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10 Best Neural Networks Courses Online

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The advent of artificial intelligence has transformed the landscape of technology, and at the heart of this revolution lie neural networks — complex algorithms modeled loosely after the human brain, designed to recognize patterns and solve a variety of problems in areas such as computer vision, natural language processing, and beyond. Whether you're a budding data scientist, an experienced machine learning engineer, or simply a curious enthusiast, gaining a solid understanding of neural networks is a crucial step in staying at the forefront of tech innovation.

For those looking to dive into this fascinating subject, the internet offers a wealth of resources. Online courses have become a popular means of learning, offering flexibility and access to expertise from leading institutions and industry professionals. To help you navigate the sea of available options, we've compiled a list of the 10 best neural networks courses online. These courses are designed to cater to a variety of learning needs, from beginner-friendly introductions to advanced explorations of the latest techniques.

Our selection criteria include the comprehensiveness of course content, the expertise of instructors, the quality of learning materials, practical applications, student feedback, and the value of certification offered. Whether you prefer self-paced study or structured, cohort-based learning, you're sure to find a course that aligns with your learning style and career goals.

Top 10 Neural Networks Courses Online

Get More Details On Each Neural Networks Course:

Deep Learning A-Z 2023: Neural Networks, AI & ChatGPT Prize

 in stock
as of May 21, 2024 1:43 am

Learn to create Deep Learning Algorithms in Python from two Machine Learning & Data Science experts. Templates included.

Created by: Kirill Eremenko
Data Scientist
Created by: Hadelin de Ponteves
Passionate AI Instructor
Created by: SuperDataScience Team
Helping Data Scientists Succeed
Created by: Ligency Team
Helping Data Scientists Succeed
Rating:4.55 (44872reviews)     371296students enrolled

What Will I 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
  • Apply AutoEncoders in practice


  • High school mathematics level
  • Basic Python programming knowledge

Target audience

  • Anyone interested in Deep Learning
  • Students who have at least high school knowledge in math and who want to start learning Deep Learning
  • Any intermediate level people who know the basics of Machine Learning or Deep Learning, including the classical algorithms like linear regression or logistic regression and more advanced topics like Artificial Neural Networks, but who want to learn more about it and explore all the different fields of Deep Learning
  • Anyone who is not that comfortable with coding but who is interested in Deep Learning and wants to apply it easily on datasets
  • Any students in college who want to start a career in Data Science
  • Any data analysts who want to level up in Deep Learning
  • Any people who are not satisfied with their job and who want to become a Data Scientist
  • Any people who want to create added value to their business by using powerful Deep Learning tools
  • Any business owners who want to understand how to leverage the Exponential technology of Deep Learning in their business
  • Any Entrepreneur who wants to create disruption in an industry using the most cutting edge Deep Learning algorithms

The Complete Neural Networks Bootcamp: Theory, Applications

 in stock
as of May 21, 2024 1:43 am

Deep Learning and Neural Networks Theory and Applications with PyTorch! Including Transformers, BERT and GPT!

Created by: Fawaz Sammani
Computer Vision Researcher
Rating:4.44 (2000reviews)     17352students enrolled

What Will I Learn?

  • Understand How Neural Networks Work (Theory and Applications)
  • Understand How Convolutional Networks Work (Theory and Applications)
  • Understand How Recurrent Networks and LSTMs work (Theory and Applications)
  • Learn how to use PyTorch in depth
  • Understand how the Backpropagation algorithm works
  • Understand Loss Functions in Neural Networks
  • Understand Weight Initialization and Regularization Techniques
  • Code-up a Neural Network from Scratch using Numpy
  • Apply Transfer Learning to CNNs
  • CNN Visualization
  • Learn the CNN Architectures that are widely used nowadays
  • Understand Residual Networks in Depth
  • Understand YOLO Object Detection in Depth
  • Visualize the Learning Process of Neural Networks
  • Learn how to Save and Load trained models
  • Learn Sequence Modeling with Attention Mechanisms
  • Build a Chatbot with Attention
  • Transformers
  • Build a Chatbot with Transformers
  • BERT
  • Build an Image Captioning Model


  • Some Basic Python Expreience is preferable
  • Some High School Mathematics

Target audience

  • Anyone who in interested in learning about Neural Networks and Deep Learning

Build Neural Networks In Python From Scratch. Step By Step!

 in stock
as of May 21, 2024 1:43 am

Understand machine learning and deep learning by building linear regression and gradient descent from the ground up.

Created by: Loek van den Ouweland
Passionate Python Teacher
Rating:4.88 (208reviews)     1104students enrolled

What Will I Learn?

  • The basic functions for any neural network, by coding linear regression, cost functions and back propagation
  • Understand the properties of neural networks by adjusting learning rates and biases
  • Train a network by implementing a gradient descent algorithm
  • Normalizing inputs for multi-input networks
  • Create classification networks by implementing multiple output neurons and activation
  • Improve network accuracy by implementing hidden layers for non-linear data


  • You have an interest in neural networks.
  • You have some programming experience in Python or another language.

Target audience

  • Developer who want to learn the mechanics of neural networks
  • Developers who want to avoid using neural network libraries and frameworks
  • Or developers who use frameworks but want to learn the meaning of the individual network parameters

TensorFlow Developer Certificate: Zero to Mastery

 in stock
as of May 21, 2024 1:43 am

Pass the TensorFlow Developer Certification Exam by Google. Become an AI, Machine Learning, and Deep Learning expert!

Created by: Andrei Neagoie
Founder of
Created by: Daniel Bourke
Machine Learning Engineer/Writer/Video maker
Rating:4.68 (9381reviews)     65749students enrolled

What Will I Learn?

  • Learn to pass Google's official TensorFlow Developer Certificate exam (and add it to your resume)
  • Build TensorFlow models using Computer Vision, Convolutional Neural Networks and Natural Language Processing
  • Complete access to ALL interactive notebooks and ALL course slides as downloadable guides
  • Increase your skills in Machine Learning and Deep Learning, to test your abilities with the TensorFlow assessment exam
  • Understand how to integrate Machine Learning into tools and applications
  • Learn to build all types of Machine Learning Models using the latest TensorFlow 2
  • Build image recognition, text recognition algorithms with deep neural networks and convolutional neural networks
  • Using real world images to visualize the journey of an image through convolutions to understand how a computer “sees” information, plot loss and accuracy
  • Applying Deep Learning for Time Series Forecasting
  • Gain the skills you need to become a TensorFlow Certified Developer
  • Be recognized as a top candidate for recruiters seeking TensorFlow developers


  • Mac / Windows / Linux - all operating systems work with this course!
  • No previous TensorFlow knowledge required. Basic understanding of Machine Learning is helpful

Target audience

  • Anyone who wants to pass the TensorFlow Developer exam so they can join Google's Certificate Network and display their certificate and badges on their resume, GitHub, and social media platforms including LinkedIn, making it easy to share their level of TensorFlow expertise with the world
  • Students, developers, and data scientists who want to demonstrate practical machine learning skills through the building and training of models using TensorFlow
  • Anyone looking to expand their knowledge when it comes to AI, Machine Learning and Deep Learning
  • Anyone looking to master building ML models with the latest version of TensorFlow

A deep understanding of deep learning (with Python intro)

 in stock
as of May 21, 2024 1:43 am

Master deep learning in PyTorch using an experimental scientific approach, with lots of examples and practice problems.

Created by: Mike X Cohen
Educator and writer
Rating:4.73 (3197reviews)     26865students enrolled

What Will I Learn?

  • The theory and math underlying deep learning
  • How to build artificial neural networks
  • Architectures of feedforward and convolutional networks
  • Building models in PyTorch
  • The calculus and code of gradient descent
  • Fine-tuning deep network models
  • Learn Python from scratch (no prior coding experience necessary)
  • How and why autoencoders work
  • How to use transfer learning
  • Improving model performance using regularization
  • Optimizing weight initializations
  • Understand image convolution using predefined and learned kernels
  • Whether deep learning models are understandable or mysterious black-boxes!
  • Using GPUs for deep learning (much faster than CPUs!)


  • Interest in learning about deep learning!
  • Python/Pytorch skills are taught in the course
  • A Google account (google-colab is used as the Python IDE)

Target audience

  • Students in a deep learning course
  • Machine-learning enthusiasts
  • Anyone interested in mechanisms of AI (artificial intelligence)
  • Data scientists who want to expand their library of skills
  • Aspiring data scientists
  • Scientists and researchers interested in deep learning

Data Science: Deep Learning and Neural Networks in Python

 in stock
as of May 21, 2024 1:43 am

The MOST in-depth look at neural network theory for machine learning, with both pure Python and Tensorflow code

Created by: Lazy Programmer Inc.
Artificial intelligence and machine learning engineer
Rating:4.72 (9508reviews)     55505students enrolled

What Will I Learn?

  • Learn how Deep Learning REALLY works (not just some diagrams and magical black box code)
  • Learn how a neural network is built from basic building blocks (the neuron)
  • Code a neural network from scratch in Python and numpy
  • Code a neural network using Google's TensorFlow
  • Describe different types of neural networks and the different types of problems they are used for
  • Derive the backpropagation rule from first principles
  • Create a neural network with an output that has K > 2 classes using softmax
  • Describe the various terms related to neural networks, such as "activation", "backpropagation" and "feedforward"
  • Install TensorFlow
  • Understand important foundations for OpenAI ChatGPT, GPT-4, DALL-E, Midjourney, and Stable Diffusion


  • Basic math (calculus derivatives, matrix arithmetic, probability)
  • Install Numpy and Python
  • Don't worry about installing TensorFlow, we will do that in the lectures.
  • Being familiar with the content of my logistic regression course (cross-entropy cost, gradient descent, neurons, XOR, donut) will give you the proper context for this course

Target audience

  • Students interested in machine learning - you'll get all the tidbits you need to do well in a neural networks course
  • Professionals who want to use neural networks in their machine learning and data science pipeline. Be able to apply more powerful models, and know its drawbacks.

Neural Networks in Python from Scratch: Complete guide

 in stock
as of May 21, 2024 1:43 am

Learn the fundamentals of Deep Learning of neural networks in Python both in theory and practice!

Created by: Jones Granatyr
Created by: SuperDataScience Team
Helping Data Scientists Succeed
Created by: Ligency Team
Helping Data Scientists Succeed
Created by: AI Expert Academy
Rating:4.4 (470reviews)     3936students enrolled

What Will I Learn?

  • Learn step by step all the mathematical calculations involving artificial neural networks
  • Implement neural networks in Python and Numpy from scratch
  • Understand concepts like perceptron, activation functions, backpropagation, gradient descent, learning rate, and others
  • Build neural networks applied to classification and regression tasks
  • Implement neural networks using libraries, such as: Pybrain, sklearn, TensorFlow, and PyTorch


  • Programming logic (if, while and for statements)
  • Basic Python programming
  • No prior knowledge about Artificial Neural Networks or Artificial Intelligence

Target audience

  • Beginners who are starting to learn about Artificial Neural Networks or Deep Learning
  • People interested in the theory of Artificial Neural Networks
  • Undergraduate students who are studying subjects related to Artificial Intelligence
  • Anyone interested in Artificial Intelligence or Artificial Neural Networks

Deep Learning for Beginners: Core Concepts and PyTorch

 in stock
as of May 21, 2024 1:43 am

Get an Intuitive Understanding of Deep Learning

Created by: Seungchan Lee
Co-founder of Sidetrek and DeepIntuitions
Created by: Nami Kim
Co-founder of Sidetrek and DeepIntuitions
Rating:4.86 (88reviews)     759students enrolled

What Will I Learn?

  • Develop an intuitive understanding of Deep Learning
  • Visual and intuitive understanding of core math concepts behind Deep Learning
  • Detailed view of how exactly deep neural networks work beneath the hood
  • Computational graphs (which libraries like PyTorch and Tensorflow are built on)
  • Build neural networks from scratch using PyTorch and PyTorch Lightening
  • You’ll be ready to explore the cutting edge of AI and more advanced neural networks like CNNs, RNNs and Transformers
  • You'll be able to understand what deep learning experts are talking about in articles and interviews
  • You’ll be able to start experimenting with your own AI projects using PyTorch


  • Basic Python programming knowledge
  • Highschool math
  • A strong desire to learn Deep Learning

Target audience

  • Students who want learn Deep Learning for the first time
  • Beginners who want to finally understand Deep Learning at an intuitive level
  • Professionals looking to supercharge their understanding of Deep Learning fundamentals

Tensorflow 2.0: Deep Learning and Artificial Intelligence

 in stock
as of May 21, 2024 1:43 am

Machine Learning & Neural Networks for Computer Vision, Time Series Analysis, NLP, GANs, Reinforcement Learning, +More!

Created by: Lazy Programmer Inc.
Artificial intelligence and machine learning engineer
Created by: Lazy Programmer Team
Artificial Intelligence and Machine Learning Engineer
Rating:4.74 (10726reviews)     50771students enrolled

What Will I Learn?

  • Artificial Neural Networks (ANNs) / Deep Neural Networks (DNNs)
  • Predict Stock Returns
  • Time Series Forecasting
  • Computer Vision
  • How to build a Deep Reinforcement Learning Stock Trading Bot
  • GANs (Generative Adversarial Networks)
  • Recommender Systems
  • Image Recognition
  • Convolutional Neural Networks (CNNs)
  • Recurrent Neural Networks (RNNs)
  • Use Tensorflow Serving to serve your model using a RESTful API
  • Use Tensorflow Lite to export your model for mobile (Android, iOS) and embedded devices
  • Use Tensorflow's Distribution Strategies to parallelize learning
  • Low-level Tensorflow, gradient tape, and how to build your own custom models
  • Natural Language Processing (NLP) with Deep Learning
  • Demonstrate Moore's Law using Code
  • Transfer Learning to create state-of-the-art image classifiers
  • Earn the Tensorflow Developer Certificate
  • Understand important foundations for OpenAI ChatGPT, GPT-4, DALL-E, Midjourney, and Stable Diffusion


  • Know how to code in Python and Numpy
  • For the theoretical parts (optional), understand derivatives and probability

Target audience

  • Beginners to advanced students who want to learn about deep learning and AI in Tensorflow 2.0

Physics Informed Neural Networks (PINNs)

 in stock
as of May 21, 2024 1:43 am

Simulations with AI

Created by: Dr.Mohammad Samara
Data Science/Machine Learning Expert
Rating:4.25 (50reviews)     219students enrolled

What Will I Learn?

  • Understand the Theory behind PDEs equations solvers.
  • Build numerical based PDEs solver.
  • Build PINNs based pdes solver.
  • Understand the Theory behind PINNs PDEs solvers.


  • High School Math
  • Basic Python knowledge

Target audience

  • Engineers and Programmers whom want to Learn PINNs

Price Statistics for Neural Networks Course

  • All prices mentioned above are in United States dollar.
  • This product is available at Udemy.
  • At you can purchase The Complete Neural Networks Bootcamp: Theory, Applications for only $10.00
  • The lowest price of Tensorflow 2.0: Deep Learning and Artificial Intelligence was obtained on May 21, 2024 1:43 am.

In wrapping up, each of these courses offers a unique stepping stone into the intricate world of neural networks. By investing your time in one or more of these offerings, you're setting yourself up for success in a field that's not just cutting-edge but also immensely rewarding. As neural networks continue to shape the future, the knowledge and skills you acquire will become invaluable assets in your professional toolkit. So choose the course that resonates with your learning style and career aspirations, and take that next bold step into the world of AI. With dedication and the insights gained from these top-rated courses, you'll be well on your way to becoming an adept practitioner in the dynamic landscape of neural networks. Happy learning, and may your passion for discovery fuel your journey towards AI mastery.

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