Machine Learning Essentials
eBook - ePub

Machine Learning Essentials

Machine Learning Exam Essentials

  1. English
  2. ePUB (mobile friendly)
  3. Available on iOS & Android
eBook - ePub

Machine Learning Essentials

Machine Learning Exam Essentials

About this book

Connect data, models and evaluation with clearer learning goals

Machine Learning Essentials: Machine Learning Exam Essentials brings your study of machine learning into focus. This Cybellium exam preparation guide connects training data, model approaches and evaluation and generalisation, helping you see how the ideas fit together.

Preparing for an assessment is easier to organise when you can see the relationships behind the terminology. Use it to organise your revision, revisit terminology and strengthen the connections between ideas as you prepare for further assessment.

Build your understanding

  • Understand the role of training data.
  • Explore the relationships behind model approaches.
  • Examine the choices and questions involving evaluation and generalisation.
  • Bring individual concepts together into a more coherent understanding of the subject.

Who this guide is for

For students and professionals organising subject revision with an interest in machine learning. Use the book for focused study, revision or the next stage of your professional learning, returning to the ideas most relevant to your goals.

Your included Cybellium learning bonus

  • One complimentary month of Cybellium access.
  • One additional Cybellium guide download of your choice.
  • Access to the Cybellium Cybersecurity Knowledge Hub and premium learning resources during the included access period.

Activate your unique voucher at www.cybellium.com/signup to begin your included month and select your additional guide.

The included voucher is valid for one-time use only and cannot be combined with another offer.

Continue your learning with Machine Learning Essentials.

Information

Table of contents

  1. Machine Learning Essentials
  2. 1. Introduction to Machine Learning and the Exam
  3. 2. Exam Structure and Strategies
  4. 3. Fundamentals of Machine Learning
  5. 4. Data Preparation and Feature Engineering
  6. 5. Exploratory Data Analysis (EDA)
  7. 6. Supervised Learning Algorithms
  8. 7. Unsupervised Learning Algorithms
  9. 8. Model Evaluation and Validation
  10. 9. Advanced Topics: Ensemble Methods
  11. 10. Neural Networks and Deep Learning Basics
  12. 11. Model Deployment and Monitoring
  13. 12. Machine Learning Tools and Libraries
  14. 13. Ethics, Fairness, and Responsible AI
  15. 14. Troubleshooting and Debugging Models
  16. 15. Real-World Applications and Case Studies
  17. 16. Practice Questions: Core Concepts
  18. 17. Practice Questions: Advanced Topics
  19. 18. Full-Length Mock Exam 1
  20. 19. Full-Length Mock Exam 2
  21. 20. Exam Day Preparation and Resources

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