Machine learning explores the study and construction of algorithms that can learn from and make predictions on data. This book will act as an entry point for anyone who wants to make a career in Machine Learning. It covers algorithms like Linear regression, Logistic Regression, SVM, Naïve Bayes, K-Means, Random Forest, and Feature engineering.

• Cover
• Title Page
• Copyright and Credits
• Dedication
• Packt Upsell
• Contributors
• Table of Contents
• Preface
• Chapter 1: A Gentle Introduction to Machine Learning
  • Introduction – classic and adaptive machines
    • Descriptive analysis
    • Predictive analysis
  • Only learning matters
    • Supervised learning
    • Unsupervised learning
    • Semi-supervised learning
    • Reinforcement learning
    • Computational neuroscience
  • Beyond machine learning – deep learning and bio-inspired adaptive systems
  • Machine learning and big data
  • Summary
• Chapter 2: Important Elements in Machine Learning
  • Data formats
    • Multiclass strategies
      • One-vs-all
      • One-vs-one
  • Learnability
    • Underfitting and overfitting
    • Error measures and cost functions
    • PAC learning
  • Introduction to statistical learning concepts
    • MAP learning
    • Maximum likelihood learning
  • Class balancing
    • Resampling with replacement
    • SMOTE resampling
  • Elements of information theory
    • Entropy
    • Cross-entropy and mutual information 
    • Divergence measures between two probability distributions
  • Summary
• Chapter 3: Feature Selection and Feature Engineering
  • scikit-learn toy datasets
  • Creating training and test sets
  • Managing categorical data
  • Managing missing features
  • Data scaling and normalization
    • Whitening
  • Feature selection and filtering
  • Principal Component Analysis
    • Non-Negative Matrix Factorization
    • Sparse PCA
    • Kernel PCA
  • Independent Component Analysis
  • Atom extraction and dictionary learning
  • Visualizing high-dimensional datasets using t-SNE
  • Summary
• Chapter 4: Regression Algorithms
  • Linear models for regression
  • A bidimensional example
  • Linear regression with scikit-learn and higher dimensionality
    • R2 score
    • Explained variance
    • Regressor analytic expression
  • Ridge, Lasso, and ElasticNet
    • Ridge
    • Lasso
    • ElasticNet
  • Robust regression
    • RANSAC
    • Huber regression
  • Bayesian regression
  • Polynomial regression
  • Isotonic regression
  • Summary
• Chapter 5: Linear Classification Algorithms
  • Linear classification
  • Logistic regression
  • Implementation and optimizations
  • Stochastic gradient descent algorithms
  • Passive-aggressive algorithms
    • Passive-aggressive regression
  • Finding the optimal hyperparameters through a grid search
  • Classification metrics
    • Confusion matrix
    • Precision
    • Recall
    • F-Beta
    • Cohen's Kappa
    • Global classification report
    • Learning curve
  • ROC curve
  • Summary
• Chapter 6: Naive Bayes and Discriminant Analysis
  • Bayes' theorem
  • Naive Bayes classifiers
  • Naive Bayes in scikit-learn
    • Bernoulli Naive Bayes
    • Multinomial Naive Bayes
      • An example of Multinomial Naive Bayes for text classification
    • Gaussian Naive Bayes
  • Discriminant analysis
  • Summary
• Chapter 7: Support Vector Machines
  • Linear SVM
  • SVMs with scikit-learn
    • Linear classification
  • Kernel-based classification
    • Radial Basis Function
    • Polynomial kernel
    • Sigmoid kernel
    • Custom kernels
    • Non-linear examples
  • ν-Support Vector Machines
  • Support Vector Regression
    • An example of SVR with the Airfoil Self-Noise dataset
  • Introducing semi-supervised Support Vector Machines (S3VM)
  • Summary
• Chapter 8: Decision Trees and Ensemble Learning
  • Binary Decision Trees
    • Binary decisions
    • Impurity measures
      • Gini impurity index
      • Cross-entropy impurity index
      • Misclassification impurity index
    • Feature importance
  • Decision Tree classification with scikit-learn
  • Decision Tree regression
    • Example of Decision Tree regression with the Concrete Compressive Strength dataset
  • Introduction to Ensemble Learning
    • Random Forests
      • Feature importance in Random Forests
    • AdaBoost
    • Gradient Tree Boosting
    • Voting classifier
  • Summary
• Chapter 9: Clustering Fundamentals
  • Clustering basics
  • k-NN
  • Gaussian mixture
    • Finding the optimal number of components
  • K-means
    • Finding the optimal number of clusters
      • Optimizing the inertia
      • Silhouette score
      • Calinski-Harabasz index
      • Cluster instability
  • Evaluation methods based on the ground truth
    • Homogeneity 
    • Completeness
    • Adjusted Rand Index
  • Summary
• Chapter 10: Advanced Clustering
  • DBSCAN
  • Spectral Clustering
  • Online Clustering
    • Mini-batch K-means
    • BIRCH
  • Biclustering
  • Summary
• Chapter 11: Hierarchical Clustering
  • Hierarchical strategies
  • Agglomerative Clustering
    • Dendrograms
    • Agglomerative Clustering in scikit-learn
    • Connectivity constraints
  • Summary
• Chapter 12: Introducing Recommendation Systems
  • Naive user-based systems
    • Implementing a user-based system with scikit-learn
  • Content-based systems
  • Model-free (or memory-based) collaborative filtering
  • Model-based collaborative filtering
    • Singular value decomposition strategy
    • Alternating least squares strategy
    • ALS with Apache Spark MLlib
  • Summary 
• Chapter 13: Introducing Natural Language Processing
  • NLTK and built-in corpora
    • Corpora examples
  • The Bag-of-Words strategy
    • Tokenizing
      • Sentence tokenizing
      • Word tokenizing
    • Stopword removal
      • Language detection
    • Stemming
    • Vectorizing
      • Count vectorizing
        • N-grams
      • TF-IDF vectorizing
  • Part-of-Speech
    • Named Entity Recognition
  • A sample text classifier based on the Reuters corpus
  • Summary
• Chapter 14: Topic Modeling and Sentiment Analysis in NLP
  • Topic modeling
    • Latent Semantic Analysis
    • Probabilistic Latent Semantic Analysis
    • Latent Dirichlet Allocation
  • Introducing Word2vec with Gensim
  • Sentiment analysis
    • VADER sentiment analysis with NLTK
  • Summary
• Chapter 15: Introducing Neural Networks
  • Deep learning at a glance
    • Artificial neural networks
  • MLPs with Keras
    • Interfacing Keras to scikit-learn
  • Summary
• Chapter 16: Advanced Deep Learning Models
  • Deep model layers
    • Fully connected layers
      • Convolutional layers
      • Dropout layers
      • Batch normalization layers
      • Recurrent Neural Networks
  • An example of a deep convolutional network with Keras
  • An example of an LSTM network with Keras
  • A brief introduction to TensorFlow
    • Computing gradients
    • Logistic regression
    • Classification with a multilayer perceptron
    • Image convolution
  • Summary
• Chapter 17: Creating a Machine Learning Architecture
  • Machine learning architectures
    • Data collection
    • Normalization and regularization
    • Dimensionality reduction
    • Data augmentation
    •  Data conversion
    • Modeling/grid search/cross-validation
    • Visualization
    • GPU support
    • A brief introduction to distributed architectures
  • Scikit-learn tools for machine learning architectures
    • Pipelines
    • Feature unions
  • Summary
• Other Books You May Enjoy
• Index