scikit-learn Cookbook - Second Edition
Python is quickly becoming the go-to language for analysts and data scientists due to its simplicity and flexibility, and within the Python data space, scikit-learn is the unequivocal choice for machine learning. This book includes walk throughs and solutions to the common as well as the not-so-common problems in machine learning, and how scikit-learn can be leveraged to perform various machine learning tasks effectively.
The second edition begins with taking you through recipes on evaluating the statistical properties of data and generates synthetic data for machine learning modelling. As you progress through the chapters, you will comes across recipes that will teach you to implement techniques like data pre-processing, linear regression, logistic regression, K-NN, Naïve Bayes, classification, decision trees, Ensembles and much more. Furthermore, you’ll learn to optimize your models with multi-class classification, cross validation, model evaluation and dive deeper in to implementing deep learning with scikit-learn. Along with covering the enhanced features on model section, API and new features like classifiers, regressors and estimators the book also contains recipes on evaluating and fine-tuning the performance of your model.
By the end of this book, you will have explored plethora of features offered by scikit-learn for Python to solve any machine learning problem you come across.
|Course Length||11 hours 13 minutes|
|Date Of Publication||16 Nov 2017|
|Loading the iris dataset|
|Viewing the iris dataset|
|Viewing the iris dataset with Pandas|
|Plotting with NumPy and matplotlib|
|A minimal machine learning recipe – SVM classification|
|Putting it all together|
|Machine learning overview – classification versus regression|
|Loading data from the UCI repository|
|Viewing the Pima Indians diabetes dataset with pandas|
|Looking at the UCI Pima Indians dataset web page|
|Machine learning with logistic regression|
|Examining logistic regression errors with a confusion matrix|
|Varying the classification threshold in logistic regression|
|Receiver operating characteristic – ROC analysis|
|Plotting an ROC curve without context|
|Putting it all together – UCI breast cancer dataset|