Full Stack AI Masterclass: 18 Courses in 1

Explore embeddings, the hidden engine of modern AI, converting data into numerical vectors that encode meaning. See how semantic search and chatbots rely on cosine similarity and vector geometry.

Install MongoDB Atlas for documents, embeddings, vector indexes; use Compass for exploration; install Python pip packages, Node.js and npm packages, OpenAI and Huggingface SDKs; enable Atlas search for semantic search.

Compare MongoDB Compass and command line for building AI powered apps, highlighting visual data inspection, schema analysis, guided vector indexing, visual aggregation pipelines, and easy embedding validation.

Load data from files, databases, or APs into a Jupyter notebook, then explore, visualize, and clean it before feature engineering and training models. Evaluate results and iterate to improve models.

Compare machine learning and deep learning by highlighting feature engineering, data size, computing power, and model complexity; clarify ideal use cases from tabular data to images, audio, and text.

Explore feature engineering, turning raw data into meaningful features, and compare manual ML feature crafting with automatic deep learning extraction from unstructured data like images and text.

Learn how structured data enables fast, interpretable predictions with SQL-friendly tables and classical models, while unstructured data, stored in data lakes or object storage, drives deep learning and perception tasks.

Compare labeled and unlabeled data to show how supervised learning achieves accuracy with labeled outputs, while unsupervised and self-supervised learning leverage abundant, unlabeled data for scalable, diverse models.

Learn to use the mongo shell to create collections and insert documents, then search with find using projections, sorting, and limits, and apply operators like $gt while noting _id.

Update and delete documents in MongoDB using update and remove, with a search term and new data; apply $set, $unset, and multi for changes, or replace documents and drop collections.

Learn how to model MongoDB document relationships—1 to 1, 1 to many, and many to many—using articles and comments to optimize performance and design a scalable database architecture.

Improve MongoDB queries by creating, viewing, and dropping indexes on fields like author and on array values, and enable text search for body content.