Karamagi Robert. Data Mining and Data Warehouse

Independently published, 2020. — 930 p. — ISBN B08H1RRG2D.This book shall train you to collect data from different sources, and discover knowledge. The book aims to teach you to group data into a Data Warehouse for analysis and reporting. The book further explains how to discover patters that can be discovered from the data by learning from historical datasets using Machine Learning Algorithms. The book provides you with knowledge of general concepts and technologies of Data Manipulation using algorithms and data processing tools. This book shall train you how to recognize problems that can be solved by using Data Warehouse and Data Mining Techniques.Expected OutcomesAt the end of this bok, you will acquire knowledge, skills and competencies that will enable you to:
Know and understand general concepts and technologies of Data Mining.
Develop Data Warehouses and run OLAP queries for various applications analysis
Apply Machine Learning Algorithms in datasets for prediction and description of various problems
Recognize problems that can be solved by Data Mining TechniquesLearning ObjectivesKnowledge Discovery in Database
Knowledge Discovery in Databases Flow and Data Types (Audio, Video, Blobs, Vectors, Lists) and Distributed Database
Data Cleaning - (Extraction, Translation and Loading – (ETL))
Data Warehouse
Data warehouse schema (Star and Snowflakes) and Data Cubes and Data Mart
Data Warehouse Operations
Data Warehouse Operations/ (Roll up, Dice, Roll down) and (On-line Analytical Processing (OLAP) and On-line Transaction Processing (OLTP)
Data Mining
Data Mining Tools, Platforms and Applications and Statistical Data Mining and Datasets (Linked Datasets, Open Datasets, Dataset Discovery/Sharing)
Machine Learning Algorithms
Machine Learning Algorithms Types, Training, Validation, Comparison
Association – Association Rules
Clustering – K Means, Hierarchical Clustering
Classification – Decision Trees, Random Forest, Boosting
Data Mining Application
Deep Learning – Neural Networks, SVMPreface
Learning Objectives

Knowledge Discovery in Databases
Knowledge Discovery in Databases Flow
Data Types
SQL Data Types
MySQL Data Types (Version 8.0)
SQL Server Data Types
Microsoft Access Data Types
Audio Data Types
Video Data Types
Blobs
Vector Data Types
Distributed Database
Types of Distributed Databases
Distributed Database Architectures
Data Warehouse
Data Warehouse Basics
Types of Data Warehouses
Operational Data Stores
ETL (Extract, Transform, and Load) Process
Data Warehouse Modeling
Data Warehouse Implementation
Meta Data
Data Marts
Data Warehouse Delivery Process
Data Warehouse Tools
Data Warehouse Applications
Data Warehouse Architecture
Components or Building Blocks of Data Warehouse
Data Warehouse Design
Data Warehouse Process Architecture
Database Parallelism
Online Analytical Processing
Characteristics of OLAP
OLAP Operations in the Multidimensional Data Model
Types of OLAP
Dimensional Modeling
Multi-Dimensional Data Model
Data Cube
Schema
Star Schema
Snowflake Schema
Fact Constellation Schema
Data Mining
History of Data Mining
Data Mining Techniques
Data Mining Implementation Process
Data Mining Architecture
Data Mining Tools
Data Mining vs. Data Warehousing
Data Mining vs. Big Data
Data Mining vs. Machine Learning
Data Mining Applications
Facebook Data Mining
Social Media Data Mining
Text Data Mining
Bitcoin Data Mining
Orange Data Mining
Educational Data Mining
Data Mining in Healthcare
Data Mining in the World Wide Web
Machine Learning
What is Machine Learning
Applications of Machine learning
Machine learning Life cycle
Installing Anaconda and Python
Difference between Artificial intelligence and Machine learning
How to get datasets for Machine Learning
Data Preprocessing in Machine learning
Supervised Machine Learning
Unsupervised Machine Learning
Reinforcement Learning
Regression
Regression Analysis in Machine learning
Metrics for Evaluating Regression Model Performance
Linear Regression in Machine Learning
Simple Linear Regression in Machine Learning
Multiple Linear Regression
Polynomial Regression
Support Vector Machine Regression
Decision Tree Regression
Random Forest Regression
Lasso Regression
Ridge Regression
Classification
Logistic Regression Classification
K-Nearest Neighbor Classification
Support Vector Machine Classification
Kernel Support Vector Machine Classification
Naïve Bayes Classification
Decision Tree Classification
Random Forest Classification
Evaluating the Classification Model
Clustering
Clustering Performance Evaluation
Affinity Propagation Clustering
K-means Clustering
Mini Batch K-Means Clustering
Mean Shift Clustering
Spectral Clustering
Hierarchichal Clustering
DBSCAN Clustering
OPTICS Clustering
BIRCH Clustering
Gaussian Mixture Models Clustering
Association Rule Learning
Apriori algorithm
Frequent Pattern Growth
Eclat algorithm
Reinforcement Learning Algorithms
Upper Confidence Bound
Thompson Sampling
Natural Language Processing
Deep Learning
Artificial Neural Network
Convolution Neural Network
Recurrent Nueral Network
Dimensionality Reduction
Principal Component Analysis
Kernel Principal Component Analysis
Linear Discriminant Analysis
Quadratic Discriminant Analysis
Model Selection
K-fold Cross Validation
Grid Search
Ensemble Models
Bagging
Boosting
Bagging vs. Boosting
Stacking and Blending
VotingDatasets
Dataset.csv
Salary_Data.csv
50_Startups.csv
Position_Salaries.csv
Social_Network_Ads.csv
Mall_Customers.csv
Market_Basket_Optimisation.csv
Ads_CTR_Optimisation.csv
Restaurant_Reviews.tsv
Churn_Modelling.csv
Wine.csv