In this article, you will find the Best Deep Learning Courses on Udemy & advanced like Beginner courses, and Practice test courses. So, check these Best Deep Learning Courses on Udemy and find the Best Deep Learning Courses on Udemy for Beginners to Advanced according to your need. In the previous article, I have shared the best Deep Learning Books for Beginners to advance that help you get practical skills with those courses.
Here we have covered the Best Deep Learning Courses on Udemy. Let’s go through the list of Udemy Deep Learning Courses one by one.
Best Deep Learning Courses on Udemy 2022
in this course you will have an opportunity to work with both and understand when Tensorflow is better and when PyTorch is the way to go. Throughout the tutorials we compare the two and give you tips and ideas on which could work best in certain circumstances.
There are lots of different tools in the space of Deep Learning and in this course we make sure to show you the most important and most progressive ones so that when you’re done with Deep Learning A-Z™ your skills are on the cutting edge of today’s technology.
If you are just starting out into Deep Learning, then you will find this course extremely useful. 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.
- 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 Boltzmann Machines
- Apply Boltzmann Machines in practice
Best for: Beginners and intermediate | Time Duration: 22.5hrs | Provider: Udemy
Total Articles: 38 | Total Downloadable resources: 5
The purpose of this course is to provide a deep-dive into deep learning. You will gain flexible, fundamental, and lasting expertise on deep learning. You will have a deep understanding of the fundamental concepts in deep learning, so that you will be able to learn new topics and trends that emerge in the future.
This is not a course for someone who wants a quick overview of deep learning with a few solved examples. Instead, this course is designed for people who really want to understand how and why deep learning works; when and how to select metaparameters like optimizers, normalizations, and learning rates; how to evaluate the performance of deep neural network models; and how to modify and adapt existing models to solve new problems.
- Theory: Why are deep learning models built the way they are?
- Math: What are the formulas and mechanisms of deep learning?
- Implementation: How are deep learning models actually constructed in Python (using the PyTorch library)?
- Intuition: Why is this or that metaparameter the right choice? How to interpret the effects of regularization? etc.
- Python: If you’re completely new to Python, go through the 8+ hour coding tutorial appendix. If you’re already a knowledgeable coder, then you’ll still learn some new tricks and code optimizations.
- Google-colab: Colab is an amazing online tool for running Python code, simulations, and heavy computations using Google’s cloud services. No need to install anything on your computer.
Best for: Beginners and intermediate | Time Duration: 57.5hrs | Provider: Udemy
Total Articles: 3 | Total Downloadable resources: 1
This course is designed for students who want to learn fast, but there are also “in-depth” sections in case you want to dig a little deeper into the theory (like what is a loss function, and what are the different types of gradient descent approaches).
This course focuses on breadth rather than depth, with less theory in favor of building more cool stuff. If you are looking for a more theory-dense course, this is not it. Generally, for each of these topics (recommender systems, natural language processing, reinforcement learning, computer vision, GANs, etc.) I already have courses singularly focused on those topics.
- Deploying a model with Tensorflow Serving (Tensorflow in the cloud)
- Deploying a model with Tensorflow Lite (mobile and embedded applications)
- Distributed Tensorflow training with Distribution Strategies
- Writing your own custom Tensorflow model
- Converting Tensorflow 1.x code to Tensorflow 2.0
- Constants, Variables, and Tensors
- Eager execution
- Gradient tape
- Use Tensorflow Serving to serve your model using a RESTful API
- Use Tensorflow’s Distribution Strategies to parallelize learning
- Natural Language Processing (NLP) with Deep Learning
- Artificial Neural Networks (ANNs) / Deep Neural Networks (DNNs)
Best for: Beginners and intermediate | Time Duration: 22hrs | Provider: Udemy
The course teaches you everything you need to know to become a data scientist at a fraction of the cost of traditional programs (not to mention the amount of time you will save). You should take this course if you want to become a Data Scientist or if you want to learn about the field.
The course is also ideal for beginners, as it starts from the fundamentals and gradually builds up your skills. Data science is all about predictive modelling and you can become an expert in these methods through this ‘advance statistics’ section.
This course doesn’t just give you the tools you need but teaches you how to use them. Statistics trains you to think like a scientist. When it comes to developing, implementing, and deploying machine learning models through powerful frameworks such as scikit-learn, TensorFlow, etc, Python is a must have programming language.
- The course provides the entire toolbox you need to become a data scientist
- Fill up your resume with in demand data science skills: Statistical analysis, Python programming with NumPy, pandas, matplotlib, and Seaborn, Advanced statistical analysis, Tableau, Machine Learning with stats models and scikit-learn, Deep learning with TensorFlow
- Understand the mathematics behind Machine Learning (an absolute must which other courses don’t teach!)
- Start coding in Python and learn how to use it for statistical analysis
- Perform linear and logistic regressions in Python
- Be able to create Machine Learning algorithms in Python, using NumPy, statsmodels and scikit-learn
- Use state-of-the-art Deep Learning frameworks such as Google’s TensorFlowDevelop a business intuition while coding and solving tasks with big data
Best for: Beginners and intermediate | Time Duration: 31hrs | Provider: Udemy
Total Articles: 92 | Total Downloadable resources: 542
This course focuses on balancing important theory concepts with practical hands-on exercises and projects that let you learn how to apply the concepts in the course to your own data sets! When you enroll in this course you will get access to carefully laid out notebooks that explain concepts in an easy to understand manner, including both code and explanations side by side. You will also get access to our slides that explain theory through easy to understand visualizations.
By the end of this course you will be able to create a wide variety of deep learning models to solve your own problems with your own data sets.
In this course we will teach you everything you need to know to get started with Deep Learning with Pytorch, including:
- Learn how to use NumPy to format data into arrays
- Use pandas for data manipulation and cleaning
- Learn classic machine learning theory principals
- Use PyTorch Deep Learning Library for image classification
- Use PyTorch with Recurrent Neural Networks for Sequence Time Series Data
- Create state of the art Deep Learning models to work with tabular data
Best for: Intermediate and Python Developers | Time Duration: 17hrs | Provider: Udemy
Total Articles: 2 | Total Downloadable resources: 2
This course is your best resource for learning how to use the Python programming language for Computer Vision.
You can start the course by learning about numerical processing with the NumPy library and how to open and manipulate images with NumPy. Then will move on to using the OpenCV library to open and work with image basics. Then we’ll start to understand how to process images and apply a variety of effects, including color mappings, blending, thresholds, gradients, and more.
Then we’ll move on to understanding video basics with OpenCV, including working with streaming video from a webcam. Afterwards we’ll learn about direct video topics, such as optical flow and object detection. Including face detection and object tracking.
Then we’ll move on to an entire section of the course devoted to the latest deep learning topics, including image recognition and custom image classifications. We’ll even cover the latest deep learning networks, including the YOLO (you only look once) deep learning network.
- Perform image manipulation with OpenCV, including smoothing, blurring, thresholding, and morphological operations.
- Use Python and OpenCV to draw shapes on images and videos
- Open and Stream video with Python and OpenCV
- Detect Objects, including corner, edge, and grid detection techniques with OpenCV and Python
- Work with Tensorflow, Keras, and Python to train on your own custom images.
- Use Python and Deep Learning to build image classifiers
Best for: Intermediate and Python Developers | Time Duration: 14hrs | Provider: Udemy
Total Articles: 4 | Total Downloadable resources: 3
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).
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.
You will learn about modern techniques such as data augmentation and batch normalization, and build modern architectures such as VGG yourself.
- 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: Intermediate and Developers | Time Duration: 12hrs | Provider: Udemy
Total Articles: 4 | Total Downloadable resources: 3
And here the list ends. So, these are the Best Deep Learning Courses on Udemy for Beginners to Advanced. I will keep adding more Best Deep Learning on Udemy to this list.
I hope these Best Deep Learning 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.