Getting Started with Machine Learning and Python
The concept of artificial intelligence (AI) outpacing human knowledge is often referred to as the “technological singularity.” Some predictions in the AI research community and other fields suggest that the singularity could happen within the next 30 years. Regardless of its time horizon, one thing is clear: the rise of AI highlights the growing importance of analytical and machine learning skills. Mastering these disciplines equips us to not only understand and interact with increasingly complex AI systems but also actively participate in shaping their development and application, ensuring they benefit humanity.
In this chapter, we will kick off our machine learning journey with the basic, yet important, concepts of machine learning. We will start with what machine learning is all about, why we need it, and its evolution over a few decades. We will then discuss typical machine learning tasks and explore several essential techniques to work with data and models.
At the end of the chapter, we will set up the software for Python, the most popular language for machine learning and data science, and the libraries and tools that are required for this book.
We will go into detail on the following topics:
- An introduction to machine learning
- Knowing the prerequisites
- Getting started with three types of machine learning
- Digging into the core of machine learning
- Data preprocessing and feature engineering
- Combining models
- Installing software and setting up
