In the realm of artificial intelligence (AI), machine learning is a domain where machines learn from data and refine their performance over time. This chapter embarks on a journey into the realm of machine learning. The chapter begins by introducing regression—a foundational technique in machine learning, demonstrating how machines map relation between observed data to predict outcomes and trends. Delving deeper, the chapter ventures into the landscape of neural network. It unveils the structure and function of a neuron and takes the threshold logic unit (TLU) as an example to reveal how these basic elements pave the way for complex calculations. The chapter guides learners through the process of TLU learning—the mechanism by which the units adapt to data and evolve their decision-making parameters. Then deep learning emerges as the expansion of these concepts. Finally, hands-on exercises invite learners to employ and design neurons to achieve specific calculations, enabling learners to harness the transformative potential of machine learning from a practical perspective. Whether predicting market trends, analyzing complex datasets, or engineering intelligent systems, this chapter empowers learners to navigate the landscape of machine learning from the scratch.

Authors and Affiliations

1. Institute of Liberal Arts and Science, Kanazawa University, Kanazawa, Ishikawa, Japan

   Wei Weng

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Correspondence toWei Weng.

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