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Machine Learning is an exciting and rapidly growing field which is all about creating algorithms and models that enable computers to recognize patterns, make predictions, and improve their performance over time. It empowers computers to learn from data and experiences and make decisions without being explicitly programmed. For beginners, understanding the fundamentals of Machine Learning can open doors to a world of possibilities. Machine Learning is as effective as the datasets it relies on. By understanding the nuances of different types of datasets, conducting thorough exploration, and following best practices, practitioners can harness the power of data to build robust and accurate Machine Learning models. As the field continues to advance, the role of datasets in shaping the future of Machine Learning becomes increasingly pivotal.
Machine Learning (ML) has revolutionized various industries, and with the advent of oneAPI, the landscape has evolved even further. oneAPI simplifies the development process by providing a unified programming model that spans different architectures, including CPUs, GPUs, FPGAs, and accelerators. It aims to break down the barriers between traditional and specialized computing, fostering a more efficient and portable programming environment. By embracing a unified programming model, oneAPI also empowers practitioners to build efficient and scalable Machine Learning solutions, marking a significant stride in the evolution of cross-​architecture development.
Embarking on the journey of Machine Learning as a beginner may seem daunting, but with a step-​by-​step approach followed in this book, it becomes an accessible and rewarding endeavor. You can delve into this fascinating field, to master Machine Learning concepts.
The term Machine Learning (ML) belonging to the research field of Artificial Intelligence (AI) has been buzzing around us for quite a few decades. Most of us would have already carried out some attempts to understand “what the word level abstract meaning is” at least by performing a Google search. Even if you have not done that, do not worry you are at the right place to learn in depth about “What is Machine Learning?” in a technical perspective. The intention of this chapter does not stop there; yes, we will dive a little deeper to understand the various types of Machine Learning, its application in real-​life scenarios, and the differences between ML and Deep Learning (DL).
Deep Learning (DL) is an advanced subset of AI, where a human brain is imitated in processing and understanding the data. Neural networks tend to be the basis of Deep Learning, which mainly acts like a black box to grab data and yield wonderful insights. Interpreting the results from the deep neural networks require good analytical skills so that the output obtained makes sense for some other useful applications