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新書推薦： 《 人生幸好有快乐（一代大师梁实秋至诚分享八十余年的快乐哲学！） 》 售價：NT$ 302.0 《 中世纪文明（400-1500年）（格致人文） 》 售價：NT$ 661.0 《 女子运动术（维持肌肉是更高级的抗衰！变瘦的同时有效增肌，成为更好的自己！） 》 售價：NT$ 325.0 《 你有多敏感，就有多珍贵 》 售價：NT$ 330.0 《 独立战争与世界重启：一部新的十八世纪晚期全球史（方尖碑书系） 》 售價：NT$ 661.0 《 中国绘画史（第三卷） 》 售價：NT$ 1669.0 《 德兰修女传：在爱中行走（世界名人名传典藏系列） 》 售價：NT$ 280.0 《 抗癌饮食指导 》 售價：NT$ 442.0

建議一齊購買： + NT$ 443 《 Python数据分析 》 + NT$ 443 《 Effective Python：编写高质量Python代码的59个有效方法 》 + NT$ 593 《 机器学习导论（原书第3版） 》 + NT$ 457 《 Rcpp：R与C++的无缝整合（R语言应用系列） 》 + NT$ 510 《 Spark机器学习（影印版） 》 內容簡介： 运用机器学习获得对于数据的深入洞见，是现代应用开发者和分析师的关键技能。Python是一种可以用于开发机器学习应用的极佳语言。作为一种动态语言，它可以进行快速探索和实验。利用其**的开源机器学习库，你可以在快速尝试很多想法的同时专注于手头的任务。 科埃略、里克特所*的《Python语言构建机器学习系统第2版影印版英文版》展示了如何在原始数据中寻找模式的具体方法，从复习Python机器学习知识和介绍程序库开始，你将很快进入应对正式而真实的数据集项目环节，运用建模技术，创建推荐系统。然后，该书介绍了主题建模、篮子分析和云计算等高级主题。这些内容将拓展你的能力，让你能够创建大型复杂系统。 有了这本书，你就能获得构建自有系统所需的工具和知识，定制化解决实际的数据分析相关问题。 目錄： Preface Chapter 1: Getting Started with Python Machine Learning Machine learning and Python - a dream team What the book will teach you and what it will not What to do when you are stuck Getting started Introduction to NumPy, SciPy, and matplotlib Installing Python Chewing data efficiently with NumPy and intelligentlywith SciPy Learning NumPy Indexing Handling nonexisting values Comparing the runtime Learning SciPy Our first tiny application of machine learning Reading in the data Preprocessing and cleaning the data Choosing the right model and learning algorithm Beforebuilding our first model... Starting with a simple straight line Towards some advanced stuff Stepping back to go forward - another look at our data Training and testing Answering our initial question Summary Chapter 2: Classifying with Real-world Examples The Iris dataset Visualization is a good first step Building our first classification model Evaluation - holding out data and cross-validation Building more complex classifiers A more complex dataset and a more complex classifim Learning about the Seeds dataset Features and feature engineering Nearest neighbor classification Classifying with scikit-learn Looking at the decision boundaries Binary and multiclass classification Summary Chapter 3: Clustering - Finding Related Posts Measuring the relatedness of posts How not to do it How to do it Preprocessing - similarity measured as a similar number of common words Converting raw text into a bag of words Counting words Normalizing word count vectors Removing less important words Stemming Stop words on steroids Our achievements and goals Clustering K-means Getting test data to evaluate our ideas on Clustering posts Solving our initial challenge Another look at noise Tweaking the parameters Summary Chapter 4: Topic Modeling Latent Dirichlet allocation Building a topic model Comparing documents by topics Modeling the whole of Wikipedia Choosing the number of topics Summary Chapter 5: Classification - Detecting Poor Answers Sketching our roadmap Learning to classify classy answers Tuning the instance Tuning the classifier Fetching the data Slimming the data down to chewable chunks Preselection and processing of attributes Defining what is a good answer Creating our first classifier Starting with kNN Engineering the features Training the classifier Measuring the classifier''s performance Designing more features Deciding how to improve Bias-variance and their tradeoff Fixing high bias Fixing high variance High bias or low bias Using logistic regression A bit of math with a small example Applying logistic regression to our post classification problem Looking behind accuracy- precision and recall Slimming the classifier Ship it! Summary Chapter 6: Classification II - Sentiment Analysis Sketching our roadmap Fetching the Twitter data Introducing the Naive Bayes classifier Getting to know the Bayes'' theorem Being naive Using Naive Bayes to classify Accounting for unseen words and other oddities Accounting for arithmetic underflows Creating our first classifier and tuning it Solving an easy problem first Using all classes Tuning the classifier''s parameters Cleaning tweets Taking the word types into account Determining the word types Successfully cheating using SentiWordNet Our first estimator Putting everything together Summary Chapter 7: Regression Predicting house prices with regression Multidimensional regression Cross-validation for regression Penalized or regularized regression L1 and L2 penalties Using Lasso or ElasticNet in scikit-learn Visualizing the Lasso path P-greater-than-N scenarios An example based on text documents Setting hyperparameters in a principled way Summary Chapter 8: Recommendations Rating predictions and recommendations Splitting into training and testing Normalizing the training data A neighborhood approach to recommendations A regression approach to recommendations Combining multiple methods Basket analysis Obtaining useful predictions Analyzing supermarket shopping baskets Association rule mining More advanced basket analysis Summary Chapter 9: Classification - Music Genre Classification Sketching our roadmap Fetching the music data Converting into a WAV format Looking at music Decomposing music into sine wave components Using FFT to build our first classifier Increasing experimentation agility Training the classifier Using a confusion matrix to measure accuracy in multiclass problems An alternative way to measure classifier performance using receiver-operator characteristics Improving classification performance with Mel Frequency Cepstral Coefficients Summary Chapter 10: Computer Vision Introducing image processing Loading and displaying images Thresholding Gaussian blurring Putting the center in focus Basic image classification Computing features from images Writing your own features Using features to find similar images Classifying a harder dataset Local feature representations Summary Chapter 11: Dimensionality Reduction Sketching our roadmap Selecting features Detecting redundant features using filters Correlation Mutual information Asking the model about the features using wrappers Other feature selection methods Feature extraction About principal component analysis Sketching PCA Applying PCA Limitations of PCAand how LDA can help Multidimensional scaling Summary Chapter 12: Bigger Data Learning about big data Using jug to break up your pipeline into tasks An introduction to tasks in jug Looking under the hood Using jug for data analysis Reusing partial results Using Amazon Web Services Creating your first virtual machines Installing Python packages on Amazon Linux Running jug on our cloud machine Automating the generation of clusters with StarCluster Summary Appendix: Where to Learn More Machine Learning Online courses Books Question and answer sites Blogs Data sources Getting competitive All that was left out Summary Index

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