Master Modern Technology
Through Real
Examples.
Learn programming, cloud engineering, DevOps, machine learning, and software testing through practical, production-ready tutorials.
Core Curriculum
Featured Learning Paths.
AI Engineer Path
Go from Python fundamentals to building production AI systems — covering ML foundations, LLMs, embeddings, RAG, agents, and real-world deployment.
Cloud Engineer Path
Configure security rules, launch elastic VMs, assign roles, and orchestrate server instances.
Data Engineer Path
Master distributed computing, read raw streams, build PySpark schemas, and configure data pipelines.
Data Engineering Curriculum
A beginner-to-advanced curriculum for building reliable data pipelines with batches and streams.
Databases Curriculum
A structured path through schema design, indexing, transactions, and query performance.
DevOps Engineer Path
Go from Linux basics to production-grade CI/CD, Infrastructure as Code, containers, Kubernetes, and observability. Structured beginner → intermediate → advanced.
Interview Preparation Curriculum
Structured practice for behavioral answers, system design reasoning, and distributed correctness.
Kubernetes Administration
Operate Kubernetes confidently: security, configuration, and workload management patterns.
Kubernetes Engineer Path
Build a practical Kubernetes foundation: Pods, Deployments, Services, configuration, and security/observability patterns.
Python Developer Path
Start from variables and basic types, learn functional structure, and build standalone command-line utilities.
System Design Curriculum
From requirements and APIs to scaling patterns and distributed consistency.
Testing Curriculum
Build confidence with the test pyramid, robust test doubles, and advanced flakiness stabilization.
Technology Catalog
All Languages & Tools.
Python
Automation, ML, AI & backend development
JavaScript
Frontend, backend & full-stack web apps
TypeScript
Type-safe modern web & enterprise systems
Java
Enterprise systems, banking & backend APIs
Go
Cloud-native systems & DevOps tooling
C#
.NET backend, Azure & desktop apps
C
OS internals, networking & embedded systems
C++
Game engines, trading & high-performance software
Rust
Safe systems software & cloud infrastructure
SQL
Queries, joins, window functions & CTEs
Bash
Linux automation, CI/CD & DevOps scripting
LLM Engineering
RAG, prompt engineering, fine-tuning & tool use
AI Agents
ReAct, memory, tool-calling & multi-agent systems
Machine Learning
Scikit-learn, PyTorch & MLOps
PyTorch
Autograd, nn.Module & distributed training
TensorFlow
Keras, training loops & TFX deployment
MLOps
Experiment tracking, model registry & CI/CD for ML
NumPy
Arrays, broadcasting & linear algebra
Pandas
DataFrames, groupby & time series
Scikit-Learn
Estimators, pipelines & model selection
AWS
EC2, S3, Lambda, IAM & cloud architecture
Docker
Containers, images & Compose environments
Kubernetes
Pods, deployments, Helm & autoscaling
Linux
Shell, file system, permissions & sysadmin
Git & GitHub
Branching, workflows & GitHub Actions
GitHub Actions
Workflows, matrix builds & reusable actions
Terraform
Infrastructure as code across cloud providers
Ansible
Playbooks, roles & configuration management
Jenkins
Pipelines-as-code, Blue Ocean & plugins
Monitoring & Observability
Prometheus, Grafana & OpenTelemetry
PySpark
Distributed data & Apache Spark DataFrames
Apache Spark
RDDs, DAG execution, Spark SQL & cluster tuning
Kafka
Event streaming, Kafka Streams & ksqlDB
Airflow
DAGs, operators, sensors & production scheduling
Databricks
Delta Lake, Unity Catalog & MLflow integration
Scala
Apache Spark & distributed computing
R
Statistics, analytics & research workflows
dbt
Models, refs, tests & incremental builds
Snowflake
Warehouses, time travel, streams & cost control
CUDA
GPU kernels, memory hierarchy & deep learning acceleration
CUDA C++
Thread hierarchies, shared memory & warp primitives
CUDA Python
CuPy, Numba CUDA & PyCUDA for ML workloads
GPU Computing Fundamentals
SIMT execution, memory coalescing & occupancy
Parallel Programming
Task & data parallelism, synchronisation & parallel patterns
Pytest
Fixtures, parametrize, plugins & coverage
Playwright
Auto-waits, traces & parallel execution
Selenium
WebDriver, POM & cross-browser testing
DSA with Java
Arrays, trees, graphs & DP in Java
DSA with Python
Pythonic DSA & LeetCode-style problems
SQL Interviews
Window functions & schema design questions
Cloud Interviews
AWS/Azure/GCP scenario & architecture Qs
Product Philosophy
Why SkillByExample.
Real-world Examples
Skip the generic "Hello World" boilerplate. Learn syntax through concrete, production-ready code samples designed for real developers.
Project-based Learning
Build actual utilities, cloud infrastructure templates, automation architectures, and ML models as you progress.
100% Free Access
No paywalls, no popups, no tracking. A pure developer-focused curriculum created to empower engineers across the globe.
Quick Starters
Featured Tutorials.
Autonomous Agent Workflows
Build agents that run long multi-step tasks autonomously — task decomposition, parallel execution, checkpointing, and graceful failure recovery.
Agent Evaluation
Measure agent quality systematically — task completion rates, tool use efficiency, safety, and regression testing for agentic systems.
Introduction to AI Agents
Understand what AI agents are, how the ReAct pattern works, and build your first autonomous agent that plans and executes multi-step tasks.
AI Agent Memory Systems
Give agents persistent memory — short-term conversation context, long-term key-value storage, and semantic memory with vector search.
Multi-Agent Systems
Build systems where multiple AI agents collaborate — orchestrators, subagents, parallel execution, and human-in-the-loop patterns.
AI Agent Planning Patterns
Give agents the ability to decompose complex goals into steps, choose tools strategically, and recover from failures.