Episode
Refine and test machine learning models - Episode 5
Join Jason DeBoever and Glenn Stephens live on Learn TV and explore this nine-part "Foundations of data science for machine learning" series. Each week, we will be walking through Learn modules and answering your questions live. From basic classical machine learning models to exploratory data analysis and customizing architectures, you'll be guided by easy to digest conceptual content and interactive Jupyter notebooks and will learn about the underlying concepts as well as how to get into building models with the most common machine learning tools.
Refine and test machine learning models: Episode 05
When we think of machine learning, we often focus on the training process. A small amount of preparation before this process can not only speed up and improve learning but also give us some confidence about how well our models will work when faced with data we have never seen before. In this episode, you will:
- Define feature normalization.
- Create and work with test datasets.
- Articulate how testing models can both improve and harm training.
Join Jason DeBoever and Glenn Stephens live on Learn TV and explore this nine-part "Foundations of data science for machine learning" series. Each week, we will be walking through Learn modules and answering your questions live. From basic classical machine learning models to exploratory data analysis and customizing architectures, you'll be guided by easy to digest conceptual content and interactive Jupyter notebooks and will learn about the underlying concepts as well as how to get into building models with the most common machine learning tools.
Refine and test machine learning models: Episode 05
When we think of machine learning, we often focus on the training process. A small amount of preparation before this process can not only speed up and improve learning but also give us some confidence about how well our models will work when faced with data we have never seen before. In this episode, you will:
- Define feature normalization.
- Create and work with test datasets.
- Articulate how testing models can both improve and harm training.
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