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Python / AI
Python / AI Courses
2 courses2 freePython for Data Engineering
Learn Python through the lens of data engineering: Pandas for data manipulation, reading and writing Parquet/CSV/JSON, building ETL pipelines, and processing large datasets efficiently.
Python Fundamentals for Engineers
Learn Python from zero to confident. Variables, functions, OOP, file I/O, and working with APIs — all explained with real engineering analogies. Perfect starting point for the AI Engineer track.
General
General Courses
26 courses26 freeModel Evaluation & Validation
Accuracy is the wrong metric more often than it is the right one. Choosing a metric that matches the decision, validating honestly on grouped and time-ordered data, tuning without contaminating the test set, and diagnosing a model rather than just scoring it.
Feature Engineering
Where most of the accuracy actually comes from, and where most of the silent mistakes live. Encoding categories without blowing up or leaking, creating signal from domain knowledge, handling dates and text, and recognising target leakage — the bug that makes a model look brilliant in a notebook and useless in production.
Classical ML with scikit-learn
The models that win most real tabular problems and most interview rounds. The scikit-learn contract, pipelines that prevent leakage, the model families and when each earns its place, and the unsupervised methods worth knowing. Deliberately not deep learning — that belongs to the AI Engineer track.
Statistical Inference
Picks up where the maths track stops. That one covers distributions and sampling; this one covers deciding things with them — confidence intervals, hypothesis tests, what a p-value does and does not mean, power, and reading regression as a set of claims rather than a prediction machine.
A/B Testing Essentials
The applied round product companies ask most, and the clearest signal that a candidate reasons about causation rather than correlation. Design a test that can be trusted, size it before you run it, read the result honestly, and know what to do when you cannot randomise at all.
Data Visualisation & Storytelling
Matplotlib and seaborn as craft rather than decoration. The figure/axes model that explains every confusing error, choosing encodings people read correctly, annotation as argument, and presenting a number to someone who will not read your notebook.
Exploratory Data Analysis
The interview round that is almost always live: here is a dataset, tell us something true about it. A repeatable opening sequence, the distributions and relationships worth checking, the traps that make averages lie, and how to turn findings into a narrative someone will act on.
Pandas & Data Wrangling
The tool you will actually spend your day in. Load messy real-world files, fix what arrives broken, reshape data with groupby and merges, and avoid the silent mistakes — chained assignment, exploding row counts, and the wrong choice about missing values.
dbt — Transform Data Like a Software Engineer
Learn dbt (data build tool): write SQL models, build DAGs, test data quality, generate documentation, and deploy to production. The modern standard for data transformation in the cloud warehouse era.
Lakehouse Architecture — Delta Lake, Iceberg & Hudi
Understand the Lakehouse paradigm: why it exists, how Delta Lake, Apache Iceberg, and Apache Hudi implement it, and how to design a modern data lakehouse. The architecture behind the modern data stack.
Databricks — The Unified Data & AI Platform
Learn Databricks from cluster setup to production: Delta Lake, notebooks, jobs, Unity Catalog, and MLflow. The platform used by 60% of Fortune 500 companies for data engineering and AI.
Apache Airflow — Orchestrating Data Pipelines
Learn Apache Airflow from basics to production: DAGs, operators, scheduling, dependency management, error handling, and Airflow on Kubernetes. The industry-standard pipeline orchestrator.
Apache Spark for Data Engineers
Master Apache Spark with PySpark: distributed data processing, DataFrames, Spark SQL, streaming, and performance optimization. The essential skill for processing data at billions-of-rows scale.
Data Modeling for Engineers
Learn how to design data models that scale: relational modeling, dimensional modeling (Kimball), and data vault. Understand schema design, normalization, and when to denormalize for analytics.
Data Warehousing — OLAP, Schemas & Cloud Warehouses
Understand data warehouses from first principles. Learn OLTP vs OLAP, dimensional modeling (star schema), and how to work with cloud data warehouses like Snowflake, BigQuery, and Redshift.
ETL Fundamentals — Extract, Transform, Load
Master the core discipline of data engineering: extracting data from any source, transforming it for analytics, and loading it reliably. Covers ETL vs ELT, incremental loading, data quality, and modern pipeline design.
LangGraph & MCP — Building Stateful AI Agents
Build production-grade stateful AI agents with LangGraph and understand the Model Context Protocol (MCP). Learn how to design agent graphs, manage state, build MCP servers, and connect AI to real tools.
AI Security — Protecting AI Systems in Production
Understand the unique security threats facing AI applications and how to defend against them. Covers prompt injection, jailbreaking, data privacy, PII handling, and secure AI system design.
AI Deployment & Cost Optimization
Deploy AI systems to production and keep costs under control. Covers model serving architectures, Docker/Kubernetes for AI, token economics, caching strategies, and observability.
Fine-Tuning LLMs with LoRA & QLoRA
Learn when and how to fine-tune large language models using parameter-efficient methods. Covers LoRA, QLoRA, dataset preparation, training, evaluation, and serving fine-tuned models.
Vector Databases — From Concept to Production
Master vector databases: how they work, when to use them, and how to choose between pgvector, Pinecone, Weaviate, and Chroma. The backbone of every production RAG system.
AI Agents & Tool Use
Build autonomous AI agents that plan, use tools, and complete multi-step tasks. Covers the ReAct pattern, function calling, memory, multi-agent systems, and production agent architecture.
RAG Architecture — Retrieval Augmented Generation
Build production RAG systems from scratch. Learn document processing, chunking strategies, embeddings, vector search, and full pipeline assembly. The most in-demand AI engineering skill in 2025.
Prompt Engineering Mastery
Master the craft of writing prompts that reliably get the output you want from LLMs. Covers zero-shot, few-shot, chain-of-thought, system prompts, structured outputs, and production prompt management.
SOLID Principles — Write Code That Doesn't Break
Learn the 5 design principles that separate junior developers from seniors. Every concept starts with a real-world story, shows you the bad code that hurts, then the clean code that fixes it — with production Spring Boot examples throughout.
OOP — The Way Things Work
Object-Oriented Programming explained so clearly that a 10-year-old would get it. Every concept starts with a real-world story, then shows you the exact Java code. By the end you will think in objects naturally — and Java will make complete sense.