The editors at Solutions Review have compiled this list of the best deep learning courses on Udemy to consider if you’re looking to grow your skills.
Deep learning is a class of machine learning algorithms that uses multiple layers to progressively extract higher-level features from the raw input. Based on artificial neural networks and representation learning, deep learning can be supervised, semi-supervised or unsupervised. Deep learning models are commonly based on convolutional neural networks but can also include propositional f formulas or latent variables organized by layer.
With this in mind, we’ve compiled this list of the best deep learning courses on Udemy if you’re looking to grow your skills for work or play. Udemy is one of the top online education platforms in the world with more than 130,000 courses, expert instruction, and lifetime access that allows you to learn on your own schedule. As you can see below, we broke the best deep learning courses on Udemy down into categories based on the recommended proficiency level. Each section also features our inclusion criteria. Click GO TO TRAINING to learn more and register.
Note: We included courses with more than 100 reviews and a rating of 4.1 stars or better.
Description: If you’ve got some programming or scripting experience, this course will teach you the techniques used by real data scientists and machine learning practitioners in the tech industry – and prepare you for a move into this hot career path. This comprehensive machine learning tutorial includes over 100 lectures spanning 14 hours of video, and most topics include hands-on Python code examples you can use for reference and for practice. Each concept is introduced in plain English, avoiding confusing mathematical notation and jargon. It’s then demonstrated using Python code you can experiment with and build upon, along with notes you can keep for future reference.
Description: Visual introduction to deep learning is based on a simple deep neural network. Take this course if you want to understand the magic behind deep neural networks and to get an excellent visual intuition on what is happening under the hood when data is traveling through the network and ends up as a prediction at its output. You will visually see what exactly they are doing and how neural networks use these components to come up with accurate predictions.
Description: Deep Learning is one of the fastest-growing areas of artificial intelligence. In the past few years, it has proven that deep learning models, even the simplest ones, can solve very hard and complex tasks. Now that the buzz-word period of deep learning has partially, passed, people are releasing its power and potential for their product improvements. The course is structured in a way that covers all topics from neural network modeling and training to put it in production.
Description: This course is for you if you are new to machine learning but want to learn it without all the math. This course is also for you if you have had a machine learning course but could never figure out how to use it to solve your own problems. This is a very applied course, so readers will immediately start coding even without installation! You will see a brief bit of absolutely essential theory and then we will get into the environment setup and explain almost all concepts through code. You will be using Keras.
Description: TensorFlow is quickly becoming the technology of choice for deep learning, because of how easy TF makes it to build powerful and sophisticated neural networks. The Google Cloud Platform is a great place to run TF models at scale, and perform distributed training and prediction. This is a comprehensive, from-the-basics course on TensorFlow and building neural networks. It assumes no prior knowledge of Tensorflow, all you need to know is basic Python programming.
Description: After endless posts on forums, tutorials, and blogs, the instructors have documented a seamless guide in the form of this course; which will show you, step-by-step, on how to implement your own deep learning object detection models on video and webcam without all the wasteful debugging. So essentially, this training has been structured to reduce debugging, speed up your time to market, and get you results sooner.
Note: We included courses with more than 1,000 reviews and a rating of 4.5 stars or better.
Description: This course will guide you through how to use Google’s TensorFlow framework to create artificial neural networks for deep learning! This course aims to give you an easy-to-understand guide to the complexities of Google’s TensorFlow framework in a way that is easy to understand. This course is designed to balance theory and practical implementation, with complete Jupyter notebook guides of code and easy to reference slides and notes.
Description: This course will get you started in building your first artificial neural network using deep learning techniques. 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. This course provides you with many practical examples so that you can really see how deep learning can be used on anything.
Description: This course is your best resource for learning how to use the Python programming language for computer vision. Students will explore how to use Python and the OpenCV (open computer vision) library to analyze images and video data. Students start the course by learning about numerical processing with the NumPy library and how to open and manipulate images with NumPy.
Description: In this course, you’ll see how we can turn a CNN into an object detection system, that not only classifies images but can locate each object in an image and predict its label. Students will be looking at a state-of-the-art algorithm called SSD which is both faster and more accurate than its predecessors. Instructors will also introduce you to the now-famous GAN architecture (Generative Adversarial Networks), where you will learn some of the technology behind how neural networks are used to generate state-of-the-art, photo-realistic images.
Description: This course is designed to provide a complete introduction to Deep Learning. It is aimed at beginners and intermediate programmers and data scientists who are familiar with Python and want to understand and apply Deep Learning techniques to a variety of problems. This course is a good balance between theory and practice. We don’t shy away from explaining mathematical details and at the same time we provide exercises and sample code to apply what you’ve just learned.
Description: This course is a big bag of tricks that make recommender systems work across multiple platforms. Students will look at popular news feed algorithms, like Reddit, Hacker News, and Google PageRank. Learners will also look at Bayesian recommendation techniques that are being used by a large number of media companies today.
Description: In this new course, students will learn the structure of data in order to produce more stuff that resembles the original data. This by itself is really cool, but learners will also be incorporating ideas from Bayesian machine learning, reinforcement learning, and game theory. That makes it even cooler! This training module is recommenced for anyone who wants to improve their deep learning knowledge.
Description: If you are interested in deep learning and you want to learn about modern deep learning developments beyond just plain backpropagation, including using unsupervised neural networks to interpret what features can be automatically and hierarchically learned in a deep learning system, this course is for you. 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.
Note: We included courses with more than 800 reviews and a rating of 4.6 stars or better.
Description: 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.
Description: This course is all about the application of deep learning and neural networks to reinforcement learning. Specifically, the combination of deep learning with reinforcement learning has led to AlphaGo beating a world champion in the strategy game Go, it has led to self-driving cars, and it has led to machines that can play video games at a superhuman level.
Description: This course enables students to explore a variety of environments. First, students will look at the classic Atari environments. These are important because they show that reinforcement learning agents can learn based on images alone. Second, learners explore MuJoCo, which is a physics simulator. This is the first step to building a robot that can navigate the real-world and understand physics – we first have to show it can work with simulated physics.
Note: We included courses with more than 800 reviews and a rating of 4.4 stars or better.
Description: In Deep Learning A-Z, the instructors code together with you. Every practical tutorial starts with a blank page and students write up the code from scratch. This way you can follow along and understand exactly how the code comes together and what each line means. In addition, teachers will purposefully structure the code in such a way so that you can download it and apply it to your own projects. They will be explained step-by-step where and how to modify the code to insert your dataset to tailor the algorithm to your needs.
Description: This course was designed for students and professionals with little Numpy experience who plan to learn deep learning and machine learning later. It is also ideal for students and professionals who have tried machine learning and data science but are having trouble putting the ideas down in code. If you’ve taken a deep learning or machine learning course, and you understand the theory, and you can see the code, but you can’t make the connection between how to turn those algorithms into actual running code, this course is for you.
Description: 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). Tensorflow is the world’s most popular library for deep learning, and it’s built by Google, whose parent Alphabet recently became the most cash-rich company in the world. It is the library of choice for many companies doing AI and machine learning.
Description: This course will guide you through how to use Google’s latest TensorFlow 2 framework to create artificial neural networks for deep learning. This course aims to give you an easy-to-understand guide to the complexities of Google’s TensorFlow 2 framework in a way that is easy to understand. Instructors focus on understanding the latest updates to TensorFlow and leveraging the Keras API (TensorFlow 2.0’s official API) to quickly and easily build models.
Description: This course teaches you about one popular technique used in machine learning, data science and statistics: linear regression. Instructors cover the theory from the ground up: derivation of the solution, and applications to real-world problems. Teachers show you how one might code their own linear regression module in Python as well. Linear regression is the simplest machine learning model you can learn, yet there is so much depth that you’ll be returning to it for years to come.
Description: 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. All of the materials required for this course can be downloaded and installed for free. Students will do most of their work in Numpy, Matplotlib, and Tensorflow.
Description: 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. All of the materials required for this course can be downloaded and installed for free.
Description: This course is a lead-in to deep learning and neural networks – it covers a popular and fundamental technique used in machine learning, data science and statistics: logistic regression. Instructors cover the theory from the ground up: derivation of the solution, and applications to real-world problems. Teachers also show you how one might code their own logistic regression module in Python.
Description: With this new course, you will not only learn how the most popular computer vision methods work, but you will also learn to apply them in practice. The only requirement for registration is basic Python programming knowledge. This training module was designed for anyone interested in computer vision or artificial intelligence.
Description: After completing this course you will be able to confidently build predictive machine learning and deep learning models to solve business problems and create business strategy, answer machinee learning-related interview questions, and participate and perform in online data analytics competitions like Kaggle. If you are a business manager or an executive, or a student who wants to learn and apply machine learning in real-world problems of business, this course will give you a solid base for that by teaching you the most popular techniques of machine learning.
Description: If you want to improve your skills with neural networks and deep learning, this is the course for you. This course is for students and professionals who want to deepen their machine learning knowledge, data scientists who want to learn more about deep learning, and those who do not yet know about backpropagation or softmax. Prerequisites include skills in gradient descent and probability and statistics.
Description: 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.
Description: This course aims to sharpen your linear regression and statistical skills. Linear regression is a starting point for data science and this training module is focused on making your foundation strong for deep learning and machine learning algorithms. In this course, the instructor explains hypothesis testing, unbiased estimators, statistical testing, gradient descent, and more. By the end of the training, you will be able to code your own regression algorithm from scratch.