Oct '23 in 💬 General
Self-Assessment on ML Knowledge and Experience
The self-assessment on ML knowledge and experience is based on the common 7 steps of machine learning (ML):
- Data Collection: Gather and prepare a dataset.
- Data Preprocessing: Clean and format the data.
- Split Data: Divide it into training and testing sets.
- Model Selection: Choose the appropriate algorithm.
- Model Training: Teach the model on the training data.
- Model Evaluation: Assess performance on the testing data.
- Model Deployment: Implement the model in real-world applications and maintain it as needed.
Self-assessment: evaluating our knowledge and experience in each step of the machine learning process.
Data Collection:
1: No experience, unfamiliar with data collection.
2: Limited experience, but can collect simple datasets.
3: Moderate experience, can collect and preprocess data effectively.
4: Proficient, experienced in collecting diverse datasets.
5: Expert, can handle complex data collection scenarios.
Data Preprocessing:
1: Minimal knowledge, need help with data cleaning.
2: Some familiarity with data cleaning techniques.
3: Can handle basic data cleaning tasks.
4: Proficient in data preprocessing, can deal with complex datasets.
5: Expert in data cleaning and transformation.
Split Data:
1: No understanding of data splitting.
2: Familiar with the concept but need guidance.
3: Can split data into training and testing sets.
4: Experienced in data splitting strategies (e.g., cross-validation).
5: Expert in data partitioning and cross-validation techniques.
Model Selection:
1: Little knowledge, unsure how to choose models.
2: Basic understanding, but difficulty selecting models.
3: Can choose models for simple problems.
4: Proficient in selecting models for various tasks.
5: Expert at model selection and tuning.
Model Training:
1: Limited experience, not familiar with model training.
2: Some understanding but require assistance.
3: Can train models on straightforward datasets.
4: Proficient in training a variety of models.
5: Expert in training complex models and deep learning.
Model Evaluation:
1: Minimal knowledge, unsure about model evaluation.
2: Limited experience, need assistance with evaluation metrics.
3: Can assess models using basic evaluation metrics.
4: Proficient in model evaluation, understand various metrics.
5: Expert in advanced model evaluation techniques.
Model Deployment:
1: No experience in deploying models.
2: Some knowledge but need guidance on deployment.
3: Can deploy models in basic applications.
4: Proficient in model deployment for various scenarios.
5: Expert in deploying models at scale and for production.
Sharing below my self-assessment results after around 3 months' learning. I hope to improve the scores in the next 2-3 months.
- Data collection: 2
- Data preprocessing: 3
- Split data: 2
- Model selection: 2
- Model training: 2
- Model evaluation: 2
- Model deployment: 2
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Self-Assessment on ML Knowledge and Experience
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