- Course
Art and Science of Machine Learning
Welcome to the Art and Science of Machine Learning. This course is delivered in 6 modules. The course covers the essential skills of ML intuition, good judgment and experimentation needed to finely tune and optimize ML models for the best performance. You will learn how to generalize your model using Regularization techniques and about the effects of hyperparameters such as batch size and learning rate on model performance. We’ll cover some of the most common model optimization algorithms and show you how to specify an optimization method in your TensorFlow code.
Intermediate
- Course
Art and Science of Machine Learning
Welcome to the Art and Science of Machine Learning. This course is delivered in 6 modules. The course covers the essential skills of ML intuition, good judgment and experimentation needed to finely tune and optimize ML models for the best performance. You will learn how to generalize your model using Regularization techniques and about the effects of hyperparameters such as batch size and learning rate on model performance. We’ll cover some of the most common model optimization algorithms and show you how to specify an optimization method in your TensorFlow code.
Intermediate
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This course is included in the libraries shown below:
- Cloud
What you'll learn
This course content has now been retired on Pluralsight and will be removed by the end of 2026
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Welcome to the Art and Science of Machine Learning. This course is delivered in 6 modules. The course covers the essential skills of ML intuition, good judgment and experimentation needed to finely tune and optimize ML models for the best performance. You will learn how to generalize your model using Regularization techniques and about the effects of hyperparameters such as batch size and learning rate on model performance. We’ll cover some of the most common model optimization algorithms and show you how to specify an optimization method in your TensorFlow code.
Art and Science of Machine Learning
Intermediate
Table of contents
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Introduction | 1m 33s
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Regularization | 4m 53s
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L1 & L2 Regularizations | 4m 35s
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Lab Intro: Regularization | 12s
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Getting Started With GCP And Qwiklabs | 3m 48s
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Lab: Regularization | 2m 56s
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Resources Readings - 1 - The Art of ML (The Art of ML) | 10s
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Learning Rate and Batch Size | 5m 9s
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Optimization | 1m 18s
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Lab Intro: Reviewing Learning Curves | 48s
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Lab: Reviewing Learning Curve | 10s
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Resources Readings - 2 - The Art of ML (Learning Rate and Batch Size) | 10s
