IN THIS ARTICLE
- How Important Is a Deep Learning Course?
- Best Deep Learning Courses
- How Do You Choose a Deep Learning Course?
- Making the Most Out of Your Deep Learning Course
- FAQs About Deep Learning Courses
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Deep learning—a branch of machine learning that uses artificial neural networks to learn how to perform tasks—is a powerful tool for solving real-world problems, from image recognition to natural language processing. And as more companies begin using artificial intelligence, demand for workers with deep learning skills will only increase. So if you’re interested in applying for jobs requiring these skills or simply want to increase your marketability as a data scientist, taking a deep learning course will help you stand out from the crowd.
However, with so many courses, how do you know which one to choose? That’s why we’ve compiled this guide. Below, we’ll cover the eight best deep learning courses and give an overview to help you decide which is best. Let’s dive right into it!
How Important Is a Deep Learning Course?
Deep learning courses will help you develop a critical eye for identifying problems and solutions, which is an essential skill for anyone who wants to work in this field. It’s a great way to get your foot in the door of the AI world, and will give you some extra skills to put on your resume.
Best Deep Learning Courses
|Data Science Career Track||4.7||$9,900||Learn More|
|Deep Learning Specialization||4.9||$49||Learn More|
|Natural Language Processing with Deep Learning||N/A||N/A||Learn More|
|Deep Learning A-Z™||4.6||$16.99||Learn More|
|Intro to Deep Learning with PyTorch||4.5||N/A||Learn More|
|Practical Deep Learning||3.8||N/A||Learn More|
|Deep Learning Prerequisites: Logistic Regression in Python||4.7||$19.99||Learn More|
|Professional Certificate in Deep Learning||4.1||$525.60||Learn More|
Maybe you’re a beginner or an advanced practitioner looking to take your skillset to the next level. With so many options, it can be overwhelming to figure out which online courses are worth your time.
That’s why we’ve put together this list of the best deep learning courses for beginners and advanced students alike.
4.7 on SwitchUp
The Springboard Data Science Bootcamp is the best way to build your data science skills and machine learning skills on your own schedule. With Springboard’s flexible, remote program and one-on-one support at every step, you’ll graduate in six months, land your dream job, or receive a refund.
Springboard’s unique curriculum focuses on practical projects that teach the skills employers are looking for—like how to use Python for machine learning and analysis, how to clean and wrangle messy data, and how to build software from scratch—so you can show off what you’ve learned at career fairs.
Before enrolling in the Data Science Bootcamp, you should have at least six months of coding experience with a programming language such as Python or Java.
You can also choose to enroll in this program if:
- You have the desire to work with data but don’t know where to start
- You want to work in the deep learning industry but don’t have a lot of experience
- You want to make a career change and are willing to put in the work
You’ll complete the course in about six months if you devote around 20 hours a week to studying and practicing.
The Springboard Data Science Bootcamp pricing is pretty straightforward. If you pay upfront, you get a 13% discount and only pay $9,900. You can also choose to pay $1,890 in monthly installments or pay only after you land a job.
4.9 on Coursera
If you’ve ever wanted to learn the fundamentals of deep learning, this course is for you. The Deep Learning Specialization course from Coursera will teach you how to build and train neural networks—an exciting branch of machine learning that has recently made huge strides in computer vision, natural language processing, image recognition, and speech recognition.
You’ll also learn how to use deep learning algorithms like convolutional neural networks (CNNs) and recurrent neural networks (RNNs) to solve problems in computer vision, natural language processing, speech recognition, and more.
With a basic understanding of theory and practice, you can use what you learn in this course to build real-world solutions for complex problems in many fields.
The Deep Learning Specialization Course from Coursera is best for anyone who wants to learn more about the latest developments in artificial intelligence, as it gives you a practical introduction to machine learning. It’s also a great choice if you’re interested in applying your knowledge of deep learning in your own work or study.
The course is designed to be accessible to students at all levels, as it focuses on giving you a solid foundation of knowledge so that you can understand and apply deep learning concepts, even if you haven’t taken any related courses before.
However, you should have basic programming skills and basic knowledge of machine learning and programming languages such as Python.
The course takes approximately five months to complete, with around 9 hours of study per week.
You can enroll for free and pay the $49 per month Coursera subscription fee after the 7-day free trial.
If you’re looking for a free deep-learning course, look no further than Stanford University’s CS224N. This course is an online video series on YouTube that costs nothing. It covers key topics like convolutional neural networks, recurrent neural networks, and deep learning models—all essential to understanding how artificial intelligence works in today’s world.
The course covers a lot of ground—from the basics to more advanced topics like adversarial networks and word embeddings. You’ll also learn about loss function and optimization methods like backpropagation and stochastic gradient descent.
If you’re interested in how computers learn to recognize patterns in data—and how you can use them to solve problems that have traditionally been the domain of humans—this is the course for you.
The course consists of 23 video lectures between 1 and 2 hours each.
The course is available for free on YouTube.
4.6 on Udemy
With the Deep Learning A-Z™ course from Udemy, you’ll learn to create deep learning algorithms using Python. You’ll be taught by machine learning and data science professionals with experience in machine learning and building real-world applications of artificial intelligence.
The course is practical, meaning that you’ll learn by doing. You’ll work through real-world examples and projects, each building on the last so that you can see how everything fits together.
This course is perfect for anyone looking to enter the field of data science or learn about new machine learning technologies that are changing the way we live and work. You should have basic Python knowledge and a deep understanding of mathematics.
You can go through this course at your own pace. It includes less than 23 hours of video content and downloadable resources and articles.
Udemy offers this course at an 80% discount, meaning you only need to pay $16.99.
4.5 on Class Central
This course is designed to teach you how to implement your first deep neural network using PyTorch. You’ll learn the basics of deep learning frameworks and get practical experience by implementing various neural networks.
Throughout this course, you’ll learn how to use the PyTorch library to build neural networks: from basic feed-forward networks to convolutional networks, recurrent neural networks (RNNs), and more.
You’ll also learn how to set up your environment for deep learning using Jupyter Notebooks, an excellent tool for performing data science tasks on your computer. And you’ll spend some time looking at how you can use TensorBoard—a data visualization tool built into PyTorch—to understand what’s happening inside your network during training.
The Introduction to PyTorch course is best for students familiar with Python libraries and data processing.
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It takes around two months to go through the course materials and graduate.
The Introduction to PyTorch course is free to access.
3.8 on Class Central
The fast.ai Practical Deep Learning course is a hands-on introduction to applying deep learning to real-world problems. You’ll learn to deploy machine learning models and use PyTorch, the state-of-the-art open-source library for deep learning research and applications.
The course focuses on practical skills you can use on your own projects and share with the community on the forum at fast.ai. The fast.ai community is made up of practitioners from all over the world who support one another in their projects, so you’ll get feedback from expert practitioners and other learners.
This course is best for people with coding experience and deep learning practitioners alike.
The fast.ai course is broken down into nine lessons. Each lesson is approximately 90 minutes long, and the course takes around 10 hours to complete.
The Practical Deep Learning course is free to access.
4.7 on Udemy
The Deep Learning Prerequisites: Logistic Regression in Python on Udemy course is designed to help you learn how to use Python for data science.
In this course, you will go through hands-on projects in real-time, so you can quickly gain the skills needed for your next job or project. You will also understand the basics of logistic regression algorithms, a prerequisite for many fundamental machine learning courses.
Before you take the Deep Learning Prerequisites: Logistic Regression in Python course, you should know some basic Python coding with the Numpy Stack. This includes derivatives, matrix arithmetic, and probability.
The course includes almost 7 hours of video content, so you can complete it at your own pace.
The course costs $19.99.
4.1 on Class Central
If you’re looking to get started with deep learning or just want to gain a basic understanding of machine learning, then the Professional Certificate in Deep Learning is for you. This deep learning program is designed to give professionals the skills they need to use deep learning algorithms in real-world scenarios. You will learn about the fundamentals of machine learning and its applications, as well as how to use popular deep learning libraries.
The course is designed for candidates familiar with Python and basic programming languages.
The course lasts for seven months, with 2 to 4 hours of study per week.
The full program costs $525.60.
How Do You Choose a Deep Learning Course?
Here are some factors to consider when picking a deep learning course.
The first thing to consider is the curriculum of the course. A good course will balance theory and practice and cover both supervised and unsupervised learning methods. It should also include practical examples to see how these methods work in real-life situations. Does the curriculum include courses that will help you reach your specific career goals? Do they cover all of the relevant topics? If not, you can always supplement with other classes or tutorials on YouTube or elsewhere online.
If you want to get into academia, look for courses emphasizing research. But if you wish to build a career in machine learning and become a data scientist, look for courses emphasizing practical applications of deep learning. And if you want to make some extra cash with freelancing gigs, look for courses that teach how to build self-sufficient projects using deep learning algorithms.
Expertise of Instructors
You want instructors who are deeply knowledgeable and passionate about their subject matter—and able to convey that passion in their teaching style. The best teachers know how to make learning fun but also challenge students with complex concepts so they can grow as learners and professionals.
Pricing and Payment Options
Some courses charge a flat fee for access to all lectures, while others charge per lecture. Paid courses almost always include a certificate that shows you’ve completed the course—this can be useful if you’re looking for a job in the field and need some kind of proof of your skillset.
Some courses also offer a free trial period so that you can try them before you buy. Just be sure that what they offer during this period matches what they promise in the full course (i.e., don’t expect to get all lectures for free if their website says otherwise).
Online courses may be for you if you want a more hands-off learning experience! Many online courses allow students to work at their own pace, so even if your busy schedule doesn’t allow for much time spent in front of a computer screen every day, there are still options available for you.
One important thing to remember is that not all courses are created equal. It’s essential to look at the reputation of the course provider before signing up for one of their courses, especially if it’s a paid course.
Reputable providers will have positive reviews from previous students and be able to answer questions about their program thoroughly and honestly.
Projects and Practical Assignments
One thing distinguishing good machine learning courses from bad ones is the number of practical programming assignments and machine learning projects in each lesson.
When choosing a course, make sure each lesson has at least one project or assignment to test what you’ve learned and see how it applies to real-world scenarios.
Making the Most Out of Your Deep Learning Course
There are many ways to make the most of your deep learning course. Here is what you should keep in mind.
Are There Any Prerequisites for a Deep Learning Course?
If you are taking a course focused on the foundations of deep learning, it’s not necessary to have any prior programming knowledge in this area. If you’re taking a course focused on applying deep learning techniques to solve specific problems, background knowledge will likely help you get the most out of your class.
Remember that some advanced courses may require you to have previous programming experience with Python or R—while others may not. So if you’re unsure whether or not a particular course requires such knowledge, check with your professor before enrolling.
What Should You Expect To Learn in a Deep Learning Course?
If you’re trying to decide whether or not to take a deep learning course, you should first check out what the course covers. It’s important to know what you’re signing up for. Here are some of the things you can expect to learn in a deep learning course:
- The basic concepts of machine learning and neural networks;
- How to build your own neural network using Python and TensorFlow;
- How to train your neural network using data and machine learning techniques like gradient descent and backpropagation.
What Will Your Schedule Look Like?
Depending on the course you are taking, your schedule will look different. For example, if you’re taking a short course that involves only 1-2 hours of study per week, your schedule would allow you to pursue a part-time job or full-time study.
However, if you are enrolled in a longer course that requires more time and effort, it may take more time to complete. Before registering, ensure you have enough time to fit your studies around other commitments such as work or family life.
FAQs About Deep Learning Courses
We’ve got the answers to your most frequently asked questions.
Is Deep Learning Difficult To Learn?
Deep learning is a highly complex topic, but it’s not as challenging to learn as you might think. The key to learning deep learning is understanding the basics of machine learning and how an artificial neural network works.
Once you understand that, you can move on to more complex topics like convolutional networks and recurrent neural networks.
Can You Learn Deep Learning on Your Own?
If you have a math and statistics background, you can probably get started with some of the basics of deep learning without any additional training. You’ll need to understand what linear regression is, for example, and how it works. The same goes for neural networks—you’ll need to know what they are, why they work (and don’t), and how they differ from other types of machine learning algorithms.
Related Read: How To Build a Linear Regression Model from Scratch Using Python
If you don’t have a math or statistics background, you might want to take some courses before diving into self-study. Suppose you’re looking at online courses that teach deep learning as part of a specialization or certificate program. In that case, plenty of instruction will cover the topics above, plus more advanced concepts like gradient descent optimization methods and backpropagation algorithms
Do Companies Value Deep Learning Courses and Certifications?
Yes. Many companies are looking for candidates with a deep learning certification, or coursework on their machine learning resumes. They want to see that you’re serious about the subject and committed to learning more about it.
While a computer science or data science degree is still an excellent way to build your resume, some employers want to know that you’ve taken the time to get hands-on experience with deep learning. This can be done through online courses and certifications