Complete Data Science Bootcamp: For Beginners (AI, ML, DL)

Architect a modern ai app in layers, from front-end prompts to a back-end api, ai layer with language models, vector store, data layer, and infrastructure for scaling.

Compare traditional databases and vector databases to understand exact, Boolean queries and deterministic results versus similarity-based, probabilistic ranking using dense meaning vectors.

Discover why Python is the language of data science, powering AI and real-time analytics. Explore its rich ecosystem with NumPy, Pandas, Matplotlib, Seaborn, Scikit-learn, TensorFlow, PyTorch, and Jupyter notebooks.

Master Python best practices for data science by improving code quality, using NumPy and pandas efficiently, organizing projects, ensuring reproducibility, and implementing pipelines, testing, and clear visualizations.

Explore embeddings as the hidden engine of modern ai that convert raw data into numerical vectors representing meaning, enabling semantic search, recommendations, chatbots, and cross-modal understanding.

Explore Python data types including booleans, strings, integers, floats, lists, sets, and dictionaries, with practical examples of comparisons, indexing, and Python's division behavior.

Create and manage databases with Python classes, including setting up the database, adding tables and elements, and viewing tables via a reusable SQL classes script.

Create a MySQL database and tables in Python using pymysql, connecting to localhost, creating the Creative Online School, and defining owners and pets with proper data types.

Learn to display data from a database in an html table with python, using dynamic headers and rows, and write the generated html for the owners table to a file.

Load data into database by reading comma-separated text files (owners and pets), build insert queries with .format, insert into owners with auto-incremented IDs, commit, and verify by selecting from owners.

Discover how traditional SQL databases compare to MongoDB for AI applications, highlighting schema flexibility, embedding storage, unstructured data handling, vector search, AI integration, horizontal scaling.

Compare MongoDB Compass with the command line for AI-powered apps, highlighting visual data inspection, schema analysis, and beginner-friendly vector indexing and aggregation pipelines.

Install MongoDB Atlas and Compass for the database and data exploration. Add Python pip packages, Node.js and npm, OpenAI and HuggingFace SDKs, and Atlas Search for semantic retrieval.

Explore basic MongoDB operations in the mongo shell, including creating databases and collections, inserting documents, querying with conditions and operators like $gt, projecting fields, sorting, and limiting results.

Learn to update and delete documents in MongoDB using update with set and unset, and multi for multiple docs; replace, remove, and drop operations complete the workflow.

Explore how to model relationships in MongoDB, including one-to-one, one-to-many, and many-to-many, to optimize performance and data loading.

Part 6 of the MongoDB tutorial explains how indexing speeds queries by adding indexes on author, tags, and the body text, including text search with $text and $search.