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Practical Machine Learning without Coding

LearnQuest via Coursera

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Details

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Provider

Coursera
Pricing

Paid Course
Languages

English
Certificate

Certificate Available
Effort

3 weeks, 1 hour a week
Sessions

Self-Paced
Level

Intermediate
Subtitles

English, Arabic, German, Korean, Portuguese, Japanese, Italian, French, Spanish

Found in

Part of

No-Code Machine Learning and Data Science for Everyone

Overview

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Learn to train and optimize common machine learning models, manage resource limitations, and streamline model building with no-code automation. Analyze output confidently with guided interpretation and error analytics.

Syllabus

Standard ML Models in No-Code Platforms
- Unlock practical machine learning for real-world impact—without ever touching code. In this module, gain hands-on experience building standard ML models on modern no-code platforms. Experiment efficiently by launching batch training jobs, automating performance reviews, and resolving errors confidently using intuitive guides. Simplify the art of hyperparameter tuning and learn to optimize model complexity for key business scenarios. Each lesson delivers actionable skills for fast career growth in today’s data-driven industries.
Advanced Algorithms and Automation for Scale
- Take your machine learning skills to the next level with no-code tools built for scale. In this module, harness cloud-enabled features to process big data and deploy advanced models—without complexity. Build resilience into your workflows with automated backups, Geo specific model tuning, and integrated auditing to spot bias and keep models performing at their peak. Learn the frameworks that future-proof your work and ensure compliance as you grow.
Production Deployment and Integration
- Bring machine learning models to life in real business environments—without a single line of code. This module shows how to deploy solutions quickly and reliably using no-code workflow editors and seamless API integrations. Learn to drive measurable impact, automate deployment, and foster ongoing improvement with strong communication and stakeholder engagement. Master the skills that transform ML prototypes into business-critical assets, clear resistance, and set the stage for success.