Machine Learning Project Checklist
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Project Checklist

Date:

September 2, 2050

Company:

[Your Company Name]

Creator:

[Your Name]

A machine learning project checklist is a specialized guide for practitioners involved in developing machine learning solutions. It covers crucial stages of the project, including data preparation, model selection, training, and evaluation. This checklist ensures that machine learning projects follow best practices, resulting in robust and well-performing models.

Data Preparation

  • Collect raw data

  • Preprocess data

  • Visualize data

  • Scale and normalize data

  • Data splitting (training set & testing set)

Model Training & Validation

  • Implement algorithm/algorithm tuning

  • Fine-tune ML model parameters

  • Assess the ML model with the validation dataset

  • Employ ensemble methods

  • Address overfitting issues

Model Deployment & Maintenance

  • Finalize model

  • Present machine learning solution

  • Deploy model

  • Maintenance and Monitoring

  • Model retraining

Reminders

  1. Triple-check the data: rectified data errors save countless hours in the long run.

  2. Document every step: in complex projects, retracing steps can be difficult without proper documentation.

  3. Stay patient: A good model comes with time and fine-tuning.

  4. Stay updated: Machine Learning is an ever-evolving field. Always stay updated with the latest tools and methodologies.

  5. Teamwork: Collaborate and work as a team to effectively share knowledge and speed up the process.

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