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Real-World Python: A Hacker's Guide to Solving Problems with Code
A project-based approach to learning Python programming for beginners. Intriguing projects teach you how to tackle challenging problems with code.You've mastered the basics. Now you're ready to explore some of Python's more powerful tools. Real-World Python will show you how.Through a series of hands-on projects, you'll investigate and solve real-world problems using sophisticated computer vision, machine learning, data analysis, and language processing tools. You'll be introduced to important modules like OpenCV, NumPy, Pandas, NLTK, Bokeh, Beautiful Soup, Requests, HoloViews, Tkinter, turtle, matplotlib, and more. You'll create complete, working programs and think through intriguing projects that show you how shipwrecked sailors with an algorithm designed to prove the existence of GodDetect asteroids and comets moving against a starfieldProgram a sentry gun to shoot your enemies and spare your friendsSelect landing sites for a Mars probe using real NASA mapsSend unbreakable messages based on a book codeSurvive a zombie outbreak using data scienceDiscover exoplanets and alien megastructures orbiting distant starsTest the hypothesis that we're all living in a computer simulationAnd more!If you're tired of learning the bare essentials of Python Programming with isolated snippets of code, you'll relish the relevant and geeky fun of Real-World Python!
351 pages, Kindle Edition
Published November 10, 2020
72 people are currently reading
524 people want to read
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Displaying 1 - 5 of 5 reviews
August 23, 2021
So I won this book in a goodreads giveaway. This is such a fun idea. I am so new in Python (little side hobby) that I found this book a bit too hard for me. I barely understand the basics but I will get there. This book is full of fun ideas that progress your learning without the boringness of a textbook. The author is mega creative, turning teaching into a game. You can learn Baye's rule through saving shipwrecked sailor. They teach about the turtle module through the space race. Everything is just sooooo fun, which is what I want when learning something new. The book doesn't hold your hand- to quote the author 'sink or swim'. However, the book's website has all the solutions for the practice problems and you can download all the code for the book on there too. The author uses Python v3.7.2 in Windows 10 but he states you can use another operating system without issue. He explains everything need to install Python and using it efficiently.
Also, this isn't an amateur author- Lee Vaughan has wrote 'Impractical Python Projects' (I totally need to get my hands on that book). He is all around smart too, he is an executive-level scientist at ExxonMobil.
I am not going to lie when I say that I am so happy that I won this book. I recommend this for hobbyist like myself.
Only info to keep in mind before buying this is what level of learning you are currently at. I would say I am a second semester freshman and this book is written for a sophomore level.
Also, this isn't an amateur author- Lee Vaughan has wrote 'Impractical Python Projects' (I totally need to get my hands on that book). He is all around smart too, he is an executive-level scientist at ExxonMobil.
I am not going to lie when I say that I am so happy that I won this book. I recommend this for hobbyist like myself.
Only info to keep in mind before buying this is what level of learning you are currently at. I would say I am a second semester freshman and this book is written for a sophomore level.
February 10, 2021
Review of “Real World Python”
By Ian Mizer
I received this book from No Starch Press, but the thoughts in this review are my own.
This is the most amazing book anyone could pick up if they are unsure about what machine learning job they should focus on. There are a lot of great jumping off points in this book, but it does focus primarily on machine learning and data topics. The author knows this and has made the amazing choice to add clear “Further reading” sections that will help you get a professional understanding of the topic. The code is solid, each chapter has it's own job associated with it, and all the libraries mentioned can get you ready for something more technical. If that's what you're after, then come and join me in Real World Python.
Readability : How easy would it be for someone to sit down with this book and finish it quickly.
Accuracy : How accurate is the information being given? Could you write every single line of code in this book and have the books answers match the code you wrote. Normally this is a -1 out of 10 per major mistake and per 4 minor mistakes the book makes
Subject : Does this book do what it wants to do and is what it wants to do good?
Bonus points: for extra stuff that I liked. This will be the most subjective part of the review and might not go into much detail. It's just little things that I highly enjoyed and wanted to give a shoutout about
Readability 10/10:
This book jumps through a lot of hurdles to make sure that you can quickly sink your teeth into and digest very complicated topics very quickly. There is a lot of pieces cut out of each topic that improve someones ability to read this speedily. I was able to get through 40-80 pages a day to complete this book in roughly 6 days. The main benefit of this book in readability is the ability to jump around freely to the topic that interests you the most. Each chapter is mostly cut off from the chapters before so you only have to take in the actual content that you think you'll enjoy. This makes the book feel so much shorter than 300+ pages.
Accuracy 10/10:
I was unable to find any errors in this book. I did have issues on windows and linux with some of this books code, but this was personal environment problems rather than code issues. Some of those issues are almost unavoidable when working with these libraries in multiple OS's
Subject 10/10:
I know at least 1 person in machine learning or data that does each of these chapters as an actual job. So I can honestly say that, while this title is a little misleading, that all of the subjects in this book can get you hired if you know them well enough. That being said I would like to take this time to talk about the title. I know that the title says Real world python projects, but it would be better suited in saying it was real world ML projects in python as almost all of the chapters focus on machine learning or data topics. At this point though I'm really just nitpicking so I won't take any points off for it, but instead inform you guys that the book focuses on machine learning topics in python and doesn't touch flask, django, or any libraries outside of the sphere of ML or Data.
Bonus Points:
+1 to the author putting in further reading sections:
I've already gone over this in Lee Vaughn's last book “Impractical Python Projects”, but I gotta give him kudos every time a book tries to push the reader beyond it's bounds. Making this book a jumping off point for more knowledge is simply what every book should be doing. Because of this I will throw a kudos at any author who does it for every book the do it in.
Unfortunately I can't find any twitter pages or links to web pages that the author might own or be a part of, but I know that you should keep an eye out for him if you're looking for fun, quick reads, where each chapter can be read freely and quickly.
By Ian Mizer
I received this book from No Starch Press, but the thoughts in this review are my own.
This is the most amazing book anyone could pick up if they are unsure about what machine learning job they should focus on. There are a lot of great jumping off points in this book, but it does focus primarily on machine learning and data topics. The author knows this and has made the amazing choice to add clear “Further reading” sections that will help you get a professional understanding of the topic. The code is solid, each chapter has it's own job associated with it, and all the libraries mentioned can get you ready for something more technical. If that's what you're after, then come and join me in Real World Python.
Readability : How easy would it be for someone to sit down with this book and finish it quickly.
Accuracy : How accurate is the information being given? Could you write every single line of code in this book and have the books answers match the code you wrote. Normally this is a -1 out of 10 per major mistake and per 4 minor mistakes the book makes
Subject : Does this book do what it wants to do and is what it wants to do good?
Bonus points: for extra stuff that I liked. This will be the most subjective part of the review and might not go into much detail. It's just little things that I highly enjoyed and wanted to give a shoutout about
Readability 10/10:
This book jumps through a lot of hurdles to make sure that you can quickly sink your teeth into and digest very complicated topics very quickly. There is a lot of pieces cut out of each topic that improve someones ability to read this speedily. I was able to get through 40-80 pages a day to complete this book in roughly 6 days. The main benefit of this book in readability is the ability to jump around freely to the topic that interests you the most. Each chapter is mostly cut off from the chapters before so you only have to take in the actual content that you think you'll enjoy. This makes the book feel so much shorter than 300+ pages.
Accuracy 10/10:
I was unable to find any errors in this book. I did have issues on windows and linux with some of this books code, but this was personal environment problems rather than code issues. Some of those issues are almost unavoidable when working with these libraries in multiple OS's
Subject 10/10:
I know at least 1 person in machine learning or data that does each of these chapters as an actual job. So I can honestly say that, while this title is a little misleading, that all of the subjects in this book can get you hired if you know them well enough. That being said I would like to take this time to talk about the title. I know that the title says Real world python projects, but it would be better suited in saying it was real world ML projects in python as almost all of the chapters focus on machine learning or data topics. At this point though I'm really just nitpicking so I won't take any points off for it, but instead inform you guys that the book focuses on machine learning topics in python and doesn't touch flask, django, or any libraries outside of the sphere of ML or Data.
Bonus Points:
+1 to the author putting in further reading sections:
I've already gone over this in Lee Vaughn's last book “Impractical Python Projects”, but I gotta give him kudos every time a book tries to push the reader beyond it's bounds. Making this book a jumping off point for more knowledge is simply what every book should be doing. Because of this I will throw a kudos at any author who does it for every book the do it in.
Unfortunately I can't find any twitter pages or links to web pages that the author might own or be a part of, but I know that you should keep an eye out for him if you're looking for fun, quick reads, where each chapter can be read freely and quickly.
May 19, 2022
And Hack I Did
This was a wonderful book.
Over the many No Starch Press books I have read over the last few months, I have quickly become a Python expert.
I absolutely love this language, and now with PyScript, I can use it in browsers!
Really excellent book, I coded so many of the examples from this book and grew on them for my own fun. A great book to hack on!
4.8/5
This was a wonderful book.
Over the many No Starch Press books I have read over the last few months, I have quickly become a Python expert.
I absolutely love this language, and now with PyScript, I can use it in browsers!
Really excellent book, I coded so many of the examples from this book and grew on them for my own fun. A great book to hack on!
4.8/5
December 22, 2020
I received this book as a goodreads giveaway. This is not a "learn to code python" book. If you have a basic familiarity with the python language, this book helps walk you through some sample projects to apply those skills. The book does a good job of outlining the theory of the project and the tools needed to accomplish the objective. It then provides challenge project objectives using the theories you just applied.
March 5, 2024
This book is similar to Lee Vaughan's other text- Impractical Python Projects. I preferred this book, because I was more interested in the selected topics (Bayes' Rule, NLP, and computer vision vs. cryptography). I have similar comments to my review of Impractical Python Projects. Vaughan is incredibly intelligent and I enjoyed a peek at how practicing scientists' approach problems. However, I did not feel this book helped me very much with my own personal development. Both books also have a heavy focus on astrophysics, so unless that topic particularly interests you I may advise looking elsewhere.
Displaying 1 - 5 of 5 reviews