Microsoft Course: Understand data science for machine learning
Demystify Machine Learning: Your No-Stress ML Starter Kit
- Grasp foundational ML concepts without heavy math prerequisites
- Learn to preprocess data, train models, and assess performance metrics
- Gain confidence in building regression and classification models
- Understand data imbalances and hyperparameter tuning
- Prepare for advancing to deep learning or specialized ML tools
- Build a base for specialized roles in AI development or cloud engineering
- Prepare for more advanced Microsoft certifications in AI
What you will learn
"Understand Data Science for Machine Learning" is a 7-hour 25-minute course designed for learners with minimal mathematical or technical background. This beginner-friendly path breaks down core machine learning concepts through interactive Jupyter notebooks, metaphors, and visuals. Covering foundational topics like supervised learning, regression, classification, and model evaluation, it emphasizes hands-on understanding over advanced tools. Perfect for those new to ML or seeking intuitive explanations, the course balances theory with practical exercises to demystify data science workflows and prepare learners for deeper exploration of AI.
Microsoft's Implement a data science and machine learning solution for AI in Microsoft Fabric course Outline
Learn the Implement a data science and machine learning solution for AI in Microsoft Fabric
Curious about machine learning but intimidated by complex math? "Understand Data Science for Machine Learning" on Microsoft Learn offers a stress-free entry point. With interactive Jupyter notebooks, relatable analogies, and visual guides, this 9-module course teaches core ML principles—from data prep to model evaluation—without overwhelming jargon. Whether you're a career switcher or a curious beginner, start learning today and unlock the skills to turn raw data into actionable insights!
Start your journey today:
Course Structure Includes:
- Module 1: Introduction to ML basics and lifecycle.
- Module 2: Supervised learning fundamentals.
- Module 3: Data preparation and encoding.
- Module 4: Regression models and performance analysis.
- Module 5: Model refinement and testing strategies.
- Module 6: Classification with logistic regression.
- Module 7: Hyperparameter tuning with random forests.
- Module 8: Confusion matrices and handling imbalanced data.
- Module 9: Optimizing models using ROC/AUC curves.
Training Options
Self Paced Learning
- Lifelong access to high-quality content
- Curated by industry experts
- Customized learning progress
- 24/7 learner assistance and support
- Follow the latest technology trends
Exam Dump
- 100% Real Exam Practice Tests
- 100% Verified Exam Questions & Answers
- 100% Guarantee Passing Rate
- Average 7 Days to Practice & Pass
Description
Learn ML basics through interactive notebooks and intuitive explanations. Master data prep, model building, and evaluation for real-world AI projects.
Pre-requisites
None
SPOTO Empowers You to Earn Your Certification.
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SPOTO's study resources provide comprehensive coverage of the exam syllabus, aligning with key topics like ML problem framing, data processing, model optimization, and pipeline automation. The materials include structured learning paths, practice exams, and real-world case studies that mirror the certification's focus on designing, implementing, and deploying machine learning solutions with Microsoft services and ML workflows. By leveraging SPOTO's targeted content, candidates can efficiently bridge knowledge gaps, reinforce practical skills, and gain familiarity with the exam format. This focused preparation increases confidence and readiness, particularly for complex tasks such as deploying CI/CD pipelines or optimizing model performance—areas emphasized in the certification. Combined with hands-on experience, SPOTO's resources offer a strategic advantage for achieving certification success.
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