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▼a 1180969905
▼a 1181834366
▼a 1197737347 |
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▼a 2562942
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▼2 23 |
| 100 | 1 |
▼a Amr, Tarek,
▼e author. |
| 245 | 10 |
▼a Hands-on machine learning with scikit-learn and scientific Python toolkits :
▼b a practical guide to implementing supervised and unsupervised machine learning algorithms in Python /
▼c Tarek Amr. |
| 260 | |
▼a Birmingham, UK :
▼b Packt Publishing, Limited,
▼c 2020. |
| 300 | |
▼a 1 online resource (1 volume) :
▼b illustrations |
| 336 | |
▼a text
▼b txt
▼2 rdacontent |
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▼a computer
▼b c
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▼a online resource
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▼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. |
| 588 | |
▼a Description based on online resource; title from digital title page (viewed on November 23, 2020). |
| 590 | |
▼a OCLC control number change |
| 650 | 0 |
▼a Machine learning. |
| 650 | 0 |
▼a Python (Computer program language) |
| 650 | 7 |
▼a Machine learning.
▼2 fast
▼0 (OCoLC)fst01004795 |
| 650 | 7 |
▼a Python (Computer program language)
▼2 fast
▼0 (OCoLC)fst01084736 |
| 655 | 4 |
▼a Electronic books. |
| 776 | 08 |
▼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,c2020 |
| 856 | 40 |
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