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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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| 자료유형 | E-Book |
|---|---|
| 개인저자 | Amr, Tarek, author. |
| 서명/저자사항 | 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. |
| 발행사항 | Birmingham, UK : Packt Publishing, Limited, 2020. |
| 형태사항 | 1 online resource (1 volume) : illustrations |
| 소장본 주기 | OCLC control number change |
| ISBN | 9781838823580 1838823581 |
| 내용주기 | 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 |
| 요약 | 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. |
| 일반주제명 | Machine learning. Python (Computer program language) Machine learning. Python (Computer program language) |
| 언어 | 영어 |
| 기타형태 저록 | Print 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 |
| 대출바로가기 | http://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&db=nlabk&AN=2562942 |
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| No. | 등록번호 | 청구기호 | 소장처 | 도서상태 | 반납예정일 | 예약 | 서비스 | 매체정보 |
|---|---|---|---|---|---|---|---|---|
| 1 | WE00018828 | 006.31 | 가야대학교/전자책서버(컴퓨터서버)/ | 대출가능 |
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