AI Database Bootcamp: Beginner to Advanced (12 Courses in 1)

Discover how embeddings convert data into numerical vectors that capture meaning, enabling semantic search, recommendations, and chatbots across text, images, and more through cosine similarity and neural networks.

Explore the five key differences between machine learning and deep learning. Learn how feature engineering, data size, computing power, model complexity, and use cases separate the approaches.

Explore feature engineering: translate raw data into meaningful features for machine learning, contrast manual crafting with automatic learning in deep learning, and understand how features drive model performance.

Explore how structured data in tables and sql databases enables efficient modeling and interpretation, while unstructured data uses deep learning to understand content; together, they provide ai precision and depth.

Compare MongoDB Compass and the command line to build AI-powered apps, highlighting visual data inspection, schema analysis, guided vector indexing, and a visual aggregation pipeline builder for beginners.

Learn the fundamentals of databases, including tables, views, stored procedures, constraints, normalization, and the basics of SQL and NoSQL, with hands-on practice on a local host.

Discover how tables store data with primary keys and foreign keys, including one-to-one and one-to-many relationships, and how views and stored procedures work to run commands.

Explore how database constraints shape schema definitions in MySQL, including not null, unique, primary keys, foreign keys, and check constraints, with demonstrations and triggers to enforce rules.

Master database normalization by organizing data into atomic values, eliminating repeating groups, and applying first through third normal forms for efficient, query-friendly relational schemas.

Create an entity relationship diagram in MySQL Workbench to visualize and connect the database with owners, cats, and pets, using reverse engineering to identify primary and foreign keys.

Define and explore relational database management systems (rdbms), explain when to use them, and describe their components, including tables with attributes, columns, and rows.

Explore NoSQL databases, learn when to use them for unstructured and rapidly changing data, and compare key value stores, document stores, wide column stores, and graph stores.

Compare relational database management systems and NoSQL to understand structured tables, constraints, and cross-table queries. Explore unstructured, scalable data models and the global availability of NoSQL across platforms.

Explore the core MySQL data types across numeric, date time, and string categories, and learn how integer, float, decimal, date time, timestamp, car, varchar, and blob impact storage and querying.

Explore how MySQL data types relate to storage and querying, including spatial and Json types, size-limited types, and how integer scales from tiny to big influence ordering and reporting.

Compare phpMyAdmin and MySQL workbench, then learn to create databases, tables, and columns with proper design, primary keys, and unsigned IDs, using UTF-8 collations and basic user permissions.

Learn to create a database and tables in MySQL Workbench, define primary keys and auto increment, model relations with diagrams, and forward engineer the schema for practical use.

Design tables to avoid redundant data and connect records with primary and foreign keys, while learning to create new users and assign per-table permissions (read, write, update, delete).

Learn to import and export data with csv, xml, and json formats using phpmyadmin and workbench, including preparing files, matching delimiters, and exporting for excel compatibility.

Explore an introduction to MySQL queries and query structure, using phpMyAdmin to build select, insert, update, and delete statements with where restrictions and common operators.

Master inserting, updating, and deleting records in SQL, using both insert into table (columns) values (...) and insert into table set column=value formats, with careful use of where clauses.

Explore the four primary database storage engines: csv, isam, myisam, and innodb, their trade-offs for queries, and how to switch engines in phpmyadmin and workbench.

Learn to master table joining by using inner, left, right, and full outer joins, perform multiple joins, and apply the having clause.

Master joins across purchases, name, and product using aliases to disambiguate columns. Learn to filter by date with where and having clauses and to select specific fields for clearer results.

Learn to order query results by date and other fields, apply arithmetic on results, and manipulate strings with concat and substring to assemble names and initials.

Apply where and having to filter query results and compute totals and averages. Validate data by length using the length function and a having clause.

Group by consolidate multiple rows into one per common value, explore retrieving most recent dates with max, and compute totals with sum to craft concise results.

Use group by to compute each customer's max purchase date and total items, then refine by id-based grouping to ensure unique people and add per-product totals.

Explore subqueries, a query inside another, to fetch data as a temporary table and join with the main query for dynamic, most recent records—illustrated with purchase and product examples.

Learn to use subqueries and inner joins to retrieve the most recent purchase data by date, while handling null values with is null and is not null.

Explore sequences and transactions that control auto incrementing, including start transaction, selecting the max ID, assigning it to a variable, and committing or rolling back an insert.

Learn how to create and search documents in MongoDB via the mongo shell, including databases, collections, inserting JSON documents, filtering with find, and sorting and limiting results.

Update MongoDB documents with the update and set operators, controlling multi-document changes. Replace documents, remove matches, and drop collections to manage data.

Examine how MongoDB uses 1-to-1, 1-to-many, and many-to-many relationships to optimize performance, with examples of users and addresses, articles and comments, and tags, and why separate collections improve loading.

Learn how indexing accelerates MongoDB queries by creating indexes on author, tags, and the body field as text, with examples of get indexes, drop index, and text search.