The course begins with a discussion about data: how to improve data quality and perform exploratory data analysis. We describe Vertex AI AutoML and how to build, train, and deploy an ML model without writing a single line of code. You will understand the benefits of Big Query ML. We then discuss how to optimize a machine learning (ML) model and how generalization and sampling can help assess the quality of ML models for custom training.
Offered By
Google CloudAbout this Course
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Shareable Certificate
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100% online
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Beginner Level
Approx. 17 hours to complete
English
Subtitles: English
What you will learn
Describe how to improve data quality and perform exploratory data analysis
Build and train AutoML Models using Vertex AI and BigQuery ML
Optimize and evaluate models using loss functions and performance metrics
Create repeatable and scalable training, evaluation, and test datasets
Skills you will gain
- Tensorflow
- Bigquery
- Machine Learning
- Data 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.
Beginner Level
Approx. 17 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
1 minute to complete
Introduction
This module provides an overview of the course and its objectives.
1 minute to complete
1 video (Total 1 min)
4 hours to complete
Get to Know Your Data: Improve Data through Exploratory Data Analysis
In this module, we look at how to improve the quality of our data and how to explore our data by performing exploratory data analysis. We look at the importance of tidy data in Machine Learning and show how it impacts data quality. For example, missing values can skew our results. You will also learn the importance of exploring your data. Once we have the data tidy, you will then perform exploratory data analysis on the dataset.
4 hours to complete
9 videos (Total 57 min), 1 reading, 3 quizzes
3 hours to complete
Machine Learning in Practice
In this module, we will introduce some of the main types of machine learning so that you can accelerate your growth as an ML practitioner.
3 hours to complete
6 videos (Total 45 min), 1 reading, 2 quizzes
3 hours to complete
Training AutoML Models Using Vertex AI
In this module, we will introduce training AutoML Models using Vertex AI.
3 hours to complete
7 videos (Total 38 min), 1 reading, 2 quizzes
3 hours to complete
BigQuery Machine Learning: Develop ML Models Where Your Data Lives
In this module, we will introduce BigQuery ML and its capabilities.
3 hours to complete
8 videos (Total 30 min), 1 reading, 3 quizzes
1 hour to complete
Optimization
In this module we will walk you through how to optimize your ML models.
1 hour to complete
12 videos (Total 58 min), 1 reading, 1 quiz
1 hour 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.
1 hour to complete
5 videos (Total 28 min), 1 reading, 1 quiz
2 hours to complete
Summary
This module is a summary of the Launching into Machine Learning course
2 hours to complete
4 readings
Reviews
- 5 stars69.33%
- 4 stars23.79%
- 3 stars5%
- 2 stars1.22%
- 1 star0.64%
TOP REVIEWS FROM LAUNCHING INTO MACHINE LEARNING
by PTJun 25, 2018
Overall it was great, and very instructive. However, the Short History of ML was a little bit confusing with too many unexplained words and too many details too early.
by KHFeb 12, 2019
This course gave me a good overview of how to work with GCP for ML and also helped in covering a bit of knowledge gaps that I had when I learnt things on my own.
by AKAug 25, 2018
Very good course for beginners! -1 star because I find labs to be less informational and practical and course to be more theoretical that practical!
by AMJul 13, 2018
Great presenter. High energy engaging. The material is more difficult and to develop intuition of why the sampling needs to result in constant RMSE didn't come across.
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