{"product_id":"139316","title":"Machine learning using Python libraries ","description":"\u003ccenter\u003e\u003cdiv style=\"text-align:center\"\u003e\u003cimg src=\"https:\/\/tmgdisk01.cafe24.com\/images\/vs\/4172\/sv\/3jXPBu9UuDm03WHNB5oQNsN5SgywMN.png?v=1765074425\" style=\"max-width:100%;max-height:10px\"\u003e\u003c\/div\u003e\u003c\/center\u003e\n\u003ccenter\u003e\u003ctable\u003e\u003ctr\u003e\u003ctd style=\"height:10px\"\u003e\u003c\/td\u003e\u003c\/tr\u003e\u003c\/table\u003e\u003c\/center\u003e\n\u003ccenter\u003e\u003ctable\u003e\u003ctr\u003e\u003ctd style=\"height:10px\"\u003e\u003c\/td\u003e\u003c\/tr\u003e\u003c\/table\u003e\u003c\/center\u003e\n\u003ccenter\u003e\n\n\u003cdiv style=\"width:95%\"\u003e\n\n\u003cdiv style=\"text-align:center;font-size:30px;font-weight:bolder;line-height:1.6em\"\u003e Machine learning using Python libraries \u003c\/div\u003e\n\n\u003ccenter\u003e\u003ctable\u003e\u003ctr\u003e\u003ctd style=\"height:10px\"\u003e\u003c\/td\u003e\u003c\/tr\u003e\u003c\/table\u003e\u003c\/center\u003e\n\n\u003ccenter\u003e\u003ctable\u003e\u003ctr\u003e\u003ctd style=\"height:10px\"\u003e\u003c\/td\u003e\u003c\/tr\u003e\u003c\/table\u003e\u003c\/center\u003e\n\n\u003ccenter\u003e\u003ctable\u003e\u003ctr\u003e\u003ctd style=\"height:10px\"\u003e\u003c\/td\u003e\u003c\/tr\u003e\u003c\/table\u003e\u003c\/center\u003e\n\n\u003ccenter\u003e\u003ctable\u003e\u003ctr\u003e\u003ctd style=\"height:10px\"\u003e\u003c\/td\u003e\u003c\/tr\u003e\u003c\/table\u003e\u003c\/center\u003e\n\n\u003cdiv style=\"border-bottom:1px;border-bottom-style:dotted;border-color:;padding-bottom:20px\"\u003e\u003ccenter\u003e\u003ctable align=\"center\" width=\"100%\"\u003e\u003ctbody style=\"border:0px\"\u003e\n\n\u003ctr\u003e\u003ctd align=\"center\" style=\"line-height:1.2em;text-align:center;font-size:18px;color:black;font-weight:bold;padding-bottom:20px;\"\u003e\u003c\/td\u003e\u003c\/tr\u003e\n\n\u003ctr\u003e\u003ctd style=\"text-align:center\"\u003e\u003cimg src=\"https:\/\/image.yes24.com\/goods\/107680777\/XL\" style=\"max-width:100%;height:auto\"\u003e\u003c\/td\u003e\u003c\/tr\u003e\n\n\n\u003c\/tbody\u003e\u003c\/table\u003e\u003c\/center\u003e\u003c\/div\u003e\n\n\u003ccenter\u003e\u003ctable\u003e\u003ctr\u003e\u003ctd style=\"height:10px\"\u003e\u003c\/td\u003e\u003c\/tr\u003e\u003c\/table\u003e\u003c\/center\u003e\n\n\u003ccenter\u003e\u003ctable\u003e\u003ctr\u003e\u003ctd style=\"height:10px\"\u003e\u003c\/td\u003e\u003c\/tr\u003e\u003c\/table\u003e\u003c\/center\u003e\n\n\u003cdiv style=\"width:95%;{split_style6}padding-top:20px;padding-bottom:20px\"\u003e\n\n\u003cdiv style=\"text-align:left;font-size:16px;font-weight:bold;padding-bottom:20px\"\u003e Description \u003c\/div\u003e\n\n\u003cdiv style=\"text-align:left;word-break:break-all;font-size:14px;line-height:1.6em;\"\u003e\n\n\u003cdiv\u003e\u003ch5\u003e \u003cb\u003eBook Introduction\u003c\/b\u003e\n\u003c\/h5\u003e\u003c\/div\u003e\n\u003cdiv\u003e\n\u003cdiv\u003e\u003cdiv\u003e \u003cb\u003eLearn Machine Learning Theory and Implementation from a Scikit-Learn Core Developer\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003e You don't necessarily need a degree to study machine learning and develop artificial intelligence services in the field. \u003cbr\u003eThis is thanks to excellent machine learning libraries like scikit-learn, which wrap complex and difficult tasks in an intuitive interface.\u003cbr\u003e In this book, a core developer of scikit-learn explains every step of building practical machine learning without complex mathematics.\u003cbr\u003e Even if you haven't studied calculus, linear algebra, or probability theory, you'll be able to utilize machine learning through this book.\u003cbr\u003e\u003cbr\u003e ※ This revised second edition of the translation is based on the fourth edition of the original book, which has been comprehensively updated in accordance with scikit-learn updates.\u003cbr\u003e We corrected typos and printed in full color for visual convenience.\u003cbr\u003e\n\n\u003c\/div\u003e\u003c\/div\u003e\n\u003cdiv\u003e\u003cul\u003e\u003cli\u003e You can preview some of the book's contents.\u003cbr\u003e \u003cspan\u003ePreview\u003c\/span\u003e\n\n\u003c\/li\u003e\u003c\/ul\u003e\u003c\/div\u003e\n\u003c\/div\u003e \",\"\u003cdiv\u003e\u003ch5\u003e \u003cb\u003eindex\u003c\/b\u003e\n\u003c\/h5\u003e\u003c\/div\u003e\n\u003cdiv\u003e\n\u003cdiv\u003e \u003cb\u003eCHAPTER 1 Introduction\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003e 1.1 Why Machine Learning?\u003cbr\u003e __1.1.1 Problems that can be solved with machine learning\u003cbr\u003e __1.1.2 Understanding the Problem and Data\u003cbr\u003e 1.2 Why Python?\u003cbr\u003e 1.3 scikit-learn\u003cbr\u003e __1.3.1 Installing scikit-learn \u003cbr\u003e1.4 Required Libraries and Tools\u003cbr\u003e __1.4.1 Jupyter Notebook\u003cbr\u003e __1.4.2 NumPy\u003cbr\u003e __1.4.3 SciPy\u003cbr\u003e __1.4.4 matplotlib\u003cbr\u003e __1.4.5 pandas\u003cbr\u003e __1.4.6 mglearn\u003cbr\u003e 1.5 Python 2 vs.\u003cbr\u003e Python 3\u003cbr\u003e 1.6 Software versions used in this book\u003cbr\u003e 1.7 First Application: Classification of Iris Varieties\u003cbr\u003e __1.7.1 Loading data\u003cbr\u003e __1.7.2 Performance Measurement: Training and Test Data\u003cbr\u003e __1.7.3 First things first: Take a look at the data\u003cbr\u003e __1.7.4 Our First Machine Learning Model: The k-Nearest Neighbor Algorithm\u003cbr\u003e __1.7.5 Predicting\u003cbr\u003e __1.7.6 Evaluating the Model\u003cbr\u003e 1.8 Summary and Summary\u003cbr\u003e\u003cbr\u003e \u003cb\u003eCHAPTER 2 Supervised Learning\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003e 2.1 Classification and Regression\u003cbr\u003e 2.2 Generalization, overfitting, and underfitting\u003cbr\u003e __2.2.1 Relationship between model complexity and dataset size\u003cbr\u003e 2.3 Supervised Learning Algorithms\u003cbr\u003e __2.3.1 Dataset to be used in the example\u003cbr\u003e __2.3.2 k-nearest neighbors\u003cbr\u003e __2.3.3 Linear Model\u003cbr\u003e __2.3.4 Naive Bayes Classifier\u003cbr\u003e __2.3.5 Decision Tree\u003cbr\u003e __2.3.6 Ensemble of Decision Trees\u003cbr\u003e __2.3.7 (Korean version appendix) Bagging, extra tree, Adaboost \u003cbr\u003e__2.3.8 Kernel Support Vector Machine\u003cbr\u003e __2.3.9 Neural Networks (Deep Learning)\u003cbr\u003e 2.4 Estimating uncertainty in classification predictions\u003cbr\u003e __2.4.1 Decision function\u003cbr\u003e __2.4.2 Predicted Probability\u003cbr\u003e __2.4.3 Uncertainty in Multiclassification\u003cbr\u003e 2.5 Summary and Summary\u003cbr\u003e\u003cbr\u003e \u003cb\u003eCHAPTER 3 Unsupervised Learning and Data Preprocessing\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003e 3.1 Types of unsupervised learning\u003cbr\u003e 3.2 Challenges of Unsupervised Learning\u003cbr\u003e 3.3 Data preprocessing and scaling\u003cbr\u003e __3.3.1 Various preprocessing methods\u003cbr\u003e __3.3.2 Applying data transformation\u003cbr\u003e __3.3.3 (Korean version appendix) QuantileTransformer and PowerTransformer\u003cbr\u003e __3.3.4 Scale training and test data in the same way\u003cbr\u003e __3.3.5 The Effect of Data Preprocessing in Supervised Learning\u003cbr\u003e 3.4 Dimensionality reduction, feature extraction, and manifold learning\u003cbr\u003e __3.4.1 Principal Component Analysis (PCA)\u003cbr\u003e __3.4.2 Nonnegative Matrix Factorization (NMF)\u003cbr\u003e __3.4.3 Manifold Learning with t-SNE\u003cbr\u003e 3.5 Cluster\u003cbr\u003e __3.5.1 k-means clustering\u003cbr\u003e __3.5.2 Merge clusters\u003cbr\u003e __3.5.3 DBSCAN\u003cbr\u003e __3.5.4 Comparison and Evaluation of Clustering Algorithms\u003cbr\u003e __3.5.5 Summary of Clustering Algorithms\u003cbr\u003e 3.6 Summary and Summary\u003cbr\u003e \u003cbr\u003e\u003cb\u003eCHAPTER 4 Data Representation and Feature Engineering\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003e 4.1 Categorical variables\u003cbr\u003e __4.1.1 One-Hot Encoding (Variable)\u003cbr\u003e __4.1.2 Categorical features expressed as numbers\u003cbr\u003e 4.2 OneHotEncoder and ColumnTransformer: Handling Categorical Variables with scikit-learn\u003cbr\u003e 4.3 Easily create a ColumnTransformer with make_column_transformer\u003cbr\u003e 4.4 Interval segmentation, discretization, and linear and tree models\u003cbr\u003e 4.5 Interactions and Polynomials\u003cbr\u003e 4.6 Univariate Nonlinear Transformation\u003cbr\u003e 4.7 Auto-selection of characteristics\u003cbr\u003e __4.7.1 Univariate Statistics\u003cbr\u003e __4.7.2 Model-Based Feature Selection\u003cbr\u003e __4.7.3 Iterative feature selection\u003cbr\u003e 4.8 Leveraging Expert Knowledge\u003cbr\u003e 4.9 Summary and Summary\u003cbr\u003e\u003cbr\u003e \u003cb\u003eCHAPTER 5 MODEL EVALUATION AND PERFORMANCE IMPROVEMENT\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003e 5.1 Cross-validation\u003cbr\u003e __5.1.1 Cross-validation in scikit-learn\u003cbr\u003e __5.1.2 Advantages of Cross-Validation\u003cbr\u003e __5.1.3 Hierarchical k-fold cross-validation and other strategies\u003cbr\u003e __5.1.4 (Korean version appendix) Repeated cross-validation\u003cbr\u003e 5.2 Grid Search\u003cbr\u003e __5.2.1 Simple Grid Search\u003cbr\u003e __5.2.2 Parameter overfitting and validation set\u003cbr\u003e __5.2.3 Grid search using cross-validation\u003cbr\u003e 5.3 Evaluation Indicators and Measurements \u003cbr\u003e__5.3.1 Remember the ultimate goal\u003cbr\u003e __5.3.2 Evaluation metrics for binary classification\u003cbr\u003e __5.3.3 Evaluation metrics for multi-classification\u003cbr\u003e __5.3.4 Regression Evaluation Indicators\u003cbr\u003e __5.3.5 Using Evaluation Metrics in Model Selection\u003cbr\u003e 5.4 Summary and Summary\u003cbr\u003e\u003cbr\u003e \u003cb\u003eCHAPTER 6 Algorithm Chains and Pipelines\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003e 6.1 Data preprocessing and parameter selection\u003cbr\u003e 6.2 Building a Pipeline\u003cbr\u003e 6.3 Applying Pipelines to Grid Search\u003cbr\u003e 6.4 Pipeline Interface\u003cbr\u003e __6.4.1 Creating a pipeline using make_pipleline\u003cbr\u003e __6.4.2 Accessing step properties\u003cbr\u003e __6.4.3 Accessing Pipeline Properties in Grid Search\u003cbr\u003e 6.5 Grid search for preprocessing and model parameters\u003cbr\u003e 6.6 Grid Search for Model Selection\u003cbr\u003e __6.6.1 Avoiding Double Counting\u003cbr\u003e 6.7 Summary and Summary\u003cbr\u003e\u003cbr\u003e \u003cb\u003eCHAPTER 7 Handling Text Data\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003e 7.1 String Data Type\u003cbr\u003e 7.2 Example Application: Movie Review Sentiment Analysis\u003cbr\u003e 7.3 Representing text data as BOW\u003cbr\u003e __7.3.1 Applying BOW to sample data \u003cbr\u003e__7.3.2 BOW on movie reviews\u003cbr\u003e 7.4 Stop words\u003cbr\u003e 7.5 Rescaling data with tf-idf\u003cbr\u003e 7.6 Model Coefficient Investigation\u003cbr\u003e 7.7 BOW (n-gram) made of multiple words\u003cbr\u003e 7.8 Advanced Tokenization, Stemming, and Heading Extraction\u003cbr\u003e __7.8.1 (Korean version appendix) Movie review analysis using KoNLPy\u003cbr\u003e 7.9 Topic Modeling and Document Clustering\u003cbr\u003e __7.9.1 LDA\u003cbr\u003e 7.10 Summary and Summary\u003cbr\u003e\u003cbr\u003e \u003cb\u003eCHAPTER 8 CONCLUSION\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003e 8.1 Approaches to Machine Learning Problems\u003cbr\u003e __8.1.1 Participation in decision-making\u003cbr\u003e 8.2 From Prototype to Product\u003cbr\u003e 8.3 Product System Testing\u003cbr\u003e 8.4 Creating Your Own Estimator\u003cbr\u003e 8.5 More to Learn\u003cbr\u003e __8.5.1 Theory\u003cbr\u003e __8.5.2 Other Machine Learning Frameworks and Packages\u003cbr\u003e __8.5.3 Ranking, Recommendation Systems, and Other Algorithms\u003cbr\u003e __8.5.4 Probabilistic Modeling, Inference, and Probabilistic Programming\u003cbr\u003e __8.5.5 Neural Networks\u003cbr\u003e __8.5.6 Scaling to large datasets\u003cbr\u003e __8.5.7 Building Skills\u003cbr\u003e 8.6 In conclusion\u003c\/div\u003e\n\u003cdiv\u003e\u003c\/div\u003e\n\u003c\/div\u003e \",\"\u003cdiv\u003e\u003ch5\u003e \u003cb\u003eDetailed image\u003c\/b\u003e \u003c\/h5\u003e\u003c\/div\u003e\n\u003cdiv\u003e\u003cdiv\u003e\u003cimg src=\"https:\/\/image.yes24.com\/momo\/TopCate3773\/MidCate004\/377233238.jpg\" border=\"0\" alt=\"Detailed Image 1\"\u003e\u003c\/div\u003e\u003c\/div\u003e \",\"\u003cdiv\u003e\u003ch5\u003e \u003cb\u003ePublisher's Review\u003c\/b\u003e\n\u003c\/h5\u003e\u003c\/div\u003e\n\u003cdiv\u003e  \u003cdiv\u003e\n\u003cb\u003eA comprehensive introduction to machine learning for machine learning practitioners seeking solutions to real-world problems.\u003cbr\u003e A revised translation of the second edition, reflecting scikit-learn 1.x and available for practice on Google Colab.\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003e This book doesn't cover how to build machine learning algorithms from scratch, but instead focuses on using the vast number of models already implemented in scikit-learn and other libraries.\u003cbr\u003e This introductory book requires no prior knowledge of machine learning or artificial intelligence. It walks you through all the steps to successfully build machine learning applications, focusing on Python and scikit-learn.\u003cbr\u003e The methods presented here will be helpful not only to data professionals building commercial applications, but also to researchers and scientists.\u003cbr\u003e If you are familiar with Python and the NumPy and matplotlib libraries, you will understand most of this book.\u003cbr\u003e\u003cbr\u003e \u003cb\u003e★ Features of the 2nd revised translation edition\u003c\/b\u003e\u003cbr\u003e \u003cbr\u003eThis revised translation is based on the fourth edition of the original book, and all typos discovered since the first edition have been corrected.\u003cbr\u003e We've also updated the content overall to follow the scikit-learn 1.x releases.\u003cbr\u003e Furthermore, we have made overall revisions to enable hands-on practice in Google Colab.\u003cbr\u003e\u003cbr\u003e \u003cb\u003e★ Main contents\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003e ● Basic concepts and applications of machine learning\u003cbr\u003e ● Advantages and disadvantages of widely used machine learning algorithms\u003cbr\u003e ● How to express data processed through machine learning\u003cbr\u003e ● Advanced methods for model evaluation and parameter tuning\u003cbr\u003e ● Pipeline for chain model and workflow encapsulation\u003cbr\u003e ● Technology for handling text data\u003cbr\u003e Advice for improving your machine learning and data science skills\u003c\/div\u003e\n\u003cdiv\u003e\u003c\/div\u003e\n\u003c\/div\u003e \"]\u003c\/div\u003e\n\n\n\u003c\/div\u003e\n\n\u003ccenter\u003e\u003ctable\u003e\u003ctr\u003e\u003ctd style=\"height:10px\"\u003e\u003c\/td\u003e\u003c\/tr\u003e\u003c\/table\u003e\u003c\/center\u003e\n\n\u003ccenter\u003e\u003ctable\u003e\u003ctr\u003e\u003ctd style=\"height:10px\"\u003e\u003c\/td\u003e\u003c\/tr\u003e\u003c\/table\u003e\u003c\/center\u003e\n\n\u003cdiv style=\"width:95%;padding-top:20px;padding-bottom:20px\"\u003e\n\n\u003cdiv style=\"text-align:left;font-size:16px;font-weight:bold;padding-bottom:20px\"\u003e GOODS SPECIFICS \u003c\/div\u003e\n\n\u003cdiv style=\"text-align:left;font-size:14px;line-height:1.6em;\"\u003e\n\n\u003cdiv style=\"width:100%;margin-bottom:5px;line-height:1.6em;font-size:14px\"\u003e - \u003cstrong\u003ePublication date:\u003c\/strong\u003e February 25, 2022\u003c\/div\u003e\n\n\u003cdiv style=\"width:100%;margin-bottom:5px;line-height:1.6em;font-size:14px\"\u003e - \u003cstrong\u003ePage count, weight, size:\u003c\/strong\u003e 504 pages | 183*235*35mm\u003c\/div\u003e\n\n\u003cdiv style=\"width:100%;margin-bottom:5px;line-height:1.6em;font-size:14px\"\u003e - \u003cstrong\u003eISBN13:\u003c\/strong\u003e 9791162245279\u003c\/div\u003e\n\n\u003cdiv style=\"width:100%;margin-bottom:5px;line-height:1.6em;font-size:14px\"\u003e - \u003cstrong\u003eISBN10:\u003c\/strong\u003e 1162245271 \u003c\/div\u003e\n\n\n\u003c\/div\u003e\n\n\n\u003c\/div\u003e\n\n\n\u003c\/div\u003e\n\n\u003ccenter\u003e\n\n\u003ccenter\u003e\u003ctable\u003e\u003ctr\u003e\u003ctd style=\"height:10px\"\u003e\u003c\/td\u003e\u003c\/tr\u003e\u003c\/table\u003e\u003c\/center\u003e\n\n\u003ccenter\u003e\u003ctable\u003e\u003ctr\u003e\u003ctd style=\"height:10px\"\u003e\u003c\/td\u003e\u003c\/tr\u003e\u003c\/table\u003e\u003c\/center\u003e\n\n\u003cspan\u003e\u003c\/span\u003e\n\n\u003c\/center\u003e\n\n\n\u003c\/center\u003e","brand":"LIBRAIRIE COREENNE","offers":[{"title":"Default Title","offer_id":43893352333354,"sku":"139316","price":42.0,"currency_code":"EUR","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0683\/2750\/5962\/files\/dc02e7e2ecc999a83394dea219bf10a6.jpg?v=1765397866","url":"https:\/\/librairie.coreenne.fr\/en\/products\/139316","provider":"LIBRAIRIE COREENNE","version":"1.0","type":"link"}