The Best Way to Learn Deep Learning

Deep learning is a form of artificial intelligence (AI) that enables machines to learn and process information in a way that is similar to the human brain. Advances in this field are supporting the development of a range of exciting technologies, from chatbots and digital assistants to fraud detection and virtual reality.

AI and deep learning are transforming the world, and companies are investing large sums of money in this industry. That rapid adoption is creating jobs and opportunities across multiple industries if you have the right skills. The global deep learning market was valued at $96.8 billion in 2024 and is likely to grow at a compounded rate of 31.8 percent per year from 2025 to 2030 [1]. So if you’re considering a career in AI, now might be the best time to get started with building your deep learning skills. Discover the best way to learn deep learning according to your unique learning journey. Whether you already have experience, are new to the subject, or you like to learn theoretically or in a practical way, find out about courses, degrees, and other paths that can help you reach your goals.

What is deep learning?

As a subset of machine learning, deep learning teaches computers and machines to learn and make decisions in a way that simulates the human brain. Deep learning uses multilayered neural networks, imitating neural pathways in the brain, to support computers in mastering image and speech recognition, problem-solving, and making accurate predictions. 

Deep learning is enhancing the development of automation tools, techniques, and technology, including self-driving cars, credit card fraud detection, and digital voice assistants, such as Siri and Google Home. 

Prerequisites for learning deep learning

Deep learning is complex, and you will need a solid foundation in math, machine learning, and programming to effectively grasp deep learning concepts. If you are new to the field, consider working on the following:

Machine learning principles: Since deep learning is a subset of machine learning, understanding machine learning concepts and having some experience can help you. 

Programming: Having experience in programming is important, especially a fundamental working understanding of Python.

Math: Build a strong understanding of linear algebra, statistics, and advanced calculus.

The best way to learn deep learning

Deep learning serves as the basis of much of the AI technology available. With the rapid growth of AI technologies and increased AI adoption across industries, the number of jobs that require knowledge of AI concepts, such as deep learning, will likely increase. Thus, building your understanding of deep learning can help you command higher salaries and find better job prospects.

Explore a range of ways to learn and develop your deep learning skills related to AI and machine learning. Everybody learns differently, and you’ll have your own preferences on how best to develop your skills. The tips below start with the basics, which you’ll need if you’re just starting out, and go up to more advanced ways to learn deep learning. Choose the options that are most suitable for you personally. 

Start with machine learning

Machine learning is a good starting point for progressing to deep learning if you are completely new to the field since deep learning uses a machine learning model at its core. As deep learning is a highly technical field, you could benefit from a degree in a machine learning-related subject such as computer science, software engineering, or information technology. From here, you might move on to an advanced degree such as a master’s degree in artificial intelligence.

Learn Python as a starting point

Programming and coding, particularly Python, are important for working in deep learning. In addition to learning Python as a language, you must develop your understanding of deep learning frameworks like TensorFlow, Keras, and PyTorch. It makes sense to choose one framework that appeals to you and learn it thoroughly, rather than trying to learn too many.

Engage in GitHub projects

Building your own deep learning projects is a great way to develop your deep learning skills. You can use GitHub to take part in hackathons, object recognition programs, music recommendation systems, and text summarization tools.

Take an online course

Taking an online course is a way of gaining practical experience at your own pace while earning a certificate or certification to add to your resume. Online courses come in a range of levels and also allow you to learn in a variety of ways. If you enjoy learning in a theoretical way, you might choose a structured online course delivered by a college or university, such as DeepLearning. AI’s Deep Learning Specialization. 

For more experiential learning, you might consider a boot camp. Generally, a boot camp is a good option if you are already a skilled professional looking to expand your knowledge or upskill in a specific area. Boot camps offer practical learning, often delivered by an industry professional. Prices vary, so consider your budget and the length of time you have available to study. 

You’ll find several online courses available from industry experts. These can be short courses, certificates, or certifications. They may be synchronous, like live lectures, or asynchronous, which is self-paced, allowing you to choose whether you study in your own time or on a schedule. Certificates come in a range of different levels, but a certification might be more suitable for you if you already have skills in the field that you wish to verify.

Examples of courses, certificates, and certifications delivered by industry experts and professional bodies include:

• IBM Introduction to Deep Learning & Neural Networks with Keras

• Neural Networks and Deep Learning

• Certified Deep Learning Expert Certification (IABAC)

• Mathworks: Deep Learning For Computer Vision Specialization

Read books and listen to podcasts

In a fast-moving field like machine learning, deep learning, and AI, it’s imperative that you keep your skills and knowledge up to date. Therefore, professionals engage in continuous learning to keep their skills current and stay in high demand. Keep up with AI and deep learning news and developments by listening to podcasts and reading relevant books and resources. You’ll find a range of options to choose from depending on your interests and specialisms. 

Join a community

Technology is continually evolving, and joining a community so you can network with others in your field helps you stay current on developments and tools. You can join communities and participate in projects on platforms like Kaggle and GitHub. 

Deep learning skills

To work in deep learning, you’ll need strong technical abilities and knowledge of machine learning and AI as well as transferable workplace skills to work with others and communicate your findings. 

Technical skills:

• Machine learning

• Math

• Data science

• Data structures

• Computer architecture

• Programming and Python

• Deep learning libraries and frameworks 

• Data management

• Generative models

Workplace skills:

• Project management

• Teamwork

• Communication 

• Adaptability

• Problem-solving

Deep learning salary and job outlook

Machine learning and deep learning, as subsets of AI, are growing fields with lots of opportunities, as AI continues to change the landscape of how people live and work. The job outlook is higher than average, with 356,700 job openings per year from 2023 to 2033 for computer and information technology occupations [2]. Moreover, AI and machine learning roles are currently some of the fastest-growing jobs. This indicates that the outlook for roles requiring deep learning and AI skills is more than favorable, driven by advancements in cloud computing, big data, and machine learning algorithms.

AI, machine learning, and deep learning salaries are generally high, and you’ll find a range of job opportunities available depending on your interests and skills. Explore possible deep learning and AI jobs along with salaries for each:

• Deep learning engineer: $136,604

• AI engineer: $103,959

• Computational linguist: $102,260

• Software engineer: $112,032

• Data scientist: $113,999

• NLP engineer: $127,303

• AI research scientist: $121,216

• AI developer: $108,680

• Computer vision engineer: $115,559

*All salary information sourced from Glassdoor, May 2025. 

Who employs deep learning professionals?

Deep learning, as a part of AI, is becoming a part of a range of industries, so you’ll find a number of employers looking for people with these skills. Examples of some well-known companies looking for deep learning professionals include:

• IBM

• NVIDIA

• PwC

• Amazon

• PayPal

• Deloitte

• Meta

• Accenture 

• IKEA

Learn deep learning with Coursera

Deep learning is a subset of machine learning and a part of the technology that’s contributing to the development of AI. The job outlook for this field is excellent, and salaries are high. You’ll find a number of ways to develop your deep learning knowledge, including online courses. For example, if you want to gain an in-depth understanding of AI concepts, you can consider the Deep Learning Specialization or the IBM Machine Learning Professional Certificate on Coursera. These can help you build the practical skills necessary to enter the field of AI.