Code Labs Academy

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Code Labs Academy is an international coding school offering bootcamps with one-on-one career coaching. Whether you're looking to switch careers, upskill, or launch a start-up, we’ve got you covered! From Cybersecurity and Data Science & AI to UX/UI Design and Web Development, our hands-on courses prepare you for the future. Follow us for updates on our bootcamps, blogs, tech news, and more!
#tech #coding
Cyber Securityhttps://codelabsacademy.com/en/courses/cybersecurity?source=mastodon
Data Science & AIhttps://codelabsacademy.com/en/courses/data-science-and-ai?source=mastodon
UX/UI Designhttps://codelabsacademy.com/en/courses/ux-ui-design?source=mastodon
Web Developmenthttps://codelabsacademy.com/en/courses/web-development?source=mastodon

In #ClimateTech and #Forecasting work, ranked risk is not enough if the probability is wrong. This guide shows how to use reliability diagrams, Brier scores, and #Python to calibrate heatwave and flood forecasts for real #DataScience and #MachineLearning decisions. Read the full article: https://codelabsacademy.com/en/blog/calibrating-climate-risk-reliability-diagrams-python-extreme-events?source=mastodon

#ClimateTech #DataScience #Python

Calibrating Climate Risk: Reliability Diagrams in Python

Learn how to calibrate climate risk probabilities with reliability diagrams, Brier scores, and Python workflows for heatwaves, floods, and extreme events.

Building smarter grids means coordinating homes, batteries, and operators, not just forecasting load. Our new guide shows how to design a #MultiAgentRL smart-grid simulator in #Python, shape rewards, and evaluate it like a real energy system.

Read the full article: https://codelabsacademy.com/en/blog/multi-agent-rl-smart-grids-python?source=mastodon

#SmartGrids #ClimateTech #PyTorch

Multi-Agent RL for Smart Grids in Python Deep Dive

Learn how to design and simulate multi-agent reinforcement learning for smart grids in Python, covering rewards, training, evaluation, and deployment.

Trying to land your first #SOCAnalyst role? This roadmap breaks down what to learn, which tools to practise (SIEM, EDR, Wireshark), and a 90‑day plan that produces portfolio-ready case notes and detections.

Read the full guide → https://codelabsacademy.com/en/blog/soc-analyst-roadmap-90-day-practice-plan?source=mastodon

For career changers in #Cybersecurity and #BlueTeam, with practical #InfoSec #Upskilling

SOC Analyst Roadmap: 90-Day Practice Plan | Code Labs

Follow this SOC analyst roadmap to build core skills, learn SOC tools, and complete a 90-day practice plan. Build a portfolio and explore Code Labs Academy.

For #Wireshark and #PacketAnalysis in #Networking: this cheat sheet helps you filter DNS failures, TLS handshakes, and TCP retransmits fast, then follow one conversation with stream filters.

Read the full guide: https://codelabsacademy.com/en/blog/wireshark-filters-cheat-sheet?source=mastodon

#Cybersecurity #NetSec

Wireshark Filters Cheat Sheet for Fast Packet Analysis

Use this Wireshark filters cheat sheet to isolate packets fast (DNS, TCP, TLS, HTTP). Learn workflows and explore Code Labs Academy bootcamps.

Climate ML should not just predict - it should admit uncertainty. This guide shows how to separate #Epistemic vs #Aleatoric uncertainty, run #MCDropout, add Bayesian layers, and check calibration in #PyTorch for climate projections.

Read the full article: https://codelabsacademy.com/en/blog/uncertainty-quantification-climate-neural-networks-bayesian-layers-mc-dropout?source=mastodon

#ClimateTech #MachineLearning

Climate NN Uncertainty: Bayesian Layers & MC Dropout

Learn epistemic vs aleatoric uncertainty for climate ML. Implement MC dropout and Bayesian layers in PyTorch to calibrate reliable climate projections.

Climate forecasting ML isn’t a notebook problem it’s a pipeline problem. This guide shows an end-to-end MLOps setup on Kubernetes + Airflow: NetCDF/Zarr preprocessing, PyTorch training, MLflow tracking, CI/CD, and monitoring for drift + failures.

Read: https://codelabsacademy.com/en/blog/end-to-end-mlops-climate-forecasting-kubernetes-airflow?source=mastodon

#ClimateTech #MLOps #Kubernetes #ApacheAirflow #PyTorch #DataEngineering

End-to-End Climate MLOps with Kubernetes & Airflow

Build an end-to-end climate forecasting MLOps pipeline with Kubernetes & Airflow: NetCDF/Zarr prep, PyTorch training, MLflow, CI/CD, deployment, and monitoring.

Build a #Cybersecurity portfolio with zero hacking. 8 beginner projects: SIEM dashboards, phishing triage, vuln remediation, IR playbook, threat modeling, hardening baseline, cloud IAM + logging, CI checks.

Read the guide → https://codelabsacademy.com/en/blog/beginner-cybersecurity-portfolio-projects-no-hacking?source=mastodon

#BlueTeam #SOC #CareerChange #DevSecOps. Which one first?

Beginner Cybersecurity Portfolio Projects: 8 No-Hack Ideas

Build a standout cybersecurity portfolio without hacking. Try 8 beginner cybersecurity portfolio projects with clear steps and deliverables, start today.

Build a beginner #Cybersecurity #HomeLab that’s cheap, legal, and portfolio-friendly.

Learn a simple VM setup, safe practice targets, and beginner projects like a vulnerability scan report or a mini incident write-up (#Infosec #CareerChange #Linux).

What would you build first?

Read the full guide: https://codelabsacademy.com/en/blog/beginner-cybersecurity-home-lab?source=mastodon

#Cybersecurity #CareerChange #HomeLab #TechCareers

Beginner Cybersecurity Home Lab: Cheap & Legal Setup

Build a beginner cybersecurity home lab on a budget, cheap, legal, and portfolio-friendly. Follow the setup steps, practice safely, and build a portfolio.

SQL interview follow-ups often need window functions.

Learn #SQLWindowFunctions for #DataAnalytics with real examples: latest-per-group (ROW_NUMBER), top-N (RANK/DENSE_RANK), running totals, rolling averages, and LAG/LEAD.

Read the full guide on Code Labs Academy: https://codelabsacademy.com/en/blog/sql-window-functions-interview-examples-beginner-intermediate?source=mastodon

#InterviewPrep #CareerChange

SQL Window Functions for Interviews: Examples & Tips

Master SQL window functions with real interview-style examples: ranking, LAG/LEAD, running totals, and rolling averages. Build skills and apply.

Flood maps drive real decisions, but optical imagery is cloudy when floods hit. Our new guide shows how to train a cloud-aware #UNet in #PyTorch on #Sentinel2 data, evaluate with #IoU and Dice, and export #GIS layers for #DisasterRisk work.

Read the full article: https://codelabsacademy.com/en/blog/deep-learning-flood-risk-mapping-sentinel-2-python?source=mastodon

Flood Risk Mapping with Sentinel‑2 in Python (U‑Net)

Build a cloud‑aware U‑Net in Python to segment flood extent from Sentinel‑2, evaluate with IoU/Dice, and export GIS‑ready risk layers for response planning.