- Define supervised and unsupervised learning.
- Explore how cost functions affect the learning process.
- Discover how models are optimized by gradient descent.
- Experiment with learning rates, and see how they can affect training.
Episode
Build classical machine learning models with supervised learning - Episode 2
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.
Build classical machine learning models with supervised learning: Episode 02
Supervised learning is a form of machine learning where an algorithm learns from examples of data. We progressively paint a picture of how supervised learning automatically generates a model that can make predictions about the real world. We also touch on how these models are tested, and difficulties that can arise when training them. In this episode, you will:
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.
Build classical machine learning models with supervised learning: Episode 02
Supervised learning is a form of machine learning where an algorithm learns from examples of data. We progressively paint a picture of how supervised learning automatically generates a model that can make predictions about the real world. We also touch on how these models are tested, and difficulties that can arise when training them. In this episode, you will:
- Define supervised and unsupervised learning.
- Explore how cost functions affect the learning process.
- Discover how models are optimized by gradient descent.
- Experiment with learning rates, and see how they can affect training.
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