Learning: This module will discuss using transfer learning methods to train models on large datasets.

  4. Multiclass Classification: You will explore how to classify things from each other categorical classification.

Pros & Cons

Pros

  • Excellent and detailed
  • World-class expert
  • Very comprehensive

Cons

  • Difficult assignments
  • Tricky and corrupt

Key Highlights & Learning Objectives

  • Learn how to handle real-world image data

  • Able to maintain plot loss and accuracy

  • Discover different strategies to prevent over fittings, such as augmentation and dropout

  • Explore new transfer learning methods and understand how to extract learned features from models. 

  • Use multiclass classification to categorize one thing to another. 

  • Gain lifetime access to 28 videos, 27 reading materials, 4 quizzes, 4 programming assignments, and 1 app item.

Who is it for?

The Coursera Convolutional Neural Networks is an excellent option for software developers who want to build scalable AI-powered algorithms. Once you complete this neural network course online, you can build models for yourself and apply best practices in Tensorflow for upcoming projects. The best Tensorflow Courses will provide you with the relevant experience you need for this specialization.

Rating: 4.7/5
Students Enrolled: 179,941
Duration: 16 hours

Deep Neural Network [Coursera]

The Neural Networks Certificate Course will build the foundation of deep learning and prepare you to participate in the development of AI technology. You will gain enough knowledge of deep learning and its processes that help to drive good results and performance. You will learn to use neural network techniques, including initialization, L2 and dropout regularization, hyperparameter tuning, batch normalization, and gradient checkin

Course Instructor

The Coursera Neural Networks Course was created and is taught by Andrew Ng. He is the chairman and co-founder of Coursera, as well as an adjunct professor at Stanford University. Many people seeking employment in the AI industry have benefited from his experience in machine learning and online education. At DeepLearning.AI, Kian Katanforoosh and Younes Bensouda Mouri are top instructors and curriculum developers.

What you’ll learn 

The Coursera Neural Networks is divided into 3 modules, namely:

  1. Practical Aspects of Deep Learning: This module will explain how to experiment with different initialization methods and apply L2 regularization and dropout to avoid model overfitting.

     
  2. Optimization Algorithms: In this module, you can build a deep learning toolbox by applying advanced optimizations and random mini-batching. Also, you will gain an understanding of rate decay scheduling that further speeds up your models.

  3. Hyperparameter Tuning and Programming Frameworks: In this module, You will learn how to use Tensorflow to build neural networks quickly and train a neural network on a TensorFlow dataset.

Pros & Cons

Pros

  • Valuable and informative
  • Carefully crafted assignments
  • Well-explained theory

Cons

  • Need more challenging assignments
  • Intermediate Python skills require

Key Highlights & Learning Objectives

  • Learn how to train and develop test sets for deep learning applications, as well as analyze biases and variances.

  • Learn to implement neural network initialization, regularization, hyperparameter tuning, batch normalization, gradient checking, and other standard techniques;

  • Explore the application of various optimization algorithms, such as mini-batch gradient descent, Momentum, RMSprop, and Adam.

  • Able to evaluate convergence and implement a neural network in TensorFlow.

  • Full access to 35+ videos, 10+ reading materials, 3 programming assignments, 3 quizzes, and 1 app item.

Who is it for?

If you are good at Python and have a basic grasp of linear algebra and ML, this Neural Network and Deep Learning Coursera Program is the right option. When you complete the course, you can level up your career with machine learning skills and work on projects related to AI or deep learning.

Rating: 4.9/5
Students Enrolled: 554,536
Duration: 23 hours

Introduction to Deep Learning & Neural Networks with Keras [Coursera]

This Neural Network Training Online Course covers all the fundamentals of deep learning and gives a brief introduction to the field. You will learn basic answers to questions, such as what deep learning is and how deep learning models compare to artificial neural networks. Also, it will discuss different deep learning models and how to create deep learning models using the Keras library.

Course Instructor

Alex Aklson, a data scientist in the Digital Business Group at IBM Canada, will teach Coursera Deep Learning and Neural Networks. He has worked on various data science projects as a data scientist using his human-centered, data-driven approach.

What you’ll learn

The Deep Learning and Neural Network Course with Keras comprises of 5 modules, including:

  1. Introduction: This module will introduce you to the deep learning fundamentals and their applications. You will also discover how deep learning algorithms are prepared and inspired by a human brain’s functionality and neuron process.

  2. Artificial Neural Networks: In this module, you can write the gradient descent algorithm and optimize variables for a defined function. It also explains about backpropagation and how neural networks learn. 

  3. Deep Learning Libraries: Build a solid knowledge of various deep learning libraries, including Keras, PyTorch, and TensorFlow. Moreover, it will explain how to use the Keras library for developing regression and classification models.

  4. Deep Learning Models: During this module, you will understand the difference between shallow and deep neural networks. Also, it gives an overview of convolutional networks and how to build them with the Keras library. 

  5. Course Project: In this module, you will conclude the course by working on a final assignment where you will use the Keras library to build a regression model and experiment with the depth and width of the model.

Pros & Cons

Pros

  • Very clear and precise
  • Good practical examples
  • Interactive and beneficial

Cons

  • Background knowledge in Python programming

Key Highlights & Learning Objectives

  • Please give an overview of neural networks, deep learning models, and their differences.

  • Demonstrate an understanding of unsupervised deep learning models such as autoencoders and restricted Boltzmann machines.

  • Demonstrate an understanding of supervised deep learning models such as convolutional neural networks and recurrent networks.

  • Build deep learning models and networks using the Keras library.

  • Learn about recurrent neural networks and autoencoders.

Who is it for?

Whether you want a career in deep learning or deep learning, it is a suitable course for you. You don’t need any basic knowledge or experience to start with deep learning. Upon completion, you can build your first deep-learning model and pursue it in the AI field.

Rating: 4.7/5
Students Enrolled: 73,452
Duration: 8 hours

Neural Network Nanodegree Program [Udacity]

This Neural Network Certificate Program is the best way to train yourself in deep learning. You will understand how deep learning concepts work to develop neural networks and AI products.

Course Instructor

Learn from the best instructors of Udacity, including Giacomo Vianello, Nathan Klarer, Eric Galinkin, and Thomas Hossler.

What you’ll learn

The Udacity Neural Network Program is divided into 6 courses that cover everything related to deep learning and neural networks. 

  1. Welcome to Deep Learning Program: This Deep Learning Training Program will cover all the fundamentals of deep learning and build skills to define a beneficial, new, AI-powered future.

  2. Introduction: This course helps you develop a deep understanding of deep learning with theory and practice. You will learn about deep learning architecture and goals and implementation in PyTorch.

  3. Convolutional Neural Networks: During this course, you will understand what makes CNNs useful for image processing, how they work, and how to build them from scratch.

  4. RNN and Transformers: A variety of RNN architectures are covered in this course, along with design patterns related to these models. Additionally, you will learn about transformer architectures.

  5. Building Generative Adversarial Networks: Throughout this course, you will learn about and implement a Deep Convolutional GAN with Ian Goodfellow, the inventor of these algorithms, and Jun-Yan Zhu, the creator of CycleGANs.

  6. Career Services: Get to improve your LinkedIn profile with the help of experts and grow your professional network in this program.

Pros & Cons

Pros

  • Refreshing 
  • Up-to-date with the current standards

Cons

  • Require deep learning fluency 
  • Advanced mathematical skill

Key Highlights & Learning Objectives

  • Learn topics including Neural Networks, Convolutional Neural Networks, Recurrent Neural Networks, and Generative Adversarial Networks. 

  • Discover the fundamental algorithms of deep learning, architecture, and goals,

  • Explore autoencoders and modern CNNs for anomaly detection and image denoising.

  • Build knowledge of CNNs and how to use them for object detection and semantic segmentation.

  • Able to apply a Deep Convolution GAN for developing complex color images.

  • Ensure your profile attracts relevant leads that will grow your professional network.

Who is it for?

The Deep Learning Online Training Program is perfect for those who want to build a foundation in the AI world. However, it would help if you had proficiency in Deep Learning and strong in linear algebra. By the end of the course, you can optimize your skills and attract relevant job opportunities.

Rating: 4.7/5
Duration: Self-paced

NLP Modelling in Python [Datacamp]

In this Neural Network Online Course, you will find out how to use RNNs for classifying text, generating phrases, and translating Portuguese sentences into English. You will learn to use a Keras RNN model for performing sentiment classification. During this course, you will also prepare data for multiclass classification and understand how to use Python for language modeling.

Course Instructor

T

Pros & Cons

Pros

  • Short and basic 
  • Good practical exercises

Cons

  • No hands-on project
  • Need Python skills

Key Highlights & Learning Objectives

  • Build the foundation of Recurrent Neural Networks (RNN) 

  • Examine the flow of information in a recurrent neural network and apply a Keras RNN model to perform sentiment classification.

  • Understand how to embed layers in a language model and solve vanishing and exploding gradient problems using it. 

  • Learn how to translate text and generate text using RNN models.

  • Transform Portuguese phrases into English using your knowledge of recurrent neural networks.

  • Access 16 videos, 54 exercises, and 4 hours of on-demand video.

Who is it for?

The Datacamp Neural Networks Course is for those skilled in Python with basic knowledge of RNNs. When you complete it, you will easily use RNN for automatic classification, text generation, and neural machine translation.

Rating: 4.7/5
Students Enrolled: 32,782
Duration: Self-paced

IBM Data Analyst Professional Certificate [Coursera]

T

Course Instructor

T

Pros & Cons

Pros

Cons

Key Highlights & Learning Objectives

  • T

Who is it for?

T

Rating: 4.7/5
Students Enrolled: 24,682
Duration: 3 months, 12 hours/week

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