Data science and machine learning engineering are two tech fields that can offer great salaries and entail fascinating work. Maybe you want to expand your career skill set to work with one of many interesting topics like machine learning for big data.
What do data scientists and machine learning engineers do? Read on to learn more.
There’s still a common misconception that machine learning requires extensive resources and is reserved for only the most mathematically oriented data scientists. However, this myth is slowly dying as more people gain awareness of cloud infrastructure services like AWS (Amazon Web Services), which provide the necessary tools to make machine learning more accessible to every business and individual.
Are you struggling to acquire computational power for your prediction generator? Making limited progress with machine learning because you can’t get your hands on that powerful GPU card? Read on to find out whether AWS Machine Learning could have a solution for you.
Chances are you’ve encountered deep learning in your everyday life. Be it driverless cars that seemingly use actual vision, browser applications that translate your texts into near-perfect French, or silly yet impressive mobile apps that age you by decades in a matter of seconds — neural networks and deep learning are ubiquitous. In this article, we’ll cover all the important aspects to get you started on deep learning neural networks.
Artificial Intelligence (AI) has big implications for healthcare. This has been brought to light by the current global COVID-19 pandemic that has overloaded hospitals, stretched resources, and infected millions of people before tests and treatment could be made available.
AI-driven technology has been in development for the healthcare community for many years. For example, AI can be used to enhance 2D and 3D imaging to better detect abnormalities and improve diagnosis. However, there are still some challenges with how AI can be applied to healthcare.
First introduced back in the 1950s, AI has evolved tremendously over the past 65 years. While initially it was used in more technical settings, like specialized computer labs, adoption has grown to the point where AI is integrated in our everyday lives.
With AI becoming part of how we work and live, it’s important to understand how it will be used in our day-to-day lives and how you can make an impact in the industry.
The field of machine learning continues to boast incredible job growth, salaries, and skill sets that can be used in many different industries. Google utilizes this technology in their Cloud product to allow startups to build machine learning models that work on data of any size, while GE utilizes IoT to help detect and prevent anomalies and crashes in their products. These are just a snapshot of the numerous applications of machine learning in the market today that display the potential for an exponential amount of professional expansion. Currently, just in the US alone, there are over 50,000 open roles for machine learning professionals, so now is the time to develop machine learning expertise!
In LinkedIn’s 2020 Emerging Jobs report, AI Specialist, a role that includes machine learning, deep learning, TensorFlow, and Python as key skills, boasts 74% annual growth. All of the above skills are incorporated into Udacity’s new Intro to Machine Learning with TensorFlow Nanodegree program, which is a great way to get introduced to the fundamentals of machine learning, including areas like manipulating data, supervised & unsupervised learning, and deep learning.
So what is TensorFlow, and how is it being utilized today? TensorFlow is a deep learning framework made by Google for creating machine learning (ML) models that use multi-layer neural networks. The TensorFlow library allows users to perform functions by creating computational graphs. AirBnB utilizes TensorFlow to improve the guest experience to categorize listing photos by classifying images and detecting objects at scale. Coca-Cola uses TensorFlow to enable mobile proof-of-purchase at scale, while PayPal uses TensorFlow to detect fraud, and Twitter uses TensorFlow to rank tweets, highlighting the broad and powerful range of applications.