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.
Offered By
Google CloudAbout this Course
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Shareable Certificate
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100% online
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Intermediate Level
Approx. 22 hours to complete
English
Subtitles: English
Skills you will gain
TensorflowBigqueryMachine LearningData Cleansing
Flexible deadlines
Reset deadlines in accordance to your schedule.
Shareable Certificate
Earn a Certificate upon completion
100% online
Start instantly and learn at your own schedule.
Intermediate Level
Approx. 22 hours to complete
English
Subtitles: English
Offered by
Google Cloud
We help millions of organizations empower their employees, serve their customers, and build what’s next for their businesses with innovative technology created in—and for—the cloud. Our products are engineered for security, reliability, and scalability, running the full stack from infrastructure to applications to devices and hardware. Our teams are dedicated to helping customers apply our technologies to create success.
Syllabus - What you will learn from this course
8 minutes to complete
Introduction to Course
In this course you’ll get foundational ML knowledge so that you understand the terminology that we use throughout the specialization. You will also learn practical tips and pitfalls from ML practitioners here at Google and walk away with the code and the knowledge to bootstrap your own ML models.
8 minutes to complete
2 videos (Total 8 min)
4 hours to complete
Improve Data Quality and Exploratory Data Analysis
In this module, we will introduce data quality issues and how to improve them. We will then look at exploratory data anlaysis.
4 hours to complete
5 videos (Total 29 min), 1 reading, 4 quizzes
8 hours to complete
Practical ML
In this module, we will introduce some of the main types of machine learning and review the history of ML leading up to the state of the art so that you can accelerate your growth as an ML practitioner.
8 hours to complete
13 videos (Total 63 min), 1 reading, 11 quizzes
3 hours to complete
Optimization
In this module we will walk you through how to optimize your ML models.
3 hours to complete
13 videos (Total 61 min), 1 reading, 4 quizzes
4 hours to complete
Generalization and Sampling
Now it’s time to answer a rather weird question: when is the most accurate ML model not the right one to pick? As we hinted at in the last module on Optimization -- simply because a model has a loss metric of 0 for your training dataset does not mean it will perform well on new data in the real world. You will learn how to create repeatable training, evaluation, and test datasets and establish performance benchmarks.
4 hours to complete
9 videos (Total 64 min), 1 reading, 4 quizzes
3 hours to complete
Summary
3 hours to complete
1 video (Total 4 min), 3 readings, 1 quiz
Reviews
TOP REVIEWS FROM LAUNCHING INTO MACHINE LEARNING
by ODMay 30, 2020
Amazing course. For a beginner like me, it was a shot in the arm. Excellent presentation very lively and engaging. Hope to see the instructor soon in a another course. Thanks so much. I learned a lot.
by PTDec 1, 2018
This is an awesome module. It will open up so much inside story of ML process which is core of the topic with such a simplicity. It greatly increases my interest into this topic and this course :)
by PAAug 3, 2018
Good course, covering all the basics about machine learning and most importantly, everything that surrounds an ml project and you need to take into account to make your ml project successful.
by CRAug 12, 2021
Interesting course, and the technical details during week 3 and 4 were highly appreicated. The labs could have been a bit better put together, but all round happy with this course!
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