---
product_id: 51719904
title: "Introduction to Machine Learning with Python: A Guide for Data Scientists"
brand: "andreas c. müller, sarah guido"
price: "B$164"
currency: BND
in_stock: true
reviews_count: 13
url: https://brunei.desertcart.com/products/51719904-introduction-to-machine-learning-with-python-a-guide-for-data
store_origin: BN
region: Brunei
---

# Introduction to Machine Learning with Python: A Guide for Data Scientists

**Brand:** andreas c. müller, sarah guido
**Price:** B$164
**Availability:** ✅ In Stock

## Quick Answers

- **What is this?** Introduction to Machine Learning with Python: A Guide for Data Scientists by andreas c. müller, sarah guido
- **How much does it cost?** B$164 with free shipping
- **Is it available?** Yes, in stock and ready to ship
- **Where can I buy it?** [brunei.desertcart.com](https://brunei.desertcart.com/products/51719904-introduction-to-machine-learning-with-python-a-guide-for-data)

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- andreas c. müller, sarah guido enthusiasts

## Why This Product

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## Description

Machine learning has become an integral part of many commercial applications and research projects, but this field is not exclusive to large companies with extensive research teams. If you use Python, even as a beginner, this book will teach you practical ways to build your own machine learning solutions. With all the data available today, machine learning applications are limited only by your imagination.
You’ll learn the steps necessary to create a successful machine-learning application with Python and the scikit-learn library. Authors Andreas Müller and Sarah Guido focus on the practical aspects of using machine learning algorithms, rather than the math behind them. Familiarity with the NumPy and matplotlib libraries will help you get even more from this book.
With this book, you’ll learn:
Fundamental concepts and applications of machine learning
Advantages and shortcomings of widely used machine learning algorithms
How to represent data processed by machine learning, including which data aspects to focus on
Advanced methods for model evaluation and parameter tuning
The concept of pipelines for chaining models and encapsulating your workflow
Methods for working with text data, including text-specific processing techniques
Suggestions for improving your machine learning and data science skills

## Technical Specifications

| Specification | Value |
|---------------|-------|
| Dimensions | 17.53 x 1.78 x 23.11 cm |
| Edition | 1st |
| Isbn 10 | 1449369413 |
| Isbn 13 | 978-1449369415 |
| Item Weight | 680 g |
| Language | English |
| Part Of Series | Learning Python |
| Print Length | 400 pages |
| Publication Date | 7 Oct. 2016 |
| Publisher | O′Reilly |

## Images

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## Customer Reviews

### ⭐⭐⭐⭐⭐ A great first book on machine learning!
*by  on Reviewed in the United Kingdom 14 April 2025*

A great book for getting started. Covers loads of models you can build with scikitlearn and walks you through good practice in machine learning. If you know python but have little to no experience with ML, this is a great place to start! Decision trees, random forests, gradient boosting, support vector machines, etc!

### ⭐⭐⭐⭐⭐ Good guide to starting ML
*by  on Reviewed in the United Kingdom 1 March 2020*

Decent guide to starting machine learning. The difficulty level is geared towards those with a little knowledge of computer science & programming, but might be too gentle for more experienced coders. I'm only part-way in, so will try to remember to update once I've completed the book.

### ⭐⭐⭐⭐ IMO, a thorough introduction to ML. I liked the code examples and also the real world chapter.
*by  on Reviewed in the United Kingdom 15 June 2022*

Gave a thorough introduction to ML in a relatively digestible form. It filled in holes in my knowledge. I was a little surprised that what I thought were "just" statistical methods were classified as ML too(e.g. PCA, random forest and k means clustering). All good though, so many of us know more than we think. :-) I managed to read over a week of hour each way train commuting and some evenings.Like many such books, the code is invaluable and all the examples I tried and played with worked, using python3 on linux on a Chromebook.Most was at the right level for me, some I will need to revisit after a month. I emerged with a far more informed mind and increased confidence.I particularly liked the chapter on how to apply one's new found knowledge in the overall context of projects and the notes of caution for the real world.As some correctly reviewed, yes one can find similar information on line for free, to me I find it easier to have a thorough set of information in one place, that I could travel with, could write in and get away from a screen.(I am an experienced bioinfomatician with MSc Advanced Computing that already knew the broader concepts, and had the used the results of ML before, but not the detail or the how in python).

## Frequently Bought Together

- Introduction to Machine Learning with Python: A Guide for Data Scientists
- Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems
- Python for Data Analysis: Data Wrangling with pandas, NumPy, and Jupyter

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*Store origin: BN*
*Last updated: 2026-08-19*