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Career Advancement Programme in Model Evaluation Metrics
-- ViewingNowModel Evaluation Metrics: This Career Advancement Programme equips data scientists and machine learning engineers with advanced skills in evaluating predictive models. Learn to select appropriate metrics like precision, recall, F1-score, AUC-ROC, and RMSE.
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2个月完成
每周2-3小时
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课程详情
- Introduction to Model Evaluation Metrics
- Regression Metrics: MSE, RMSE, MAE, R-squared
- Classification Metrics: Accuracy, Precision, Recall, F1-score, AUC-ROC
- Understanding Confusion Matrices and their components
- Bias-Variance Tradeoff and its impact on Model Evaluation
- Choosing the Right Metric for Your Problem: Business Context and Data Characteristics
- Advanced Model Evaluation Techniques: Cross-Validation and Bootstrapping
- Practical Application of Model Evaluation Metrics in Python (using scikit-learn)
- Interpreting Evaluation Results and Improving Model Performance
职业道路
Career Role Description Machine Learning Engineer (Model Evaluation) Develops and implements robust model evaluation metrics for machine learning systems, ensuring high accuracy and performance in various applications.
High demand in Fintech and Healthcare.
Data Scientist (Metrics Specialist) Specialises in designing and implementing advanced evaluation metrics, interpreting results, and communicating findings to stakeholders.
Key skills include statistical modelling and data visualisation.
AI/ML Consultant (Evaluation Focus) Advises clients on the selection and implementation of appropriate model evaluation strategies to optimise their AI/ML systems.
Strong communication and problem-solving skills are essential.
入学要求
- 对主题的基本理解
- 英语语言能力
- 计算机和互联网访问
- 基本计算机技能
- 完成课程的奉献精神
无需事先的正式资格。课程设计注重可访问性。
课程状态
本课程为职业发展提供实用的知识和技能。它是：
- 未经认可机构认证
- 未经授权机构监管
- 对正式资格的补充
成功完成课程后，您将获得结业证书。
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