Machine Learning in Action
Peter HarringtonSummary
Machine Learning in Action is unique book that blends the foundational theories of machine learning with the practical realities of building tools for everyday data analysis. 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.
About the BookA machine is said to learn when its performance improves with experience. Learning requires algorithms and programs that capture data and ferret out the interesting or useful patterns. Once the specialized domain of analysts and mathematicians, machine learning is becoming a skill needed by many.
Machine Learning in Action
is a clearly written tutorial for developers. It avoids academic language and takes you straight to the techniques you'll use in your day-to-day work. Many (Python) examples present the core algorithms of statistical data processing, data analysis, and data visualization in code you can reuse. You'll understand the concepts and how they fit in with tactical tasks like classification, forecasting, recommendations, and higher-level features like summarization and simplification.Readers need no prior experience with machine learning or statistical processing. Familiarity with Python is helpful.
What's Inside- A no-nonsense introduction
- Examples showing common ML tasks
- Everyday data analysis
- Implementing classic algorithms like Apriori and Adaboos
===================================
Table of Contents- PART 1 CLASSIFICATION
- Machine learning basics
- Classifying with k-Nearest Neighbors
- Splitting datasets one feature at a time: decision trees
- Classifying with probability theory: naïve Bayes
- Logistic regression
- Support vector machines
- Improving classification with the AdaBoost meta algorithm PART 2 FORECASTING NUMERIC VALUES WITH REGRESSION
- Predicting numeric values: regression
- Tree-based regression PART 3 UNSUPERVISED LEARNING
- Grouping unlabeled items using k-means clustering
- Association analysis with the Apriori algorithm
- Efficiently finding frequent itemsets with FP-growth PART 4 ADDITIONAL TOOLS
- Using principal component analysis to simplify data
- Simplifying data with the singular value decomposition
- Big data and MapReduce
该文件将通过电报信使发送给您。 您最多可能需要 1-5 分钟才能收到它。
注意:确保您已将您的帐户链接到 Z-Library Telegram 机器人。
该文件将发送到您的 Kindle 帐户。 您最多可能需要 1-5 分钟才能收到它。
请注意:您需要验证要发送到Kindle的每本书。检查您的邮箱中是否有来自亚马逊Kindle的验证电子邮件。