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Choosing the Most Appropriate Algorithm
Discover which applied machine learning algorithm is best for your needsPreview the first lesson free — get full access to all 6 lessons.
Intermediate Provider : Briana Brownell 6 Lessons · 15m in English
Course Description
Machine learning in practice requires the practitioner to understand the trade-offs between a variety of factors, both technical and non-technical. A practitioner may wish to consider trade-offs between factors like speed, accuracy, and ethics.
In order to validate a machine-learning model, there are a number of factors that need to be considered. These lessons discuss best practices in splitting the data used for modelling into a training data set and a test data set and comparing results so that common problems like overfitting can be addressed. You’ll also discover common model validation methods, model quality metrics, and common challenges.
Using pre-trained models is an important part of any machine learning practitioner’s toolkit. Pre-trained models for many important tasks like natural language processing, image recognition, and text generation can be used as part of a machine learning pipeline. These lessons discuss the advantages and disadvantages of using them in practice.
What You'll Learn
- Identify the common challenges encountered in model validation
- Assess the trade-offs between technical and non-technical factors such as speed, accuracy, and ethics
- Distinguish between underfitting and overfitting when validating a model
- Apply best practices for splitting data into training and test sets and comparing results
- Evaluate model validation methods and model quality metrics
- Weigh the advantages and disadvantages of using pre-trained models in a machine learning pipeline
Key Takeaways
- Machine learning in practice requires understanding the trade-offs between technical and non-technical factors such as speed, accuracy, and ethics.
- Splitting data into a training set and a test set and comparing results helps address common problems like overfitting.
- Validating a model involves model validation methods, model quality metrics, and awareness of common challenges.
- Pre-trained models for tasks like natural language processing, image recognition, and text generation can be used as part of a machine learning pipeline.
- Using pre-trained models carries both advantages and disadvantages that practitioners should weigh in practice.
Frequently Asked Questions
What does this course cover?
It covers choosing the most appropriate algorithm by considering trade-offs between factors like speed, accuracy, and ethics, validating machine learning models, and using pre-trained models. Lessons include Model Validation, Model Quality Metrics, Common Challenges, Explainability, Using Pre-trained Models, and Ethical Considerations.
What will I learn about model validation?
You'll learn best practices in splitting data into a training set and a test set and comparing results to address problems like overfitting, along with common model validation methods, model quality metrics, and common challenges. You'll also learn to distinguish between underfitting and overfitting.
Does this course address pre-trained models?
Yes. It discusses using pre-trained models for tasks such as natural language processing, image recognition, and text generation as part of a machine learning pipeline, including the advantages and disadvantages of using them in practice.
What skills does this course help develop?
It addresses skills including Learning Metrics, Machine Learning Algorithms, Machine Learning Methods, Machine Learning Model Training, Machine Learning Model Monitoring and Evaluation, and Quantitative Modeling.
Who is this course for?
It is for machine learning practitioners who need to understand the trade-offs between technical and non-technical factors when validating models and choosing an appropriate algorithm.
