Based on the actual 200+ page contents you provided, this is much more substantial than a basic beginner ebook. It covers machine-learning algorithms, evaluation, transformations, ensemble methods, clustering, Bayesian networks, Weka, Knowledge Flow, Experimenter, CLI, and embedded machine learning.

One important point before selling: the contents you provided closely resemble the structure/content of an established Data Mining: Practical Machine Learning Tools and Techniques textbook. Make sure you own the copyright or have the necessary rights/permission to sell this version, especially if the chapters/text are reproduced or adapted from that work.

📘 Payhip Product Title

Data Mining: Practical Machine Learning Tools and Techniques

Subtitle

A Comprehensive Guide to Machine Learning Algorithms, Data Mining Methods, Evaluation, Weka, and Practical Applications

Author: Mrs Roopa Phenil Chauhan

Publisher: PRSMC Digital Technologies

Format: Digital PDF

Length: 200+ pages

📝 Payhip Product Description

Master Data Mining and Practical Machine Learning

Data Mining: Practical Machine Learning Tools and Techniques is a comprehensive 200+ page digital guide covering the fundamental concepts, algorithms, evaluation techniques, transformations, applications, and practical machine-learning tools used in data mining.

The book takes readers from the fundamentals of data mining and machine learning through practical algorithms and model evaluation, and then introduces the Weka machine-learning workbench for experimenting with real machine-learning techniques.

Whether you are a student, developer, data analyst, aspiring data scientist, or technology professional, this guide provides a structured reference for understanding how machine-learning methods can be applied to discover patterns, build predictive models, evaluate results, and work with real datasets.

📚 What's Inside?

Part I — Machine Learning Tools and Techniques

Chapter 1 — What Is It All About?

• Data mining and machine learning
• Structural pattern discovery
• Machine learning fundamentals
• Data mining concepts
• Weather and contact-lens examples
• Iris and CPU-performance datasets
• Classification problems
• Numeric prediction
• Real-world applications
• Machine learning and statistics
• Generalization and search
• Bias
• Data-mining ethics

Chapter 2 — Input: Concepts, Instances, and Attributes

• Concepts and examples
• Attributes and attribute types
• Data preparation
• Data collection
• ARFF format
• Sparse data
• Missing values
• Inaccurate values
• Understanding datasets

Chapter 3 — Output: Knowledge Representation

• Decision tables
• Decision trees
• Classification rules
• Association rules
• Rules with exceptions
• Relational rules
• Numeric prediction trees
• Instance-based representation
• Clustering

Chapter 4 — Algorithms: The Basic Methods

• Rudimentary rule learning
• Statistical modeling
• Bayesian models
• Decision-tree construction
• Information-based methods
• Rule-learning algorithms
• Association-rule mining
• Linear regression
• Logistic regression
• Perceptron
• Winnow
• Instance-based learning
• Nearest-neighbor methods
• Clustering

Chapter 5 — Credibility: Evaluating What Has Been Learned

• Training and testing
• Performance prediction
• Cross-validation
• Leave-one-out evaluation
• Bootstrap methods
• Comparing data-mining methods
• Probability prediction
• Cost-sensitive classification
• Lift charts
• ROC curves
• Precision-recall curves
• Cost curves
• Numeric prediction evaluation
• Minimum Description Length (MDL)

Chapter 6 — Implementations: Real Machine Learning Schemes

• Decision trees
• Missing-value handling
• Pruning
• Classification rules
• Linear models
• Maximum-margin methods
• Nonlinear classification
• Support vector regression
• Kernel perceptron
• Multilayer perceptrons
• Instance-based learning
• Numeric prediction
• Model trees
• Locally weighted regression
• Clustering
• EM algorithm
• Bayesian clustering
• Bayesian networks

Chapter 7 — Transformations: Engineering the Input and Output

• Attribute selection
• Feature-space searching
• Numeric discretization
• Principal Component Analysis (PCA)
• Random projections
• Text-to-attribute vectors
• Time-series data
• Automatic data cleansing
• Anomaly detection
• Bagging
• Boosting
• Additive regression
• Logistic model trees
• Stacking
• Error-correcting output codes
• Learning from unlabeled data
• Co-training

Chapter 8 — Moving On: Extensions and Applications

• Massive datasets
• Domain knowledge
• Text and Web mining
• Adversarial situations
• Ubiquitous data mining
• Advanced applications

🖥️ Part II — The Weka Machine Learning Workbench

The second part provides extensive coverage of Weka, including:

Chapter 9 — Introduction to Weka

• Weka overview
• Using Weka
• Weka capabilities
• Installation and access

Chapter 10 — The Explorer

• Preparing datasets
• Loading data
• Building decision trees
• Examining model output
• Filtering
• Training and testing
• User Classifier
• Metalearning
• Clustering
• Association rules
• Attribute selection
• Visualization
• Bayesian classifiers
• Trees
• Rules
• Functions
• Lazy classifiers

Chapter 11 — Knowledge Flow

• Getting started
• Knowledge Flow components
• Configuring components
• Connecting workflows
• Incremental learning

Chapter 12 — The Experimenter

• Creating experiments
• Running experiments
• Analyzing results
• Simple and advanced configurations
• Distributed processing

Chapter 13 — Command-Line Interface

• Weka command line
• Weka structure
• Classes and instances
• Weka packages
• Classifier packages
• Command-line options
• Generic and scheme-specific options

Chapter 14 — Embedded Machine Learning

• Building a data-mining application
• Working with machine-learning code
• Data updates
• Message classification
• Classification workflows