Cloud Computing & DevOps Course
Cloud Computing and DevOps Roadmap: Linux to Reliable Releases
A practical learning sequence for cloud models, Linux, containers, CI/CD, infrastructure as code, monitoring, and operational review.
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Practical ideas for learning technology, building projects, and making your next career move.
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Cloud Computing & DevOps Course
Linux and Networking Basics for Cloud Deployments
Review processes, permissions, DNS, ports, HTTP, and safe inspection practices when troubleshooting a cloud-hosted service.
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Docker Containers: Images, Volumes, and Runtime Configuration
Understand the difference between Docker images and containers, and review configuration, storage, networks, and secret boundaries.
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CI/CD Pipeline Basics: Tests, Builds, and Safer Deployments
Plan CI/CD stages for automated checks, artifacts, environment approvals, post-deployment health checks, and rollback.
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Terraform Infrastructure as Code: Plans, State, and Review
Review infrastructure plans, understand state and drift, and use approvals and scoped access before changing cloud resources.
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Cloud Monitoring: Logs, Metrics, Alerts, and Recovery
Connect logs, metrics, actionable alerts, least-privilege access, backups, and recovery checks in an operational runbook.
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Data Science and Machine Learning Roadmap: Python to Projects
A practical sequence from Python and data preparation to statistics, machine learning evaluation, and a reproducible capstone.
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A Python and Pandas Workflow for Data Preparation
Inspect, clean, transform, and summarize a table with Pandas while keeping data-quality decisions auditable.
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Statistics for Machine Learning: Samples, Splits, and Metrics
Understand sampling, variability, train/test splits, and why an evaluation metric must match the prediction task.
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Regression vs Classification: Choosing a Machine Learning Task
Distinguish numeric prediction from category prediction, define the target carefully, and build a baseline before choosing a model.
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Model Evaluation and Data Leakage: A Practical Checklist
Use a practical checklist to choose metrics, preserve held-out data, fit preprocessing correctly, and report model limitations.
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Build a Reproducible Data Science Capstone Project
Plan a manageable data project with a clear question, documented source, baseline, evaluation plan, and honest limitations.
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