In this article, you will find the Best Neural Networks Courses on Udemy & advanced like Beginner courses, and Practice test courses. So, check these Best Neural Networks Courses on Udemy and find the Best Neural Networks Courses on Udemy for Beginners to Advanced according to your need. In the previous article, I shared the Best Neural Networks Books for Beginners to Advanced, you can go through the list and enjoy reading.
Here we have covered the Best Neural Networks Courses on Udemy. Let’s go through the list of Udemy Neural Networks Courses one by one.
Best Neural Networks Courses on Udemy 2022
Deep Learning A-Z: Hands-On Artificial Neural Networks
Rating: 4.6
Deep Learning A-Z is structured around special coding blueprint approaches meaning that you won’t get bogged down in unnecessary programming or mathematical complexities and instead you will be applying Deep Learning techniques from very early on in the course. You will build your knowledge from the ground up and you will see how with every tutorial you are getting more and more confident.
If you already have experience with Deep Learning, you will find this course refreshing, inspiring and very practical. Inside Deep Learning A-Z you will master some of the most cutting-edge Deep Learning algorithms and techniques (some of which didn’t even exist a year ago) and through this course you will gain an immense amount of valuable hands-on experience with real-world business challenges. Plus, inside you will find inspiration to explore new Deep Learning skills and applications.
Mastering Deep Learning is not just about knowing the intuition and tools, it’s also about being able to apply these models to real-world scenarios and derive actual measurable results for the business or project.
Best for:
- 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
Best for: Beginners to Intermediate Level | Course duration: 22.5 hrs | Provider: Udemy
Total Articles: 38 | Downloadable resources: 5
Info: Visit this course and get amazing offers on this course
The Complete Neural Networks Bootcamp: Theory, Applications
Rating: 4.6
This course is a comprehensive guide to Deep Learning and Neural Networks. The theories are explained in depth and in a friendly manner. After that, we’ll have the hands-on session, where we will be learning how to code Neural Networks in PyTorch, a very advanced and powerful deep learning framework!
You will deeply understand the theories of how neural networks and the backpropagation algorithm works, in a friendly manner. You will walk through an example and do the calculations step-by-step. You will also learn the activation functions used in Neural Networks, with their advantages and disadvantages!
- Understand How Neural Networks Work (Theory and Applications)
- Understand How Convolutional Networks Work (Theory and Applications)
- Learn how to use PyTorch in depth
- Learn the CNN Architectures that are widely used nowadays
- 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
Best for: Beginners Level | Course duration: 43.5 hrs | Provider: Udemy
Total Articles: 16 | Downloadable resources: 2
Info: Visit this Course and get amazing offers on this course
Data Science: Deep Learning and Neural Networks in Python
Rating: 4.6
This course will get you started in building your FIRST artificial neural network using deep learning techniques. Following my previous course on logistic regression, You will learn from basic building block, and build full-on non-linear neural networks right out of the gate using Python and Numpy. All the materials for this course are FREE.
You should take this course if you are interested in starting your journey toward becoming a master at deep learning, or if you are interested in machine learning and data science in general. You will go beyond basic models like logistic regression and linear regression and other automatically learns features.
This course provides you with many practical examples so that you can really see how deep learning can be used on anything. Throughout the course, you will do a course project, that can know you how to predict user actions on a website given user data like whether or not that user is on a mobile device, the number of products they viewed, how long they stayed on your site, whether or not they are a returning visitor, and what time of day they visited.
- 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
- 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”
Best for: Beginners Level | Course duration: 11 hrs | Provider: Udemy
Info: Visit this course and get amazing offers on this course
Deep Learning: Convolutional Neural Networks in Python
Rating: 4.7
The Convolutional Neural Network (CNN) has been used to obtain state-of-the-art results in computer vision tasks such as object detection, image segmentation, and generating photo-realistic images of people and things that don’t exist in the real world!
This course will teach you the fundamentals of convolution and why it’s useful for deep learning and even NLP (natural language processing).
You will learn about modern techniques such as data augmentation and batch normalization, and build modern architectures such as VGG yourself.
This course focuses on “how to build and understand“, not just “how to use”. Anyone can learn to use an API in 15 minutes after reading some documentation. It’s not about “remembering facts”, it’s about “seeing for yourself” via experimentation. It will teach you how to visualize what’s happening in the model internally. If you want more than just a superficial look at machine learning models, this course is for you.
This course will teach you:
- The basics of machine learning and neurons (just a review to get you warmed up!)
- Neural networks for classification and regression (just a review to get you warmed up!)
- How to model image data in code
- How to model text data for NLP (including preprocessing steps for text)
- How to build an CNN using Tensorflow 2
- How to use batch normalization and dropout regularization in Tensorflow 2
- How to do image classification in Tensorflow 2
- How to do data preprocessing for your own custom image dataset
- How to use Embeddings in Tensorflow 2 for NLP
- How to build a Text Classification CNN for NLP (examples: spam detection, sentiment analysis, parts-of-speech tagging, named entity recognition)
Best for: Beginners Level | Course duration: 12 hrs | Provider: Udemy
Info: Visit this course and get amazing offers on this course
Deep Learning: Recurrent Neural Networks in Python
Rating: 4.6
The Recurrent Neural Network (RNN) has been used to obtain state-of-the-art results in sequence modeling.
This includes time series analysis, forecasting and natural language processing (NLP). Learn about why RNNs beat old-school machine learning algorithms like Hidden Markov Models.
This course focuses on “how to build and understand“, not just “how to use”. Anyone can learn to use an API in 15 minutes after reading some documentation. It’s not about “remembering facts”, it’s about “seeing for yourself” via experimentation. It will teach you how to visualize what’s happening in the model internally. If you want more than just a superficial look at machine learning models, this course is for you.
- The basics of machine learning and neurons (just a review to get you warmed up!)
- Neural networks for classification and regression (just a review to get you warmed up!)
- How to model sequence data
- How to model time series data
- How to model text data for NLP (including preprocessing steps for text)
- How to build an RNN using Tensorflow 2
- How to use a GRU and LSTM in Tensorflow 2
- How to do time series forecasting with Tensorflow 2
- How to predict stock prices and stock returns with LSTMs in Tensorflow 2 (hint: it’s not what you think!)
- How to use Embeddings in Tensorflow 2 for NLP
- How to build a Text Classification RNN for NLP (examples: spam detection, sentiment analysis, parts-of-speech tagging, named entity recognition)
Best for: Beginners Level | Course duration: 12 hrs | Provider: Udemy
Total Articles: 1
Info: Visit this course and get amazing offers on this course
Learn Artificial Neural Network From Scratch in Python
Rating: 4.9
If you’re looking for a complete Course on Deep Learning using ANN that teaches you everything you need to create a Neural Network model in Python?
After completing this course you will be able to:
- Identify the business problem which can be solved using Neural network Models.
- Have a clear understanding of Advanced Neural network concepts such as Gradient Descent, forward and Backward Propagation etc.
- Create Neural network models in Python and ability to optimize the model tuning hyper parameters
- Confidently practice, discuss and understand Deep Learning concepts
This course will get you started in building your FIRST artificial neural network using deep learning techniques. Following my previous course on logistic regression, we take this basic building block, and build full-on non-linear neural networks right out of the gate using Python and Numpy.
This course teaches you all the steps of creating a Neural network based model i.e. a Deep Learning model, to solve business problems.
Best for: Beginners Level | Course duration: 18 hrs | Provider: Udemy
Downloadable resources: 15
Info: Visit this course and get amazing offers on this course
Convolutional Neural Networks in Python: CNN Computer Vision
Rating: 4.6
You’re looking for a complete Convolutional Neural Network (CNN) course that teaches you everything you need to create a Image Recognition model in Python, right?
You’ve found the right Convolutional Neural Networks course!
After completing this course you will be able to:
- Identify the Image Recognition problems which can be solved using CNN Models.
- Create CNN models in Python using Keras and Tensorflow libraries and analyze their results.
- Confidently practice, discuss and understand Deep Learning concepts
- Have a clear understanding of Advanced Image Recognition models such as LeNet, GoogleNet, VGG16 etc.
This course covers all the steps that one should take to create an image recognition model using Convolutional Neural Networks.
Most courses only focus on teaching how to run the analysis but we believe that having a strong theoretical understanding of the concepts enables us to create a good model . And after running the analysis, one should be able to judge how good the model is and interpret the results to actually be able to help the business.
Best for: Beginners Level | Course duration: 4 hrs | Provider: Udemy
Total Articles: 4
Info: Visit this Course and get amazing offers on this course
And here the list ends. So, these are the Best Neural Networks Courses on Udemy for Beginners to Advanced. I will keep adding more Best Neural Networks on Udemy to this list.
Conclusion
I hope these Best Neural Networks Courses on Udemy 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.