Machine learning has been at the forefront of recent years due to impressive advances in computer science, statistics, the development of neural networks, and the improved quality and quantity ofdatasets. Here we take a deep dive into machine learning examples to give you a better perspective. In...
One machine learning application is process automation, which can free up time and resources, allowing your team to focus on what matters most.Machine learning techniques Supervised learning Addressing datasets with labels or structure, data acts as a teacher and “trains” the machine, increasing...
After reading this chapter you will be able to apply the machine learning techniques explained in the previous chapters to your own machine learning problems, even if they are different from the examples presented here.doi:10.1007/978-3-030-60032-7_8Z. Somogyi...
The choice of algorithm depends on the nature of the data. Many algorithms and techniques aren't limited to a single type of ML; they can be adapted to multiple types depending on the problem and data set. For instance, deep learning algorithms such asconvolutional and recurrent neural network...
Some high-level examples include: Improved hiring and worker performance :A global fast food company uses machine learning to gain insight on talent acquisition, retention and employee performance. Such “people analytics” provides deep insights about HR data using predictive modeling techniques on mult...
While there are many techniques available to achieve these goals, using a machine learning approach has become the preferred method in recent years. Optical character recognition can be taken a step further through sentiment analysis. This is a real-time application of machine learning that utilizes...
Machine learning and cybersecurity use cases There are four ways ML is being used in cybersecurity: ML and facial recognition are used in authentication methods to protect an enterprise’s security. Antivirus programs may use AI and ML techniques to detect and block malware. ...
At its core, machine learning is the process of using algorithms to analyze data. It allows computers to “learn” from that data without being explicitly programmed or told what to do by a human operator. Instead, ML uses statistical techniques to make sense of large datasets, identify patter...
Examples of deep learning applications include speech recognition, image classification, and pharmaceutical analysis. How does machine learning work? Machine learning is comprised of different types of machine learning models, using various algorithmic techniques. Depending upon the nature of the data and ...
Reinforcement learningis often used for robotics, gaming and navigation. It's also used in conjunction with generative AI techniques, like large language models. With reinforcement learning, the algorithm discovers through trial and error which actions yield the greatest rewards. This type of learning...