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
| Company | Course | Rank | Price | Details |
|---|---|---|---|---|
 | 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.
Data Science Career Track – Springboard
Rating
4.7 on SwitchUp
Overview
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.
Best For
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
Duration
You’ll complete the course in about six months if you devote around 20 hours a week to studying and practicing.
Price
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.
Deep Learning Specialization – DeepLearning.AI on Coursera
Rating
4.9 on Coursera
Overview
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.
Related Read: RNN vs. CNN: Which Neural Network Is Right for Your Project?
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.
Best For
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.
Duration
The course takes approximately five months to complete, with around 9 hours of study per week.
Price
You can enroll for free and pay the $49 per month Coursera subscription fee after the 7-day free trial.
Stanford CS224N: Natural Language Processing with Deep Learning – Stanford Online on Youtube
Rating
N/A
Overview
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.
Best For
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.
Duration
The course consists of 23 video lectures between 1 and 2 hours each.
Price
The course is available for free on YouTube.
Deep Learning A-Z™: Hands-On Artificial Neural Networks (Udemy)
Rating
4.6 on Udemy
Overview
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.
Best For
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.
Duration
You can go through this course at your own pace. It includes less than 23 hours of video content and downloadable resources and articles.
Price
Udemy offers this course at an 80% discount, meaning you only need to pay $16.99.
Intro to Deep Learning with PyTorch – Facebook Artificial Intelligence on Udacity
Rating
4.5 on Class Central
Overview
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.
Best For
The Introduction to PyTorch course is best for students familiar with Python libraries and data processing.
Get To Know Other Data Science Students
Bret Marshall
Software Engineer at Growers Edge
Garrick Chu
Contract Data Engineer at Meta
Leoman Momoh
Senior Data Engineer at Enterprise Products
Duration
It takes around two months to go through the course materials and graduate.
Price
The Introduction to PyTorch course is free to access.
Practical Deep Learning for Coders 2022 – fast.ai
Rating
3.8 on Class Central
Overview
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.
Best For
This course is best for people with coding experience and deep learning practitioners alike.
Duration
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.
Price
The Practical Deep Learning course is free to access.
Deep Learning Prerequisites: Logistic Regression in Python (Lazy Programmer Inc. on Udemy)
Rating
4.7 on Udemy
Overview
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.
Best For
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.
Duration
The course includes almost 7 hours of video content, so you can complete it at your own pace.
Price
The course costs $19.99.
Professional Certificate in Deep Learning – IBM on edX
Rating
4.1 on Class Central
Overview
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.
Best For
The course is designed for candidates familiar with Python and basic programming languages.
Duration
The course lasts for seven months, with 2 to 4 hours of study per week.
Price
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.
Curriculum
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.
Career Goals
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).
Schedule
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.
Reputation
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
Companies are no longer just collecting data. They’re seeking to use it to outpace competitors, especially with the rise of AI and advanced analytics techniques. Between organizations and these techniques are the data scientists – the experts who crunch numbers and translate them into actionable strategies. The future, it seems, belongs to those who can decipher the story hidden within the data, making the role of data scientists more important than ever.
In this article, we’ll look at 13 careers in data science, analyzing the roles and responsibilities and how to land that specific job in the best way. Whether you’re more drawn out to the creative side or interested in the strategy planning part of data architecture, there’s a niche for you.
Is Data Science A Good Career?
Yes. Besides being a field that comes with competitive salaries, the demand for data scientists continues to increase as they have an enormous impact on their organizations. It’s an interdisciplinary field that keeps the work varied and interesting.
10 Data Science Careers To Consider
Whether you want to change careers or land your first job in the field, here are 13 of the most lucrative data science careers to consider.
Data Scientist
Data scientists represent the foundation of the data science department. At the core of their role is the ability to analyze and interpret complex digital data, such as usage statistics, sales figures, logistics, or market research – all depending on the field they operate in.
They combine their computer science, statistics, and mathematics expertise to process and model data, then interpret the outcomes to create actionable plans for companies.
General Requirements
A data scientist’s career starts with a solid mathematical foundation, whether it’s interpreting the results of an A/B test or optimizing a marketing campaign. Data scientists should have programming expertise (primarily in Python and R) and strong data manipulation skills.
Although a university degree is not always required beyond their on-the-job experience, data scientists need a bunch of data science courses and certifications that demonstrate their expertise and willingness to learn.
Average Salary
The average salary of a data scientist in the US is $156,363 per year.
Data Analyst
A data analyst explores the nitty-gritty of data to uncover patterns, trends, and insights that are not always immediately apparent. They collect, process, and perform statistical analysis on large datasets and translate numbers and data to inform business decisions.
A typical day in their life can involve using tools like Excel or SQL and more advanced reporting tools like Power BI or Tableau to create dashboards and reports or visualize data for stakeholders. With that in mind, they have a unique skill set that allows them to act as a bridge between an organization’s technical and business sides.
General Requirements
To become a data analyst, you should have basic programming skills and proficiency in several data analysis tools. A lot of data analysts turn to specialized courses or data science bootcamps to acquire these skills.
For example, Coursera offers courses like Google’s Data Analytics Professional Certificate or IBM’s Data Analyst Professional Certificate, which are well-regarded in the industry. A bachelor’s degree in fields like computer science, statistics, or economics is standard, but many data analysts also come from diverse backgrounds like business, finance, or even social sciences.
Average Salary
The average base salary of a data analyst is $76,892 per year.
Business Analyst
Business analysts often have an essential role in an organization, driving change and improvement. That’s because their main role is to understand business challenges and needs and translate them into solutions through data analysis, process improvement, or resource allocation.
A typical day as a business analyst involves conducting market analysis, assessing business processes, or developing strategies to address areas of improvement. They use a variety of tools and methodologies, like SWOT analysis, to evaluate business models and their integration with technology.
General Requirements
Business analysts often have related degrees, such as BAs in Business Administration, Computer Science, or IT. Some roles might require or favor a master’s degree, especially in more complex industries or corporate environments.
Employers also value a business analyst’s knowledge of project management principles like Agile or Scrum and the ability to think critically and make well-informed decisions.
Average Salary
A business analyst can earn an average of $84,435 per year.
Database Administrator
The role of a database administrator is multifaceted. Their responsibilities include managing an organization’s database servers and application tools.
A DBA manages, backs up, and secures the data, making sure the database is available to all the necessary users and is performing correctly. They are also responsible for setting up user accounts and regulating access to the database. DBAs need to stay updated with the latest trends in database management and seek ways to improve database performance and capacity. As such, they collaborate closely with IT and database programmers.
General Requirements
Becoming a database administrator typically requires a solid educational foundation, such as a BA degree in data science-related fields. Nonetheless, it’s not all about the degree because real-world skills matter a lot. Aspiring database administrators should learn database languages, with SQL being the key player. They should also get their hands dirty with popular database systems like Oracle and Microsoft SQL Server.
Average Salary
Database administrators earn an average salary of $77,391 annually.
Data Engineer
Successful data engineers construct and maintain the infrastructure that allows the data to flow seamlessly. Besides understanding data ecosystems on the day-to-day, they build and oversee the pipelines that gather data from various sources so as to make data more accessible for those who need to analyze it (e.g., data analysts).
General Requirements
Data engineering is a role that demands not just technical expertise in tools like SQL, Python, and Hadoop but also a creative problem-solving approach to tackle the complex challenges of managing massive amounts of data efficiently.
Usually, employers look for credentials like university degrees or advanced data science courses and bootcamps.
Average Salary
Data engineers earn a whooping average salary of $125,180 per year.
Database Architect
A database architect’s main responsibility involves designing the entire blueprint of a data management system, much like an architect who sketches the plan for a building. They lay down the groundwork for an efficient and scalable data infrastructure.
Their day-to-day work is a fascinating mix of big-picture thinking and intricate detail management. They decide how to store, consume, integrate, and manage data by different business systems.
General Requirements
If you’re aiming to excel as a database architect but don’t necessarily want to pursue a degree, you could start honing your technical skills. Become proficient in database systems like MySQL or Oracle, and learn data modeling tools like ERwin. Don’t forget programming languages – SQL, Python, or Java.
If you want to take it one step further, pursue a credential like the Certified Data Management Professional (CDMP) or the Data Science Bootcamp by Springboard.
Average Salary
Data architecture is a very lucrative career. A database architect can earn an average of $165,383 per year.
Machine Learning Engineer
A machine learning engineer experiments with various machine learning models and algorithms, fine-tuning them for specific tasks like image recognition, natural language processing, or predictive analytics. Machine learning engineers also collaborate closely with data scientists and analysts to understand the requirements and limitations of data and translate these insights into solutions.
General Requirements
As a rule of thumb, machine learning engineers must be proficient in programming languages like Python or Java, and be familiar with machine learning frameworks like TensorFlow or PyTorch. To successfully pursue this career, you can either choose to undergo a degree or enroll in courses and follow a self-study approach.
Average Salary
Depending heavily on the company’s size, machine learning engineers can earn between $125K and $187K per year, one of the highest-paying AI careers.
Quantitative Analyst
Qualitative analysts are essential for financial institutions, where they apply mathematical and statistical methods to analyze financial markets and assess risks. They are the brains behind complex models that predict market trends, evaluate investment strategies, and assist in making informed financial decisions.
They often deal with derivatives pricing, algorithmic trading, and risk management strategies, requiring a deep understanding of both finance and mathematics.
General Requirements
This data science role demands strong analytical skills, proficiency in mathematics and statistics, and a good grasp of financial theory. It always helps if you come from a finance-related background.
Average Salary
A quantitative analyst earns an average of $173,307 per year.
Data Mining Specialist
A data mining specialist uses their statistics and machine learning expertise to reveal patterns and insights that can solve problems. They swift through huge amounts of data, applying algorithms and data mining techniques to identify correlations and anomalies. In addition to these, data mining specialists are also essential for organizations to predict future trends and behaviors.
General Requirements
If you want to land a career in data mining, you should possess a degree or have a solid background in computer science, statistics, or a related field.
Average Salary
Data mining specialists earn $109,023 per year.
Data Visualisation Engineer
Data visualisation engineers specialize in transforming data into visually appealing graphical representations, much like a data storyteller. A big part of their day involves working with data analysts and business teams to understand the data’s context.
General Requirements
Data visualization engineers need a strong foundation in data analysis and be proficient in programming languages often used in data visualization, such as JavaScript, Python, or R. A valuable addition to their already-existing experience is a bit of expertise in design principles to allow them to create visualizations.
Average Salary
The average annual pay of a data visualization engineer is $103,031.
Resources To Find Data Science Jobs
The key to finding a good data science job is knowing where to look without procrastinating. To make sure you leverage the right platforms, read on.
Job Boards
When hunting for data science jobs, both niche job boards and general ones can be treasure troves of opportunity.
Niche boards are created specifically for data science and related fields, offering listings that cut through the noise of broader job markets. Meanwhile, general job boards can have hidden gems and opportunities.
Online Communities
Spend time on platforms like Slack, Discord, GitHub, or IndieHackers, as they are a space to share knowledge, collaborate on projects, and find job openings posted by community members.
Network And LinkedIn
Don’t forget about socials like LinkedIn or Twitter. The LinkedIn Jobs section, in particular, is a useful resource, offering a wide range of opportunities and the ability to directly reach out to hiring managers or apply for positions. Just make sure not to apply through the “Easy Apply” options, as you’ll be competing with thousands of applicants who bring nothing unique to the table.
FAQs about Data Science Careers
We answer your most frequently asked questions.
Do I Need A Degree For Data Science?
A degree is not a set-in-stone requirement to become a data scientist. It’s true many data scientists hold a BA’s or MA’s degree, but these just provide foundational knowledge. It’s up to you to pursue further education through courses or bootcamps or work on projects that enhance your expertise. What matters most is your ability to demonstrate proficiency in data science concepts and tools.
Does Data Science Need Coding?
Yes. Coding is essential for data manipulation and analysis, especially knowledge of programming languages like Python and R.
Is Data Science A Lot Of Math?
It depends on the career you want to pursue. Data science involves quite a lot of math, particularly in areas like statistics, probability, and linear algebra.
What Skills Do You Need To Land an Entry-Level Data Science Position?
To land an entry-level job in data science, you should be proficient in several areas. As mentioned above, knowledge of programming languages is essential, and you should also have a good understanding of statistical analysis and machine learning. Soft skills are equally valuable, so make sure you’re acing problem-solving, critical thinking, and effective communication.
Since you’re here…Are you interested in this career track? Investigate with our free guide to what a data professional actually does. When you’re ready to build a CV that will make hiring managers melt, join our Data Science Bootcamp which will help you land a job or your tuition back!
