About the Book
This is a comprehensive resource on various concepts in machine learning, deep learning, and artificial intelligence. Learning-based systems and models are fundamental to automation and artificial intelligence and they will form the core to all functions in business and all aspects of human lives. Hence, the importance of machine learning and artificial intelligence cannot be overemphasized. This book makes an integrated reference combining all concepts and applications of machine learning and artificial intelligence.
Key Features
Comprehensive review of AI methodologies, combining all important concepts in machine learning, deep learning, and reinforcement learning.
Includes worked examples, case studies, and end-of-chapter summaries.
Use of mathematics for more comprehensive explanations of the machine learning models unlike most of the current books on the market.
Real-world applications and case studies to illustrate theoretical concepts in machine learning, machine learning, and reinforcement learning.
Table of Contents:
Volume I – Machine Learning
1. The Machine Learning Landscape
1. What is Machine Learning?
2. Why use Machine Learning?
3. Examples of Applications: types of machine learning systems,
supervised and unsupervised learning, batch and online learning
instance-based versus model-based learning.
4. Main Challenges of Machine Learning: insufficient quantity of training
data, nonrepresentative training data, poor-quality data, irrelevant
features, overfitting the training data, stepping back.
5. Testing and Validating: hyperparameter tuning and model selection
6. Data Mismatch
2. End-to-End Machine Learning Project
1. Working with Real-World Data:
2. Looking at the Bigger Picture: framing the problem, selecting a
performance metric, checking the assumptions.
3. Getting the Data: creating the workspace, downloading the data,
understanding the data structure, creating a test set
4. Discovering and Visualizing the Data to Gain Insight: visualizing
geographical data, looking for correlations, experimenting with
attribute combinations.
5. Preparing the Data for Machine Learning Algorithms: data cleaning,
handling text and categorical attributes, custom transformers, feature
scaling, transformation pipelines.
6. Selecting and Training a Model: training and evaluating on the training
set, better evaluation using cross-validation.
7. Fine-Tuning the Model: grid search, randomized search, ensemble
methods, analyze the best models and their errors, evaluate the model
on the test set
8. Launching, Monitoring, and Maintaining the Model
3. Classification
1. MNIST
2. Training a Binary Classifier
3. Performance Metrics: measuring accuracy using cross-validation,
confusion matrix, precision and recall, precision-recall trade-off, the
ROC curve.
4. Multiclass Classification
5. Error Analysis
6. Multilabel Classification
7. Multioutput Classification
4. Training Models
1. Linear Regression: the normal equation, computational complexity
2. Gradient Descent: batch gradient descent, stochastic gradient descent,
mini-batch gradient descent
3. Polynomial Regression
4. Learning Curves
5. Regularized Linear Models: ridge regression, LASSO regression, elastic
net, early stopping.
6. Logistic Regression: estimating probabilities, training and cost
functions, decision boundaries, Softmax regression.
5. Support Vector Machines1. Linear SVM Classification: soft margin classification
2. Nonlinear SVM Classification: polynomial kernel, similarity features,
Gaussian RBF kernel, computational complexity.
3. SVM Regression
4. Under the Hood: decision function and predictions, training objectives,
quadratic programming, the dual problem, kernelized SVMs, online
SVMs.
6. Decision Trees
1. Training and Visualizing a Decision Tree:
2. Making Predictions
3. Estimating Class Probabilities
4. The CART Training Algorithm
5. Computational Complexity
6. Gini Impurity or Entropy
7. Regularization Hyperparameters
8. Regression
9. Instability
7. Ensemble Learning and Random Forests
1. Voting Classifiers:
2. Bagging and Pasting: Scikit-Learn in bagging and pasting
3. Random Patches and Random Subspaces:
4. Random Forests: extra-trees, feature importance
5. Boosting: Adaboost, gradient boosting
6. Stacking
8. Dimensionality Reduction
1. The Curse of Dimensionality
2. Main Approaches for Dimensionality Reduction: projection, manifold
learning.
3. Principal Component Analysis: preserving the variance, principal
components, projecting down to a minimum number of dimensions,
Use of Scikit-Learn, explained variance ratio, choosing the right number
of dimensions, PCA for compression, randomized PCA, incremental
PCA.
4. Kernel PCA: selecting a Kernel and tuning hyperparameters
5. Locally Linear Embedding
6. Other Dimensionality Reduction Techniques
9. Unsupervised Learning Techniques
1. Clustering: K-means, limits of k-means, using clustering for image
segmentation, using clustering for pre-processing, using clustering for
semi-supervised learning, DBSCAN, other clustering algorithms.
2. Gaussian Mixtures: Anomaly detection using Gaussian mixtures,
selecting the number of clusters, Bayesian Gaussian Mixture Models,
Other algorithms for anomaly and novelty detection
