Scala for Machine Learning 2nd Edition by Patrick R Nicolas ISBN 1787122387 9781787122383

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ISBN 10: 1787122387 
ISBN 13: 9781787122383
Author: Patrick R Nicolas

• Explore a broad variety of data processing, machine learning, and genetic algorithms through diagrams, mathematical formulation, and updated source code in Scala
• Take your expertise in Scala programming to the next level by creating and customizing AI applications
• Experiment with different techniques and evaluate their benefits and limitations using real-world applications in a tutorial style

Scala for Machine Learning 2nd Table of contents:

1: Scala and Data Science

• Introducing data science
• How Scala fits into data science
• Learning the Scala ecosystem
• The functional programming paradigm
• The Scala shell (REPL)
• Learning Scala’s basic syntax
• Immutability and mutability
• Operators
• If-else statements
• Pattern matching
• Functional objects
• Functions as first-class citizens
• Classes, traits, and generics
• Collections
• Building a project with SBT
• Summary

2: Statistical Computing

• Understanding the basics of statistics
• Types of data
• Probability distributions
• Random variables
• Statistical inference
• Data structures for statistical computing
• Vectors
• Matrices
• Data frames
• Statistical and mathematical libraries
• Breeze
• Squants
• Apache Commons Math
• JDistlib
• Summary

3: Data Preprocessing

• The importance of data preprocessing
• Data acquisition
• Types of data
• Data quality
• Data cleaning
• Missing values
• Outliers
• Data transformation
• Normalization and standardization
• Discretization
• Feature engineering
• Feature selection
• Scala libraries for data preprocessing
• Apache Spark for data preprocessing
• Summary

4: Data Visualization

• The importance of data visualization
• Types of plots
• Histograms
• Scatter plots
• Box plots
• Line plots
• Bar charts
• Heatmaps
• Scala libraries for data visualization
• Vegas
• Apache Spark for data visualization
• Summary

5: Regression and Classification

• Understanding supervised learning
• Regression
• Linear regression
• Ridge and Lasso regression
• Polynomial regression
• Support Vector Regression (SVR)
• Classification
• Logistic regression
• Support Vector Machines (SVM)
• Decision trees
• Random forests
• Gradient boosting
• Naive Bayes
• K-Nearest Neighbors (KNN)
• Scala libraries for regression and classification
• MLlib (Spark)
• ML (Spark)
• Weka
• ONNX (Scala bindings if available)
• Summary

6: Clustering

• Understanding unsupervised learning
• Clustering algorithms
• K-Means
• Hierarchical clustering
• DBSCAN
• Gaussian Mixture Models (GMM)
• Evaluating clustering performance
• Silhouette score
• Davies-Bouldin index
• Scala libraries for clustering
• MLlib (Spark)
• Summary

7: Reinforcement Learning and Neural Networks

• Introduction to reinforcement learning
• Markov Decision Processes (MDPs)
• Q-learning
• Deep Q-Networks (DQN)
• Introduction to neural networks
• Perceptrons
• Multi-layer perceptrons (MLPs)
• Convolutional Neural Networks (CNNs)
• Recurrent Neural Networks (RNNs)
• Deep learning frameworks in Scala
• Deeplearning4j
• MXNet (Scala API)
• TensorFlow (Scala API if available)
• Summary

8: Text Mining and Natural Language Processing

• Introduction to text mining
• Text preprocessing
• Tokenization
• Stemming and lemmatization
• Stop word removal
• Feature extraction
• Term Frequency-Inverse Document Frequency (TF-IDF)
• Word embeddings (Word2Vec, GloVe)
• Natural Language Processing (NLP) tasks
• Sentiment analysis
• Text classification
• Named Entity Recognition (NER)
• Scala libraries for NLP
• ScalaNLP/OpenNLP
• Spark NLP
• Summary

9: Dimensionality Reduction

• The curse of dimensionality
• Principal Component Analysis (PCA)
• Linear Discriminant Analysis (LDA)
• t-Distributed Stochastic Neighbor Embedding (t-SNE)
• Non-negative Matrix Factorization (NMF)
• Autoencoders
• Scala libraries for dimensionality reduction
• MLlib (Spark)
• Breeze
• Summary

10: Building Real-World Applications

• Machine learning pipeline
• Data ingestion
• Model training
• Model evaluation
• Model deployment
• Monitoring and maintenance
• Case study 1: Recommender systems
• Collaborative filtering
• Content-based filtering
• Hybrid approaches
• Case study 2: Fraud detection
• Anomaly detection
• Imbalanced datasets
• Case study 3: Predictive maintenance
• Time series analysis
• Feature engineering for time series
• Summary

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Tags: Patrick R Nicolas, Scala, Machine