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Art and Science of Machine Learning

LENGTH OF ACCESS: Two Months
LOCATION: Online
ENROLLMENT DEADLINE: Rolling

Course 6 of 9. 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.

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MLOps (Machine Learning Operations) Fundamentals

LENGTH OF ACCESS: Two Months
LOCATION: Online
ENROLLMENT DEADLINE: Rolling

Course 8 of 9. This course introduces participants to MLOps tools and best practices for deploying, evaluating, monitoring and operating production ML systems on Google Cloud. MLOps is a discipline focused on the deployment, testing, monitoring, and automation of ML systems in production. Machine Learning Engineering professionals use tools for continuous improvement and evaluation of deployed models. 

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Machine Learning Pipelines on Google Cloud

LENGTH OF ACCESS: Two Months
LOCATION: Online
ENROLLMENT DEADLINE:  Rolling

Course 9 of 9. In this course, you will be learning from ML Engineers and Trainers who work with the state-of-the-art development of ML pipelines here at Google Cloud. The first few modules will cover about TensorFlow Extended (or TFX), which is Google’s production machine learning platform based on TensorFlow for management of ML pipelines and metadata. You will learn about pipeline components and pipeline orchestration with TFX. And finally, we will go over how to use MLflow for managing the complete machine learning life cycle. Please take note that this is an advanced-level course.

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Feature Engineering

LENGTH OF ACCESS: Two Months
LOCATION: Online
ENROLLMENT DEADLINE: Rolling

Course 5 of 9. Want to know how you can improve the accuracy of your ML models? What about how to find which data columns make the most useful features? Welcome to Feature Engineering where we will discuss good vs bad features and how you can preprocess and transform them for optimal use in your models.

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How Google Does Machine Learning

LENGTH OF ACCESS: Two Months
LOCATION: Online
ENROLLMENT DEADLINE:  Rolling

Course 2 of 9. What is machine learning, and what kinds of problems can it solve? Google thinks about machine learning slightly differently -- of being about logic, rather than just data. We talk about why such a framing is useful for data scientists when thinking about building a pipeline of machine learning models. Then, we discuss the five phases of converting a candidate use case to be driven by machine learning and consider why it is important the phases not be skipped. We end with a recognition of the biases that machine learning can amplify and how to recognize this.

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Launching Into Machine Learning

LENGTH OF ACCESS: Two Months
LOCATION: Online
ENROLLMENT DEADLINE:  Rolling

Course 3 of 9. Starting from a history of machine learning, we discuss why neural networks today perform so well in a variety of data science problems. We then discuss how to set up a supervised learning problem and find a good solution using gradient descent. This involves creating datasets that permit generalization; we talk about methods of doing so in a repeatable way that supports experimentation.

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Introduction to TensorFlow

LENGTH OF ACCESS: Two Months
LOCATION: Online
ENROLLMENT DEADLINE:  Rolling

Course 4 of 9. This course is focused on using the flexibility and “ease of use” of TensorFlow 2. x and Keras to build, train, and deploy machine learning models.  You will learn about the TensorFlow 2. x API hierarchy and will get to know the main components of TensorFlow through hands-on exercises. You will learn how to design and build a TensorFlow 2. x input data pipeline.  You will get hands-on practice loading CSV data, NumPy arrays, text data, and images using tf.Data.Dataset. You will learn how to train, deploy, and productionalize machine learning models at scale with the Cloud AI Platform.

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LENGTH OF ACCESS: Three Months
LOCATION: Online
ENROLLMENT DEADLINE: Rolling

In this Google Cloud Certificate bundle, take 9 courses at a 10% discount rate off the individual course tuition. Courses include:

1) Google Cloud Platform Big Data and Machine Learning Fundamentals
2) How Google does Machine Learning
3) Launching into Machine Learning
4) Introduction to TensorFlow
5) Feature Engineering
6) Art and Science of Machine Learning
7) Production Machine Learning Systems
8) MLOps (Machine Learning Operations) Fundamentals
9) MLP (Machine Learning Pipelines) on Google Cloud

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