Summary
In this chapter, we delved into the fundamental concepts of artificial intelligence, which will serve as the foundation for our journey in Part 2 of this book. We explored various types of tasks, including supervised learning, unsupervised learning, and reinforcement learning. Through a hands-on example, we gained insights into the typical machine learning process, which encompasses model selection, training, and evaluation.
Throughout this chapter, we acquired essential knowledge related to common challenges in machine learning, such as striking the right balance between underfitting and overfitting models, the existence of imbalanced datasets, and which metrics are relevant to evaluate models that are trained with them. Understanding these concepts is vital for any successful machine learning project.
Moreover, we progressed into the basics of deep learning, where we explored the key components of a neural network using Keras. Additionally, we implemented a pipeline...
