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Online Course

Art and Science of Machine Learning

Google Cloud and Google via Coursera


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.


-Course overview highlighting the key objectives and modules. First, you will learn about aspects of Machine Learning that require some intuition, good judgment and experimentation. We call it the Art of ML. You will learn the many knobs and levers involved in training a model. You will manually adjust them to see their effects on model performance.

The Art of ML
-In this course you will learn about The Art of Machine Learning. We will review aspects of machine learning that require intuition, judgment and experimentation to find the right balance and what’s good enough (spoiler alert: it's never perfect!).

Hyperparameter Tuning
-In this module you will learn how to differentiate between parameters and hyperparameters. Then we’ll discuss traditional grid search approach and learn how to think beyond it with smarter algorithms. Finally you’ll learn how Cloud ML engine makes it so convenient to automate hyperparameter tuning.

A pinch of science
-In this module, we will start to introduce the science along with the art of machine learning. We’re first going to talk about how to perform regularization for sparsity so that we can have simpler, more concise models. Then we’re going to talk about logistic regression and learning how to determine performance.

The science of neural networks
-In this module we will now be diving deep into the science, specifically with neural networks.

-In this module, you will learn how to use embeddings to manage sparse data, to make machine learning models that use sparse data consume less memory and train faster. Embeddings are also a way to do dimensionality reduction, and in that way, make models simpler and more generalizable.

Custom Estimator
-In this module we will go beyond using canned estimators by writing a custom estimator. By writing a custom estimator, you will be able to gain greater control over the model function itself.

-Review the key concepts we covered in the Art and Science of ML course.

Taught by

Google Cloud Training

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