Data Mining Algorithms and Techniques Training Course - Beginner and Intermediate Level, for Computer Science Professionals and Non-Professionals. The course content (according to my book published on Google Books), is titled: Advanced Analysis and Data Mining. The book can be searched for using its name or the author's name. Table of Contents Chapter 1: Introduction to Advanced Analysis and Data Mining 1-1 What is data mining, its procedures and tools 1-2 What type of data is mined? 1-3 What are databases? 1-4 Relational Database 1-5 Query Language 1-6 Benefits of Database Mining 1-7 months data mining applications A - Business Intelligence (Business Intelligence) B - Internet search engines Chapter Two: Data Recognition 2-1 Data Types, Characteristics, and Features 2-2 Statistical Description of Data 2-3 Visualization of Data 2-4 Measuring data similarity and difference Chapter Three: Preparing Data for Analysis and Mining 3-1 The importance of preparing data for analysis and mining 3-2 Data Cleanup 3-3 Data Integration 3-4 Data Reduction 3-5 Data Transformation and Data Individualization Chapter Four: Pattern Discovery and Exploration, Dependency and Correlation Rules 4-1 Basic Concepts 4-2 Shopping basket analysis (example) 4-3 Evaluating the dependency and correlation rules being explored 4-4 Mining Multi-Level Dependency and Linkage Rules 4-5 Mining multidimensional dependency and correlation rules 4-6 Rules of nominal and quantitative dependency and correlation 4-7 Exploring and identifying rare and negative patterns 4-8 Exploring and Determining the Rules of Dependency and Conditional Linkage 4-9 Evaluating dependency and correlation rules and distinguishing between useful and unhelpful ones 4-10 Measuring the type and strength of the relationship in dependency and correlation rules 4-11 Applications of pattern mining in practical life Chapter Five: Analysis and Mining Using Classification and Prediction Algorithms 5-1 Basic Concepts 5-2 Classification using decision tree extrapolation 5-3 Classification using probability theory (hypothetical theory) 5-4 Classification using hypothetical network theory 5-5 Classification using correlation rules extrapolation 5-6 Classification using neural network algorithm 5-7 Classification using the nearest neighbor algorithm 5-8 Multi-category classification algorithms 5-9 Evaluating the efficiency and selection of classification algorithms Chapter Six: Analysis and Mining Using Cluster Hashing Algorithms 6-1 Basic Concepts 6-2 Clustering by Division 6-3 Hierarchical Clustering A. Hierarchical clustering b. Hierarchical fission 6-4 Probability Clustering 6-5 High-Dimensional Clustering 6-6 Clustering of graphs and network data 6-7 Conditional Clustering 6-8 Cluster Segmentation Assessment Chapter Seven: Analyzing and Mining Outliers and Complex Data Types 7-1 Basic Concepts 7-2 Types of extreme values 7-3 Ways to Explore Extreme Values 7-4 Complex Data Analysis and Mining Chapter Eight: Planning Data Mining Operations and Their Applications in Society 8-1 Planning Data Mining Operations 8-2 Data Mining in the Community 8-3 Data mining applications in vital areas of society 8-4 Practical Application: Recommendation System Usage Scenario Appendix 1: Database Fundamentals Appendix 2: Data Warehouse Fundamentals Appendix 3: Glossary of Data Mining Terms

As a computer science teacher and software engineer, I offer private online tutoring for secondary school students (High School/Baccalaureate) and university students. My goal is to make computer science simple, intuitive and practical, by combining the rigor of the school curriculum with the reality of the professional software world. For High School / Baccalaureate: Perfect mastery of algorithms, programming logic (Python, Pascal, Web, etc.), homework review and intensive preparation for Baccalaureate exams. For Higher Education (Preparatory classes, Bachelor's degrees, Engineering schools): Programming languages (C/C++, Java, Python, JavaScript...), data structures, OOP, databases (SQL) and application development.