The flexibility of TensorFlow and breadth of its machine learning applications have been important in enabling a wide range of uses. TensorFlow is frequently used for computer vision applications, including facial recognition in social media, automatic X-ray scanning in healthcare, and autonomous vehicle driving. Similarly, natural language processing (NLP) applications can understand and respond to spoken and written text, making possible the creation of helpful chatbots and other digital agents as well as the automatic reading and summarization of text. Recommendation engines used by music streaming services and online retailers may also be built in TensorFlow.
These are all just a few examples of the power of machine learning applications and the ways that TensorFlow can be leveraged to enable them. If you’re interested in pushing the boundaries of this fast-changing field even further, learning TensorFlow is essential.
Expertise in TensorFlow is an extremely valuable addition to your skillset, and can open the door to many exciting careers. As one of the most popular and useful platforms for machine learning and deep learning applications, TensorFlow skills are in demand from companies throughout the tech world, as well as in the automotive industry, medicine, robotics, and other fields. This high level of demand for skills in TensorFlow and machine learning translates into high levels of pay; according to Glassdoor, machine learning engineers in America earn an average salary of $114,121.
Absolutely - in fact, Coursera is one of the best places to learn TensorFlow skills online. You can take individual courses as well as Specializations spanning multiple courses from deeplearning.ai, one of the pioneers in the field, or Google Cloud, an industry leader. You can also take courses from top-ranked universities from around the world, including Imperial College London and National Research University Higher School of Economics. Guided Projects from Coursera offer another way to learn, with hands-on Tensorflow tutorials presented by experienced instructors.
You need to have a basic understanding of Python before starting to learn TensorFlow, so it's best to start with an introductory course to this programming language first. Python is the language used to design TensorFlow. It's also helpful to have knowledge of artificial intelligence (AI) concepts as well. You should have strong math skills, especially in algebra so that you'll be familiar with the calculations and algorithms required in TensorFlow. Foundational knowledge of vectors, scalars, and matrices is also very helpful as you start learning TensorFlow, as well as basic statistics. And it's important to know the basics of machine learning as well.
People who are best suited for roles in TensorFlow have an interest in machine learning or deep learning. Important soft skills include communication skills, problem-solving skills, time management, teamwork, and a thirst for learning. Someone who uses TensorFlow in their job likely works with a team of professionals like software engineers, research scientists, marketing teams, data scientists, and product teams, so they must be able to communicate clearly, prioritize tasks, and work toward a common goal. And since fields that use TensorFlow—such as AI, machine learning, and deep learning—are constantly evolving, people who adapt well to change and are eager to learn or develop the next new technology are well suited for these roles.
If you are currently in the machine learning field or aspire to be, learning about TensorFlow is most likely right for you. The same applies if you want to enter the deep learning field in positions like deep learning scientist, deep learning software engineer, or deep learning researcher since TensorFlow is a good starting point for deep learning. If you're in a deep learning internship, learning TensorFlow is right for you as well.
This FAQ content has been made available for informational purposes only. Learners are advised to conduct additional research to ensure that courses and other credentials pursued meet their personal, professional, and financial goals.