Machine Learning Solutions :: Expert techniques to tackle complex machine learning problems using Python.
This book demonstrates a set of simple to complex problems you may encounter while building machine learning models. You'll not only learn the best possible solutions to these problems but also find out how to build projects based on each problem mentioned in the book, with a practical approach...
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
Birmingham :
Packt Publishing,
2018.
|
| Schlagworte: | |
| Online-Zugang: | Volltext |
| Zusammenfassung: | This book demonstrates a set of simple to complex problems you may encounter while building machine learning models. You'll not only learn the best possible solutions to these problems but also find out how to build projects based on each problem mentioned in the book, with a practical approach and easy-to-follow examples. |
| Beschreibung: | Implementing logistic regression. |
| Beschreibung: | 1 online resource (567 pages) |
| Bibliographie: | Includes bibliographical references and index. |
| ISBN: | 9781788398893 1788398890 |
Internformat
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| 100 | 1 | |a Thanaki, Jalaj. | |
| 245 | 1 | 0 | |a Machine Learning Solutions : |b Expert techniques to tackle complex machine learning problems using Python. |
| 260 | |a Birmingham : |b Packt Publishing, |c 2018. | ||
| 300 | |a 1 online resource (567 pages) | ||
| 336 | |a text |b txt |2 rdacontent | ||
| 337 | |a computer |b c |2 rdamedia | ||
| 338 | |a online resource |b cr |2 rdacarrier | ||
| 588 | 0 | |a Print version record. | |
| 505 | 0 | |a Cover; Copyright; Foreword; Contributors; Table of Contents; Preface; Chapter 1: Credit Risk Modeling; Introducing the problem statement; Understanding the dataset; Understanding attributes of the dataset; Data analysis; Data preprocessing; Basic data analysis followed by data preprocessing; Number of dependents; Feature engineering for the baseline model; Finding out Feature importance; Selecting machine learning algorithms; K-Nearest Neighbor (KNN); Logistic regression; AdaBoost; GradientBoosting; RandomForest; Training the baseline model; Understanding the testing matrix. | |
| 505 | 8 | |a The Mean accuracy of the trained modelsThe ROC-AUC score; ROC; AUC; Testing the baseline model; Problems with the existing approach; Optimizing the existing approach; Understanding key concepts to optimize the approach; Cross-validation; Hyperparameter tuning; Implementing the revised approach; Implementing a cross-validation based approach; Implementing hyperparameter tuning; Implementing and testing the revised approach; Understanding problems with the revised approach; Best approach; Implementing the best approach; Log transformation of features; Voting-based ensemble ML model. | |
| 505 | 8 | |a Running ML models on real test dataSummary; Chapter 2: Stock Market Price Prediction; Introducing the problem statement; Collecting the dataset; Collecting DJIA index prices; Collecting news articles; Understanding the dataset; Understanding the DJIA dataset; Understanding the NYTimes news article dataset; Data preprocessing and data analysis; Preparing the DJIA training dataset; Basic data analysis for a DJIA dataset; Preparing the NYTimes news dataset; Converting publication date into the YYYY-MM-DD format; Filtering news articles by category. | |
| 505 | 8 | |a Implementing the filter functionality and merging the datasetSaving the merged dataset in the pickle file format; Feature engineering; Loading the dataset; Minor preprocessing; Converting adj close price into the integer format; Removing the leftmost dot from news headlines; Feature engineering; Sentiment analysis of NYTimes news articles; Selecting the Machine Learning algorithm; Training the baseline model; Splitting the training and testing dataset; Splitting prediction labels for the training and testing datasets; Converting sentiment scores into the numpy array; Training of the ML model. | |
| 505 | 8 | |a Understanding the testing matrixThe default testing matrix; The visualization approach; Testing the baseline model; Generating and interpreting the output; Generating the accuracy score; Visualizing the output; Exploring problems with the existing approach; Alignment; Smoothing; Trying a different ML algorithm; Understanding the revised approach; Understanding concepts and approaches; Alignment-based approach; Smoothing-based approach; Logistic Regression-based approach; Implementing the revised approach; Implementation; Implementing alignment; Implementing smoothing. | |
| 500 | |a Implementing logistic regression. | ||
| 520 | |a This book demonstrates a set of simple to complex problems you may encounter while building machine learning models. You'll not only learn the best possible solutions to these problems but also find out how to build projects based on each problem mentioned in the book, with a practical approach and easy-to-follow examples. | ||
| 504 | |a Includes bibliographical references and index. | ||
| 650 | 0 | |a Python. | |
| 650 | 0 | |a Machine learning. |0 http://id.loc.gov/authorities/subjects/sh85079324 | |
| 650 | 6 | |a Apprentissage automatique. | |
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| 776 | 0 | 8 | |i Print version: |a Thanaki, Jalaj. |t Machine Learning Solutions : Expert techniques to tackle complex machine learning problems using Python. |d Birmingham : Packt Publishing, ©2018 |
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| contents | Cover; Copyright; Foreword; Contributors; Table of Contents; Preface; Chapter 1: Credit Risk Modeling; Introducing the problem statement; Understanding the dataset; Understanding attributes of the dataset; Data analysis; Data preprocessing; Basic data analysis followed by data preprocessing; Number of dependents; Feature engineering for the baseline model; Finding out Feature importance; Selecting machine learning algorithms; K-Nearest Neighbor (KNN); Logistic regression; AdaBoost; GradientBoosting; RandomForest; Training the baseline model; Understanding the testing matrix. The Mean accuracy of the trained modelsThe ROC-AUC score; ROC; AUC; Testing the baseline model; Problems with the existing approach; Optimizing the existing approach; Understanding key concepts to optimize the approach; Cross-validation; Hyperparameter tuning; Implementing the revised approach; Implementing a cross-validation based approach; Implementing hyperparameter tuning; Implementing and testing the revised approach; Understanding problems with the revised approach; Best approach; Implementing the best approach; Log transformation of features; Voting-based ensemble ML model. Running ML models on real test dataSummary; Chapter 2: Stock Market Price Prediction; Introducing the problem statement; Collecting the dataset; Collecting DJIA index prices; Collecting news articles; Understanding the dataset; Understanding the DJIA dataset; Understanding the NYTimes news article dataset; Data preprocessing and data analysis; Preparing the DJIA training dataset; Basic data analysis for a DJIA dataset; Preparing the NYTimes news dataset; Converting publication date into the YYYY-MM-DD format; Filtering news articles by category. Implementing the filter functionality and merging the datasetSaving the merged dataset in the pickle file format; Feature engineering; Loading the dataset; Minor preprocessing; Converting adj close price into the integer format; Removing the leftmost dot from news headlines; Feature engineering; Sentiment analysis of NYTimes news articles; Selecting the Machine Learning algorithm; Training the baseline model; Splitting the training and testing dataset; Splitting prediction labels for the training and testing datasets; Converting sentiment scores into the numpy array; Training of the ML model. Understanding the testing matrixThe default testing matrix; The visualization approach; Testing the baseline model; Generating and interpreting the output; Generating the accuracy score; Visualizing the output; Exploring problems with the existing approach; Alignment; Smoothing; Trying a different ML algorithm; Understanding the revised approach; Understanding concepts and approaches; Alignment-based approach; Smoothing-based approach; Logistic Regression-based approach; Implementing the revised approach; Implementation; Implementing alignment; Implementing smoothing. |
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| discipline | Informatik |
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| publisher | Packt Publishing, |
| record_format | marc |
| spelling | Thanaki, Jalaj. Machine Learning Solutions : Expert techniques to tackle complex machine learning problems using Python. Birmingham : Packt Publishing, 2018. 1 online resource (567 pages) text txt rdacontent computer c rdamedia online resource cr rdacarrier Print version record. Cover; Copyright; Foreword; Contributors; Table of Contents; Preface; Chapter 1: Credit Risk Modeling; Introducing the problem statement; Understanding the dataset; Understanding attributes of the dataset; Data analysis; Data preprocessing; Basic data analysis followed by data preprocessing; Number of dependents; Feature engineering for the baseline model; Finding out Feature importance; Selecting machine learning algorithms; K-Nearest Neighbor (KNN); Logistic regression; AdaBoost; GradientBoosting; RandomForest; Training the baseline model; Understanding the testing matrix. The Mean accuracy of the trained modelsThe ROC-AUC score; ROC; AUC; Testing the baseline model; Problems with the existing approach; Optimizing the existing approach; Understanding key concepts to optimize the approach; Cross-validation; Hyperparameter tuning; Implementing the revised approach; Implementing a cross-validation based approach; Implementing hyperparameter tuning; Implementing and testing the revised approach; Understanding problems with the revised approach; Best approach; Implementing the best approach; Log transformation of features; Voting-based ensemble ML model. Running ML models on real test dataSummary; Chapter 2: Stock Market Price Prediction; Introducing the problem statement; Collecting the dataset; Collecting DJIA index prices; Collecting news articles; Understanding the dataset; Understanding the DJIA dataset; Understanding the NYTimes news article dataset; Data preprocessing and data analysis; Preparing the DJIA training dataset; Basic data analysis for a DJIA dataset; Preparing the NYTimes news dataset; Converting publication date into the YYYY-MM-DD format; Filtering news articles by category. Implementing the filter functionality and merging the datasetSaving the merged dataset in the pickle file format; Feature engineering; Loading the dataset; Minor preprocessing; Converting adj close price into the integer format; Removing the leftmost dot from news headlines; Feature engineering; Sentiment analysis of NYTimes news articles; Selecting the Machine Learning algorithm; Training the baseline model; Splitting the training and testing dataset; Splitting prediction labels for the training and testing datasets; Converting sentiment scores into the numpy array; Training of the ML model. Understanding the testing matrixThe default testing matrix; The visualization approach; Testing the baseline model; Generating and interpreting the output; Generating the accuracy score; Visualizing the output; Exploring problems with the existing approach; Alignment; Smoothing; Trying a different ML algorithm; Understanding the revised approach; Understanding concepts and approaches; Alignment-based approach; Smoothing-based approach; Logistic Regression-based approach; Implementing the revised approach; Implementation; Implementing alignment; Implementing smoothing. Implementing logistic regression. This book demonstrates a set of simple to complex problems you may encounter while building machine learning models. You'll not only learn the best possible solutions to these problems but also find out how to build projects based on each problem mentioned in the book, with a practical approach and easy-to-follow examples. Includes bibliographical references and index. Python. Machine learning. http://id.loc.gov/authorities/subjects/sh85079324 Apprentissage automatique. Information technology: general issues. bicssc Neural networks & fuzzy systems. bicssc Artificial intelligence. bicssc COMPUTERS Machine Theory. bisacsh COMPUTERS Programming Languages Python. bisacsh Computers Information Technology. bisacsh Computers Neural Networks. bisacsh Computers Intelligence (AI) & Semantics. bisacsh Machine learning fast Electronic book. has work: Machine learning solutions (Text) https://id.oclc.org/worldcat/entity/E39PCGYRctRqt4pVfrbD86CKQy https://id.oclc.org/worldcat/ontology/hasWork Print version: Thanaki, Jalaj. Machine Learning Solutions : Expert techniques to tackle complex machine learning problems using Python. Birmingham : Packt Publishing, ©2018 FWS01 ZDB-4-EBA FWS_PDA_EBA https://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&AN=1804693 Volltext |
| spellingShingle | Thanaki, Jalaj Machine Learning Solutions : Expert techniques to tackle complex machine learning problems using Python. Cover; Copyright; Foreword; Contributors; Table of Contents; Preface; Chapter 1: Credit Risk Modeling; Introducing the problem statement; Understanding the dataset; Understanding attributes of the dataset; Data analysis; Data preprocessing; Basic data analysis followed by data preprocessing; Number of dependents; Feature engineering for the baseline model; Finding out Feature importance; Selecting machine learning algorithms; K-Nearest Neighbor (KNN); Logistic regression; AdaBoost; GradientBoosting; RandomForest; Training the baseline model; Understanding the testing matrix. The Mean accuracy of the trained modelsThe ROC-AUC score; ROC; AUC; Testing the baseline model; Problems with the existing approach; Optimizing the existing approach; Understanding key concepts to optimize the approach; Cross-validation; Hyperparameter tuning; Implementing the revised approach; Implementing a cross-validation based approach; Implementing hyperparameter tuning; Implementing and testing the revised approach; Understanding problems with the revised approach; Best approach; Implementing the best approach; Log transformation of features; Voting-based ensemble ML model. Running ML models on real test dataSummary; Chapter 2: Stock Market Price Prediction; Introducing the problem statement; Collecting the dataset; Collecting DJIA index prices; Collecting news articles; Understanding the dataset; Understanding the DJIA dataset; Understanding the NYTimes news article dataset; Data preprocessing and data analysis; Preparing the DJIA training dataset; Basic data analysis for a DJIA dataset; Preparing the NYTimes news dataset; Converting publication date into the YYYY-MM-DD format; Filtering news articles by category. Implementing the filter functionality and merging the datasetSaving the merged dataset in the pickle file format; Feature engineering; Loading the dataset; Minor preprocessing; Converting adj close price into the integer format; Removing the leftmost dot from news headlines; Feature engineering; Sentiment analysis of NYTimes news articles; Selecting the Machine Learning algorithm; Training the baseline model; Splitting the training and testing dataset; Splitting prediction labels for the training and testing datasets; Converting sentiment scores into the numpy array; Training of the ML model. Understanding the testing matrixThe default testing matrix; The visualization approach; Testing the baseline model; Generating and interpreting the output; Generating the accuracy score; Visualizing the output; Exploring problems with the existing approach; Alignment; Smoothing; Trying a different ML algorithm; Understanding the revised approach; Understanding concepts and approaches; Alignment-based approach; Smoothing-based approach; Logistic Regression-based approach; Implementing the revised approach; Implementation; Implementing alignment; Implementing smoothing. Python. Machine learning. http://id.loc.gov/authorities/subjects/sh85079324 Apprentissage automatique. Information technology: general issues. bicssc Neural networks & fuzzy systems. bicssc Artificial intelligence. bicssc COMPUTERS Machine Theory. bisacsh COMPUTERS Programming Languages Python. bisacsh Computers Information Technology. bisacsh Computers Neural Networks. bisacsh Computers Intelligence (AI) & Semantics. bisacsh Machine learning fast |
| subject_GND | http://id.loc.gov/authorities/subjects/sh85079324 |
| title | Machine Learning Solutions : Expert techniques to tackle complex machine learning problems using Python. |
| title_auth | Machine Learning Solutions : Expert techniques to tackle complex machine learning problems using Python. |
| title_exact_search | Machine Learning Solutions : Expert techniques to tackle complex machine learning problems using Python. |
| title_full | Machine Learning Solutions : Expert techniques to tackle complex machine learning problems using Python. |
| title_fullStr | Machine Learning Solutions : Expert techniques to tackle complex machine learning problems using Python. |
| title_full_unstemmed | Machine Learning Solutions : Expert techniques to tackle complex machine learning problems using Python. |
| title_short | Machine Learning Solutions : |
| title_sort | machine learning solutions expert techniques to tackle complex machine learning problems using python |
| title_sub | Expert techniques to tackle complex machine learning problems using Python. |
| topic | Python. Machine learning. http://id.loc.gov/authorities/subjects/sh85079324 Apprentissage automatique. Information technology: general issues. bicssc Neural networks & fuzzy systems. bicssc Artificial intelligence. bicssc COMPUTERS Machine Theory. bisacsh COMPUTERS Programming Languages Python. bisacsh Computers Information Technology. bisacsh Computers Neural Networks. bisacsh Computers Intelligence (AI) & Semantics. bisacsh Machine learning fast |
| topic_facet | Python. Machine learning. Apprentissage automatique. Information technology: general issues. Neural networks & fuzzy systems. Artificial intelligence. COMPUTERS Machine Theory. COMPUTERS Programming Languages Python. Computers Information Technology. Computers Neural Networks. Computers Intelligence (AI) & Semantics. Machine learning Electronic book. |
| url | https://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&AN=1804693 |
| work_keys_str_mv | AT thanakijalaj machinelearningsolutionsexperttechniquestotacklecomplexmachinelearningproblemsusingpython |