Hands-on machine learning with scikit-learn and scientific Python toolkits :: a practical guide to implementing supervised and unsupervised machine learning algorithms in Python /
This book covers the theory and practice of building data-driven solutions. Includes the end-to-end process, using supervised and unsupervised algorithms. With each algorithm, you will learn the data acquisition and data engineering methods, the apt metrics, and the available hyper-parameters. You w...
Gespeichert in:
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| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
Birmingham, UK :
Packt Publishing, Limited,
2020.
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| Schlagworte: | |
| Online-Zugang: | Volltext |
| Zusammenfassung: | This book covers the theory and practice of building data-driven solutions. Includes the end-to-end process, using supervised and unsupervised algorithms. With each algorithm, you will learn the data acquisition and data engineering methods, the apt metrics, and the available hyper-parameters. You will learn how to deploy the models in production. |
| Beschreibung: | 1 online resource (1 volume) : illustrations |
| ISBN: | 9781838823580 1838823581 |
Internformat
MARC
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| 264 | 1 | |a Birmingham, UK : |b Packt Publishing, Limited, |c 2020. | |
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| 505 | 0 | |a Cover -- Title Page -- Copyright and Credits -- About Packt -- Contributors -- Table of Contents -- Preface -- Section 1: Supervised Learning -- Chapter 1: Introduction to Machine Learning -- Understanding machine learning -- Types of machine learning algorithms -- Supervised learning -- Classification versus regression -- Supervised learning evaluation -- Unsupervised learning -- Reinforcement learning -- The model development life cycle -- Understanding a problem -- Splitting our data -- Finding the best manner to split the data -- Making sure the training and the test datasets are separate | |
| 505 | 8 | |a Development set -- Evaluating our model -- Deploying in production and monitoring -- Iterating -- When to use machine learning -- Introduction to scikit-learn -- It plays well with the Python data ecosystem -- Practical level of abstraction -- When not to use scikit-learn -- Installing the packages you need -- Introduction to pandas -- Python's scientific computing ecosystem conventions -- Summary -- Further reading -- Chapter 2: Making Decisions with Trees -- Understanding decision trees -- What are decision trees? -- Iris classification -- Loading the Iris dataset -- Splitting the data | |
| 505 | 8 | |a Training the model and using it for prediction -- Evaluating our predictions -- Which features were more important? -- Displaying the internal tree decisions -- How do decision trees learn? -- Splitting criteria -- Preventing overfitting -- Predictions -- Getting a more reliable score -- What to do now to get a more reliable score -- ShuffleSplit -- Tuning the hyperparameters for higher accuracy -- Splitting the data -- Trying different hyperparameter values -- Comparing the accuracy scores -- Visualizing the tree's decision boundaries -- Feature engineering -- Building decision tree regressors | |
| 505 | 8 | |a Predicting people's heights -- Regressor's evaluation -- Setting sample weights -- Summary -- Chapter 3: Making Decisions with Linear Equations -- Understanding linear models -- Linear equations -- Linear regression -- Estimating the amount paid to the taxi driver -- Predicting house prices in Boston -- Data exploration -- Splitting the data -- Calculating a baseline -- Training the linear regressor -- Evaluating our model's accuracy -- Showing feature coefficients -- Scaling for more meaningful coefficients -- Adding polynomial features -- Fitting the linear regressor with the derived features | |
| 505 | 8 | |a Regularizing the regressor -- Training the lasso regressor -- Finding the optimum regularization parameter -- Finding regression intervals -- Getting to know additional linear regressors -- Using logistic regression for classification -- Understanding the logistic function -- Plugging the logistic function into a linear model -- Objective function -- Regularization -- Solvers -- Configuring the logistic regression classifier -- Classifying the Iris dataset using logistic regression -- Understanding the classifier's decision boundaries -- Getting to know additional linear classifiers -- Summary | |
| 520 | |a This book covers the theory and practice of building data-driven solutions. Includes the end-to-end process, using supervised and unsupervised algorithms. With each algorithm, you will learn the data acquisition and data engineering methods, the apt metrics, and the available hyper-parameters. You will learn how to deploy the models in production. | ||
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| 776 | 0 | 8 | |i Print version: |a Amr, Tarek. |t Hands-On Machine Learning with Scikit-learn and Scientific Python Toolkits : A Practical Guide to Implementing Supervised and Unsupervised Machine Learning Algorithms in Python. |d Birmingham : Packt Publishing, Limited, ©2020 |
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| contents | Cover -- Title Page -- Copyright and Credits -- About Packt -- Contributors -- Table of Contents -- Preface -- Section 1: Supervised Learning -- Chapter 1: Introduction to Machine Learning -- Understanding machine learning -- Types of machine learning algorithms -- Supervised learning -- Classification versus regression -- Supervised learning evaluation -- Unsupervised learning -- Reinforcement learning -- The model development life cycle -- Understanding a problem -- Splitting our data -- Finding the best manner to split the data -- Making sure the training and the test datasets are separate Development set -- Evaluating our model -- Deploying in production and monitoring -- Iterating -- When to use machine learning -- Introduction to scikit-learn -- It plays well with the Python data ecosystem -- Practical level of abstraction -- When not to use scikit-learn -- Installing the packages you need -- Introduction to pandas -- Python's scientific computing ecosystem conventions -- Summary -- Further reading -- Chapter 2: Making Decisions with Trees -- Understanding decision trees -- What are decision trees? -- Iris classification -- Loading the Iris dataset -- Splitting the data Training the model and using it for prediction -- Evaluating our predictions -- Which features were more important? -- Displaying the internal tree decisions -- How do decision trees learn? -- Splitting criteria -- Preventing overfitting -- Predictions -- Getting a more reliable score -- What to do now to get a more reliable score -- ShuffleSplit -- Tuning the hyperparameters for higher accuracy -- Splitting the data -- Trying different hyperparameter values -- Comparing the accuracy scores -- Visualizing the tree's decision boundaries -- Feature engineering -- Building decision tree regressors Predicting people's heights -- Regressor's evaluation -- Setting sample weights -- Summary -- Chapter 3: Making Decisions with Linear Equations -- Understanding linear models -- Linear equations -- Linear regression -- Estimating the amount paid to the taxi driver -- Predicting house prices in Boston -- Data exploration -- Splitting the data -- Calculating a baseline -- Training the linear regressor -- Evaluating our model's accuracy -- Showing feature coefficients -- Scaling for more meaningful coefficients -- Adding polynomial features -- Fitting the linear regressor with the derived features Regularizing the regressor -- Training the lasso regressor -- Finding the optimum regularization parameter -- Finding regression intervals -- Getting to know additional linear regressors -- Using logistic regression for classification -- Understanding the logistic function -- Plugging the logistic function into a linear model -- Objective function -- Regularization -- Solvers -- Configuring the logistic regression classifier -- Classifying the Iris dataset using logistic regression -- Understanding the classifier's decision boundaries -- Getting to know additional linear classifiers -- Summary |
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| spelling | Amr, Tarek, author. http://id.loc.gov/authorities/names/no2020105423 Hands-on machine learning with scikit-learn and scientific Python toolkits : a practical guide to implementing supervised and unsupervised machine learning algorithms in Python / Tarek Amr. Birmingham, UK : Packt Publishing, Limited, 2020. ©2020 1 online resource (1 volume) : illustrations text txt rdacontent computer c rdamedia online resource cr rdacarrier Online resource; title from digital title page (viewed on November 23, 2020). Cover -- Title Page -- Copyright and Credits -- About Packt -- Contributors -- Table of Contents -- Preface -- Section 1: Supervised Learning -- Chapter 1: Introduction to Machine Learning -- Understanding machine learning -- Types of machine learning algorithms -- Supervised learning -- Classification versus regression -- Supervised learning evaluation -- Unsupervised learning -- Reinforcement learning -- The model development life cycle -- Understanding a problem -- Splitting our data -- Finding the best manner to split the data -- Making sure the training and the test datasets are separate Development set -- Evaluating our model -- Deploying in production and monitoring -- Iterating -- When to use machine learning -- Introduction to scikit-learn -- It plays well with the Python data ecosystem -- Practical level of abstraction -- When not to use scikit-learn -- Installing the packages you need -- Introduction to pandas -- Python's scientific computing ecosystem conventions -- Summary -- Further reading -- Chapter 2: Making Decisions with Trees -- Understanding decision trees -- What are decision trees? -- Iris classification -- Loading the Iris dataset -- Splitting the data Training the model and using it for prediction -- Evaluating our predictions -- Which features were more important? -- Displaying the internal tree decisions -- How do decision trees learn? -- Splitting criteria -- Preventing overfitting -- Predictions -- Getting a more reliable score -- What to do now to get a more reliable score -- ShuffleSplit -- Tuning the hyperparameters for higher accuracy -- Splitting the data -- Trying different hyperparameter values -- Comparing the accuracy scores -- Visualizing the tree's decision boundaries -- Feature engineering -- Building decision tree regressors Predicting people's heights -- Regressor's evaluation -- Setting sample weights -- Summary -- Chapter 3: Making Decisions with Linear Equations -- Understanding linear models -- Linear equations -- Linear regression -- Estimating the amount paid to the taxi driver -- Predicting house prices in Boston -- Data exploration -- Splitting the data -- Calculating a baseline -- Training the linear regressor -- Evaluating our model's accuracy -- Showing feature coefficients -- Scaling for more meaningful coefficients -- Adding polynomial features -- Fitting the linear regressor with the derived features Regularizing the regressor -- Training the lasso regressor -- Finding the optimum regularization parameter -- Finding regression intervals -- Getting to know additional linear regressors -- Using logistic regression for classification -- Understanding the logistic function -- Plugging the logistic function into a linear model -- Objective function -- Regularization -- Solvers -- Configuring the logistic regression classifier -- Classifying the Iris dataset using logistic regression -- Understanding the classifier's decision boundaries -- Getting to know additional linear classifiers -- Summary This book covers the theory and practice of building data-driven solutions. Includes the end-to-end process, using supervised and unsupervised algorithms. With each algorithm, you will learn the data acquisition and data engineering methods, the apt metrics, and the available hyper-parameters. You will learn how to deploy the models in production. Machine learning. http://id.loc.gov/authorities/subjects/sh85079324 Python (Computer program language) http://id.loc.gov/authorities/subjects/sh96008834 Apprentissage automatique. Python (Langage de programmation) Machine learning fast Python (Computer program language) fast Python (programmeertaal). nbdbt has work: Hands-on machine learning with scikit-learn and scientific Python toolkits (Text) https://id.oclc.org/worldcat/entity/E39PCGDfRm7PqrtMfKhfTmr44q https://id.oclc.org/worldcat/ontology/hasWork Print version: Amr, Tarek. Hands-On Machine Learning with Scikit-learn and Scientific Python Toolkits : A Practical Guide to Implementing Supervised and Unsupervised Machine Learning Algorithms in Python. Birmingham : Packt Publishing, Limited, ©2020 FWS01 ZDB-4-EBA FWS_PDA_EBA https://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&AN=2562942 Volltext |
| spellingShingle | Amr, Tarek Hands-on machine learning with scikit-learn and scientific Python toolkits : a practical guide to implementing supervised and unsupervised machine learning algorithms in Python / Cover -- Title Page -- Copyright and Credits -- About Packt -- Contributors -- Table of Contents -- Preface -- Section 1: Supervised Learning -- Chapter 1: Introduction to Machine Learning -- Understanding machine learning -- Types of machine learning algorithms -- Supervised learning -- Classification versus regression -- Supervised learning evaluation -- Unsupervised learning -- Reinforcement learning -- The model development life cycle -- Understanding a problem -- Splitting our data -- Finding the best manner to split the data -- Making sure the training and the test datasets are separate Development set -- Evaluating our model -- Deploying in production and monitoring -- Iterating -- When to use machine learning -- Introduction to scikit-learn -- It plays well with the Python data ecosystem -- Practical level of abstraction -- When not to use scikit-learn -- Installing the packages you need -- Introduction to pandas -- Python's scientific computing ecosystem conventions -- Summary -- Further reading -- Chapter 2: Making Decisions with Trees -- Understanding decision trees -- What are decision trees? -- Iris classification -- Loading the Iris dataset -- Splitting the data Training the model and using it for prediction -- Evaluating our predictions -- Which features were more important? -- Displaying the internal tree decisions -- How do decision trees learn? -- Splitting criteria -- Preventing overfitting -- Predictions -- Getting a more reliable score -- What to do now to get a more reliable score -- ShuffleSplit -- Tuning the hyperparameters for higher accuracy -- Splitting the data -- Trying different hyperparameter values -- Comparing the accuracy scores -- Visualizing the tree's decision boundaries -- Feature engineering -- Building decision tree regressors Predicting people's heights -- Regressor's evaluation -- Setting sample weights -- Summary -- Chapter 3: Making Decisions with Linear Equations -- Understanding linear models -- Linear equations -- Linear regression -- Estimating the amount paid to the taxi driver -- Predicting house prices in Boston -- Data exploration -- Splitting the data -- Calculating a baseline -- Training the linear regressor -- Evaluating our model's accuracy -- Showing feature coefficients -- Scaling for more meaningful coefficients -- Adding polynomial features -- Fitting the linear regressor with the derived features Regularizing the regressor -- Training the lasso regressor -- Finding the optimum regularization parameter -- Finding regression intervals -- Getting to know additional linear regressors -- Using logistic regression for classification -- Understanding the logistic function -- Plugging the logistic function into a linear model -- Objective function -- Regularization -- Solvers -- Configuring the logistic regression classifier -- Classifying the Iris dataset using logistic regression -- Understanding the classifier's decision boundaries -- Getting to know additional linear classifiers -- Summary Machine learning. http://id.loc.gov/authorities/subjects/sh85079324 Python (Computer program language) http://id.loc.gov/authorities/subjects/sh96008834 Apprentissage automatique. Python (Langage de programmation) Machine learning fast Python (Computer program language) fast Python (programmeertaal). nbdbt |
| subject_GND | http://id.loc.gov/authorities/subjects/sh85079324 http://id.loc.gov/authorities/subjects/sh96008834 |
| title | Hands-on machine learning with scikit-learn and scientific Python toolkits : a practical guide to implementing supervised and unsupervised machine learning algorithms in Python / |
| title_auth | Hands-on machine learning with scikit-learn and scientific Python toolkits : a practical guide to implementing supervised and unsupervised machine learning algorithms in Python / |
| title_exact_search | Hands-on machine learning with scikit-learn and scientific Python toolkits : a practical guide to implementing supervised and unsupervised machine learning algorithms in Python / |
| title_full | Hands-on machine learning with scikit-learn and scientific Python toolkits : a practical guide to implementing supervised and unsupervised machine learning algorithms in Python / Tarek Amr. |
| title_fullStr | Hands-on machine learning with scikit-learn and scientific Python toolkits : a practical guide to implementing supervised and unsupervised machine learning algorithms in Python / Tarek Amr. |
| title_full_unstemmed | Hands-on machine learning with scikit-learn and scientific Python toolkits : a practical guide to implementing supervised and unsupervised machine learning algorithms in Python / Tarek Amr. |
| title_short | Hands-on machine learning with scikit-learn and scientific Python toolkits : |
| title_sort | hands on machine learning with scikit learn and scientific python toolkits a practical guide to implementing supervised and unsupervised machine learning algorithms in python |
| title_sub | a practical guide to implementing supervised and unsupervised machine learning algorithms in Python / |
| topic | Machine learning. http://id.loc.gov/authorities/subjects/sh85079324 Python (Computer program language) http://id.loc.gov/authorities/subjects/sh96008834 Apprentissage automatique. Python (Langage de programmation) Machine learning fast Python (Computer program language) fast Python (programmeertaal). nbdbt |
| topic_facet | Machine learning. Python (Computer program language) Apprentissage automatique. Python (Langage de programmation) Machine learning Python (programmeertaal). |
| url | https://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&AN=2562942 |
| work_keys_str_mv | AT amrtarek handsonmachinelearningwithscikitlearnandscientificpythontoolkitsapracticalguidetoimplementingsupervisedandunsupervisedmachinelearningalgorithmsinpython |