Machine learning is a term coined around 1960, composed of two words—machine corresponds to a computer, robot, or other device, and learning refers to an activity intended to acquire or discover event patterns, which we humans are good at.

So, why do we need machine learning and why do we want a machine to learn as a human? First and foremost, of course, computers and robots can work 24/7 and don't get tired, need breaks, call in sick, or go on strike. Their maintenance is much lower than a human's and costs a lot less in the long run. Also, for sophisticated problems that involve a variety of huge datasets or complex calculations, for instance, it's much more justifiable, not to mention intelligent, to let computers do all of the work. Machines driven by algorithms designed by humans are able to learn latent rules...

Machine learning mimicking human intelligence is a subfield of AI—a field of computer science concerned with creating systems. Software engineering is another field in computer science. Generally, we can label Python programming as a type of software engineering. Machine learning is also closely related to linear algebra, probability theory, statistics, and mathematical optimization. We usually build machine learning models based on statistics, probability theory, and linear algebra, then optimize the models using mathematical optimization. The majority of you reading this book should have a good, or at least sufficient, command of Python programming. Those who aren't feeling confident about mathematical knowledge might be wondering how much time should be spent learning or brushing up on the aforementioned...

The good thing about data is that there's a lot of it in the world. The bad thing is that it's hard to process this data. The challenges stem from the diversity and noisiness of the data. We humans usually process data coming into our ears and eyes. These inputs are transformed into electrical or chemical signals. On a very basic level, computers and robots also work with electrical signals. These electrical signals are then translated into ones and zeroes. However, we program in Python in this book and, on that level, normally we represent the data either as numbers, images, or texts. Actually, images and text aren't very convenient, so we need to transform images and text into numerical values.

Especially in the context of supervised learning, we have a scenario similar to studying for an exam. We have a...

Data mining, a buzzword in the 1990s, is the predecessor of data science (the science of data). One of the methodologies popular in the data mining community is called Cross-Industry Standard Process for Data Mining (CRISP-DM). CRISP-DM was created in 1996 and is still used today. I'm not endorsing CRISP-DM, however, I do like its general framework.

The CRISP DM consists of the following phases, which aren't mutually exclusive and can occur in parallel:

• Business understanding: This phase is often taken care of by specialized domain experts. Usually, we have a business person formulate a business problem, such as selling more units of a certain product.
• Data understanding: This is also a phase that may require input from domain experts, however, often a technical specialist needs to get involved more than in the business...

In high school, we sit together with other students and learn together, but we aren't supposed to work together during the exam. The reason is, of course, that teachers want to know what we've learned, and if we just copy exam answers from friends, we may not have learned anything. Later in life, we discover that teamwork is important. For example, this book is the product of a whole team or possibly a group of teams.

Clearly, a team can produce better results than a single person. However, this goes against Occam's razor, since a single person can come up with simpler theories compared to what a team will produce. In machine learning, we nevertheless prefer to have our models cooperate with the following schemes:

• Voting and averaging
• Bagging
• Boosting
• Stacking

As the title says, Python is the language used to implement all machine learning algorithms and techniques throughout this entire book. We'll also use many popular Python packages and tools such as NumPy, SciPy, TensorFlow, and Scikit-learn. So at the end of this kick-off chapter, let's make sure we set up the tools and working environment properly, even though some of you are already experts in Python or might be familiar with some tools.

We'll be using Python 3 in this book. As you may know, Python 2 will no longer be supported after 2020, so starting with or switching to Python 3 is strongly recommended. Trust me, the transition is pretty...

We just finished our first mile on the Python and machine learning journey! Throughout this chapter, we became familiar with the basics of machine learning. We started with what machine learning is all about, the importance of machine learning (DT era) and its brief history, and looked at recent developments as well. We also learned typical machine learning tasks and explored several essential techniques of working with data and working with models. Now that we're equipped with basic machine learning knowledge and we've set up the software and tools, let's get ready for the real-world machine learning examples ahead.

In particular, we will be exploring newsgroups text data in our first ML project coming up next chapter.

• Can you tell the difference between machine learning and traditional programming (rule-based automation)?
• What's overfitting and how do we avoid it?
• Name two feature engineering approaches.
• Name two ways to combine multiple models.
• Install Matplotlib if you're interested.