Book Image

Python Machine Learning By Example - Third Edition

By : Yuxi (Hayden) Liu
Book Image

Python Machine Learning By Example - Third Edition

By: Yuxi (Hayden) Liu

Overview of this book

Python Machine Learning By Example, Third Edition serves as a comprehensive gateway into the world of machine learning (ML). With six new chapters, on topics including movie recommendation engine development with Naïve Bayes, recognizing faces with support vector machine, predicting stock prices with artificial neural networks, categorizing images of clothing with convolutional neural networks, predicting with sequences using recurring neural networks, and leveraging reinforcement learning for making decisions, the book has been considerably updated for the latest enterprise requirements. At the same time, this book provides actionable insights on the key fundamentals of ML with Python programming. Hayden applies his expertise to demonstrate implementations of algorithms in Python, both from scratch and with libraries. Each chapter walks through an industry-adopted application. With the help of realistic examples, you will gain an understanding of the mechanics of ML techniques in areas such as exploratory data analysis, feature engineering, classification, regression, clustering, and NLP. By the end of this ML Python book, you will have gained a broad picture of the ML ecosystem and will be well-versed in the best practices of applying ML techniques to solve problems.
Table of Contents (17 chapters)
15
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16
Index

Knowing the prerequisites

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, and 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 subjects. Don't panic: we will get machine learning to work for us without going into any mathematical details in this book. It just requires some basic 101 knowledge of probability theory and linear algebra, which helps us to understand the mechanics of machine learning techniques and algorithms. And it gets easier as we will be building models both from scratch and with popular packages in Python, a language we like and are familiar with.

For those who want to learn or brush up on probability theory and linear algebra, feel free to search for basic probability theory and basic linear algebra. There are a lot of resources available online, for example, https://people.ucsc.edu/~abrsvn/intro_prob_1.pdf regarding probability 101, and http://www.maths.gla.ac.uk/~ajb/dvi-ps/2w-notes.pdf regarding basic linear algebra.

Those who want to study machine learning systematically can enroll in computer science, AI, and, more recently, data science master's programs. There are also various data science boot camps. However, the selection for boot camps is usually stricter as they're more job-oriented and the program duration is often short, ranging from four to 10 weeks. Another option is the free Massive Open Online Courses (MOOCs), Andrew Ng's popular course on machine learning. Last but not least, industry blogs and websites are great resources for us to keep up with the latest developments.

Machine learning isn't only a skill but also a bit of sport. We can compete in several machine learning competitions, such as Kaggle (www.kaggle.com)—sometimes for decent cash prizes, sometimes for joy, and most of the time to play to our strengths. However, to win these competitions, we may need to utilize certain techniques, which are only useful in the context of competitions and not in the context of trying to solve a business problem. That's right, the no free lunch theorem (https://en.wikipedia.org/wiki/No_free_lunch_theorem) applies here.

Next, we'll take a look at the three types of machine learning.