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Hands-on machine learning with scikit-learn and scientific Python toolkits : a practical guide to implementing supervised and unsupervised machine learning algorithms in Python /

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개인저자Amr, Tarek, author.
서명/저자사항Hands-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.
발행사항Birmingham, UK : Packt Publishing, Limited, 2020.
형태사항1 online resource (1 volume) : illustrations
소장본 주기OCLC control number change
ISBN9781838823580
1838823581

내용주기Cover -- 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
요약This 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.
일반주제명Machine learning.
Python (Computer program language)
Machine learning.
Python (Computer program language)
언어영어
기타형태 저록Print version:Amr, TarekHands-On Machine Learning with Scikit-learn and Scientific Python Toolkits : A Practical Guide to Implementing Supervised and Unsupervised Machine Learning Algorithms in PythonBirmingham : Packt Publishing, Limited,c2020
대출바로가기http://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&db=nlabk&AN=2562942

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