Includes bibliographical references and index.

The Trials and Tribulations of a Data Scientist Data? Science? Data Science! The Data Scientist: A Modern Jackalope Data Science Tools From Data to Insight: the Data Science Workflow Python: For Something Completely Different Why Python? Why not?! Firsts Slithers with Python Control Flow Computation and Data Manipulation Pandas to the rescue Plotting and visualising: Matplotlib The Machine that Goes "Ping": Machine Learning and Pattern Recognition Recognising Patterns Artificial Intelligence and Machine Learning Data is good, but other things are also needed Learning, Predicting and Classifying Machine Learning and Data Science Feature selection Bias, Variance and Regularisation: A Balancing Act Some Useful Measures: Distance and Similarity Beware the Curse of Dimensionality Scikit-learn is our Friend Training and Testing Cross-validation The Relationship Conundrum: Regression Relationships between variables: Regression Multivariate Linear Regression Ordinary Least Squares Brain and Body: Regression with one variable Logarithmic transformation Making the Task Easier: Standardisation and Scaling Polynomial Regression Variance-Bias Trade-Off Shrinkage: LASSO and Ridge Jackalopes and Hares: Clustering Clustering Clustering with k-means Summary Unicorns and Horses: Classification Classification Classification with KNN Classification with Logistic Regression Classification with Naive Bayes Decisions, Decisions: Hierarchical Clustering, Decision Trees and Ensable Techniques Hierarchical Clustering Decision Trees Ensemble Techniques Ensemble Techniques in Action Less is More: Dimensionality Reduction Dimensionality Reduction Principal Component Analysis Singular Value Decomposition Recommendation Systems Kernel Tricks under the Sleeve: Support Vector Machines Support Vector Machines and Kernel Methods Pipelines in Scikit-learn.