It's been said that data is the new "dirt"—the raw material from which and on which you build the structures of the modern world. And like dirt, data can seem like a limitless, undifferentiated mass. The ability to take raw data, access it, filter it, process it, visualize it, understand it, and communicate it to others is possibly the most essential business problem for the coming decades.
"Machine learning," the process of automating tasks once considered the domain of highly-trained analysts and mathematicians, is the key to efficiently extracting useful information from this sea of raw data. By implementing the core algorithms of statistical data processing, data analysis, and data visualization as reusable computer code, you can scale your capacity for data analysis well beyond the capabilities of individual knowledge workers.
Machine Learning in Action is a unique book that blends the foundational theories of machine learning with the practical realities of building tools for everyday data analysis. In it, you'll use the flexible Python programming language to build programs that implement algorithms for data classification, forecasting, recommendations, and higher-level features like summarization and simplification.
As you work through the numerous examples, you'll explore key topics like classification, numeric prediction, and clustering. Along the way, you'll be introduced to important established algorithms, such as Apriori, through which you identify association patterns in large datasets and Adaboost, a meta-algorithm that can increase the efficiency of many machine learning tasks.
Peter Harrington holds Bachelors and Masters Degrees in Electrical Engineering. He worked for Intel Corporation for seven years in California and China. Peter holds five US patents and his work has been published in three academic journals. He is currently the chief scientist for Zillabyte Inc. Peter spends his free time competing in programming competitions, and building 3D pr...
- 以Python代码实现经典机器学习算法
- 涵盖分类、回归及无监督学习核心内容
- 强调从底层构建代码理解算法原理
- 具备Python基础的编程初学者
- 希望快速上手机器学习的实践者
- 偏好通过代码实例理解理论的读者
- 注意核对Python版本兼容性
- 需补充数学理论以深入理解
- 结合源码调试以掌握细节
- 优秀的机器学习入门实战指南
- 代码实现有助于直观理解概念
- 数学理论较浅需配合其他教材
本导读基于书籍简介、目录、原文摘录、短评和书评生成，不等同于全文精读。
- "Pros: High accuracy, insensitive to outliers, no assumptions about data Cons: Computationally expensive, requires a lot of memory Works with: Numeric values, nominal values The first machine-learning algorithm we’ll look at is k-Nearest Neighbors (kNN). It works like this: we have an existing set of"
- "Pros: Computationally cheap to use, easy for humans to understand learned results, missing values OK, can deal with irrelevant features Cons: Prone to overfitting Works with: Numeric values, nominal values"
- "General approach to decision trees 1. Collect: Any method. 2. Prepare: This tree-building algorithm works only on nominal values, so any continuous values will need to be quantized. 3. Analyze: Any method. You should visually inspect the tree after it is built. 4. Train: Construct a tree data struct"
- "Logistic regression Pros: Computationally inexpensive, easy to implement, knowledge representation easy to interpret Cons: Prone to underfitting, may have low accuracy Works with: Numeric values, nominal values"
- "The clear syntax of Python has earned it the name executable pseudo-code."
- "With Python, you can program in any style you’re familiar with: object-oriented, procedural, functional, and so on."
- "With Python it’s easy to process and manipulate text"
- "Python is popular in the scientific and financial communities as well.A number of scientific libraries such as SciPy and NumPy allow you to do vector and matrix operations."
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Deep Learning with Python (2/e)
Functional Design and Architecture
Grokking Machine Learning
Unit Testing
Deep Learning with JavaScript
Istio in Action