Mastering Machine Learning with Scikit-Learn (Python)

Cover
Copyright
Credits
About the Author
About the Reviewer
www.PacktPub.com
Customer Feedback
Table of Contents
Preface
Chapter 1: The Fundamentals of Machine Learning
Defining machine learning
Learning from experience
Machine learning tasks
Training data, testing data, and validation data
Bias and variance
An introduction to scikit-learn
Installing scikit-learn
Installing using pip
Installing on Windows
Installing on Ubuntu 16.04
Installing on Mac OS
Installing Anaconda
Verifying the installation
Installing pandas, Pillow, NLTK, and matplotlib
Summary
Chapter 2: Simple Linear Regression
Simple linear regression
Evaluating the fitness of the model with a cost function
Solving OLS for simple linear regression
Evaluating the model
Summary
Chapter 3: Classification and Regression with k-Nearest Neighbors
K-Nearest Neighbors
Lazy learning and non-parametric models
Classification with KNN
Regression with KNN
Scaling features
Summary
Chapter 4: Feature Extraction
Extracting features from categorical variables
Standardizing features
Extracting features from text
The bag-of-words model
Stop word filtering
Stemming and lemmatization
Extending bag-of-words with tf-idf weights
Space-efficient feature vectorizing with the hashing trick
Word embeddings
Extracting features from images
Extracting features from pixel intensities
Using convolutional neural network activations as features
Summary
Chapter 5: From Simple Linear Regression to Multiple Linear Regression
Multiple linear regression
Polynomial regression
Regularization
Applying linear regression
Exploring the data
Fitting and evaluating the model
Gradient descent
Summary
Chapter 6: From Linear Regression to Logistic Regression
Binary classification with logistic regression
Spam filtering
Binary classification performance metrics
Accuracy
Precision and recall
Calculating the F1 measure
ROC AUC
Tuning models with grid search
Multi-class classification
Multi-class classification performance metrics
Multi-label classification and problem transformation
Multi-label classification performance metrics
Summary
Chapter 7: Naive Bayes
Bayes' theorem
Generative and discriminative models
Naive Bayes
Assumptions of Naive Bayes
Naive Bayes with scikit-learn
Summary
Chapter 8: Nonlinear Classification and Regression with Decision Trees
Decision trees
Training decision trees
Selecting the questions
Information gain
Gini impurity
Decision trees with scikit-learn
Advantages and disadvantages of decision trees
Summary
Chapter 9: From Decision Trees to Random Forests and Other Ensemble Methods
Bagging
Boosting
Stacking
Summary
Chapter 10: The Perceptron
The perceptron
Activation functions
The perceptron learning algorithm
Binary classification with the perceptron
Document classification with the perceptron
Limitations of the perceptron
Summary
Chapter 11: From the Perceptron to Support Vector Machines
Kernels and the kernel trick
Maximum margin classification and support vectors
Classifying characters in scikit-learn
Classifying handwritten digits
Classifying characters in natural images
Summary
Chapter 12: From the Perceptron to Artificial Neural Networks
Nonlinear decision boundaries
Feed-forward and feedback ANNs
Multi-layer perceptrons
Training multi-layer perceptrons
Backpropagation
Training a multi-layer perceptron to approximate XOR
Training a multi-layer perceptron to classify handwritten digits
Summary
Chapter 13: K-means
Clustering
K-means
Local optima
Selecting K with the elbow method
Evaluating clusters
Image quantization
Clustering to learn features
Summary
Chapter 14: Dimensionality Reduction with Principal Component Analysis
Principal component analysis
Variance, covariance, and covariance matrices
Eigenvectors and eigenvalues
Performing PCA
Visualizing high-dimensional data with PCA
Face recognition with PCA
Summary
Index