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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 /
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...
| Clasificación: | Libro Electrónico |
|---|---|
| Autor principal: | |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Birmingham, UK :
Packt Publishing, Limited,
2020.
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| Temas: | |
| Acceso en línea: | Texto completo (Requiere registro previo con correo institucional) |
Tabla de Contenidos:
- 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