The course Applied Machine Learning: Foundations - Matt Harrison aims to describe machine learning approaches implemented in Python. Learn a real-life end-to-end example of the data science and machine learning process from raw data, data preprocessing, feature engineering and selection to model building and model evaluation with model selection, cross-validation and hyperparameter tuning using MLFlow. End the section with coding exercises as part of learning that engages the practical aspects of programming that the text focuses on.
Applied Machine Learning: Foundations - Matt Harrison Table of Contents:
- Introduction
- Introduction to Machine Learning
- EDA
- Model Creation
- Model Evaluation
- Model Tuning
- Model Deployment
- Conclusion
Who is this course for?
- Those programmers interested in shifting their career to machine learning and who code in Python.
- Machine learning fundamentals are to be refined, which is why data scientists prefer it.
- The following are the types of people who may find it useful: working professionals who are seeking experience in creating end-to-end machine learning models.
- Any individual aspiring to learn fundamental techniques in machine learning and use Python to execute them.
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