Welcome to "AI and Data Science" – a comprehensive, customizable course designed for learners at any level, from beginners to advanced professionals. Whether you're just starting your journey into the world of artificial intelligence and data science or looking to enhance your existing skills, this course will provide you with the knowledge and practical tools you need to excel. What You'll Learn: Fundamentals of Data Science: Understanding data collection, cleaning, and preprocessing; learning to analyze and visualize data using tools like Python, Pandas, and Matplotlib. Introduction to AI and Machine Learning: Explore basic concepts of AI, supervised and unsupervised learning, and popular algorithms (e.g., regression, classification, clustering) with hands-on coding exercises. Advanced AI Techniques: Delve into deep learning, neural networks, and advanced algorithms like decision trees, SVMs, and reinforcement learning. Practical Projects: Work on real-world projects such as predictive modeling, sentiment analysis, and building AI applications using Python libraries like TensorFlow and PyTorch. Storytelling with Data: Develop skills to communicate insights effectively, using data visualization tools and storytelling techniques to create compelling narratives from data. Database Management: Learn how to work with databases (SQL and NoSQL) and manage data efficiently for large-scale applications. What to Prepare: Basic Computer Skills: No prior programming experience is required for beginners, but familiarity with basic computer operations is recommended. Software Setup: Students will need to install software like Python, Jupyter Notebooks, and data science libraries (instructions will be provided during the course). Curiosity and Dedication: This course encourages a hands-on approach, so students should come ready to code, experiment, and learn through practical examples. What to Expect: Customized Learning Experience: Lessons are tailored based on the student’s level and goals, ensuring a personalized approach that aligns with your learning pace and interests. Supportive Environment: Receive one-on-one mentoring and support to help you overcome challenges and master complex topics. Skills You Can Apply Immediately: Gain practical, job-ready skills that are in high demand across industries, including AI, finance, marketing, and tech.

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 senior computer science professor with over 19 years of experience, I offer courses tailored to students, beginners, and professionals. You will learn computer science, programming, SQL and NoSQL databases, as well as the main concepts of artificial intelligence. The courses are based on clear explanations, concrete examples, and practical exercises. The content is adapted to your level and objectives, whether you want to learn, prepare for an exam, complete a project, or improve your skills.

My lessons are designed to take you from simply following code to genuinely understanding how data science works. We can cover the complete data science process, including data cleaning, exploratory data analysis, feature engineering, visualisation, statistics, machine learning, model evaluation and communicating results. Depending on your goals, lessons can include: Python, pandas, NumPy and scikit-learn Data cleaning and exploratory analysis Regression and classification Decision trees, random forests and boosting Clustering and dimensionality reduction Cross-validation and model evaluation Feature engineering and model interpretation Neural networks and deep learning foundations Bayesian modelling and PyMC Portfolio and interview preparation Support understanding university modules and projects I use diagrams, analogies and practical demonstrations to make difficult ideas easier to understand. We will normally begin with an intuitive explanation, look at the underlying logic or mathematics, and then implement the concept in Python. Lessons are personalised around your level. Complete beginners receive a structured learning path, while experienced students can focus on advanced topics, project guidance, debugging or interview preparation. You will be encouraged to explain ideas back to me, interpret results and make your own modelling decisions. My goal is not only to help you produce working code, but to help you become an independent and confident data scientist.