Getting Started with Machine Learning and Python
It is believed that in the next 30 years, artificial intelligence (AI) will outpace human knowledge. Regardless of whether it will lead to job losses, analytical and machine learning skills are becoming increasingly important. In fact, this point has been emphasized by the most influential business leaders, including the Microsoft co-founder, Bill Gates, Tesla CEO, Elon Musk, and former Google executive chairman, Eric Schmidt.
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 of working with data and working with models.
At the end of the chapter, we will also set up the software for Python, the most popular language for machine learning and data science, and its libraries and tools that are required for this book.
We will go into detail on the following topics:
- The importance of machine learning
- The core of machine learning—generalizing with data
- Overfitting and underfitting
- The bias-variance trade-off
- Techniques to avoid overfitting
- Techniques for data preprocessing
- Techniques for feature engineering
- Techniques for model aggregation
- Setting up a Python environment
- Installing the main Python packages
- Introducing TensorFlow 2
