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.
Launching into Machine Learning
Launching into Machine Learning
This course is part of multiple programs.
Instructor: Google Cloud Training
52,854 already enrolled
Included with 
Gain insight into a topic and learn the fundamentals.
4.6
(4,325 reviews)
Beginner level
No prior experience required
Flexible schedule
Approx. 14 hours
Learn at your own pace
90%
Most learners liked this course
What you'll 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'll gain
- Test Data
- Statistical Machine Learning
- Data Analysis
- Google Cloud Platform
- Regression Analysis
- Sampling (Statistics)
- Big Data
- Predictive Modeling
- Machine Learning Methods
- Data Quality
- Applied Machine Learning
- Machine Learning Algorithms
- Data Cleansing
- Exploratory Data Analysis
- Scikit Learn (Machine Learning Library)
- Machine Learning
- Supervised Learning
- Artificial Intelligence and Machine Learning (AI/ML)
- Data Processing
Details to know
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Assessments
6 assignments
Taught in English
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There are 8 modules in this course
This module provides an overview of the course and its objectives.
What's included
1 video
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.
What's included
9 videos1 reading1 assignment2 app items
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.
What's included
6 videos1 reading1 assignment1 app item
In this module, we will introduce training AutoML Models using Vertex AI.
What's included
5 videos1 reading1 assignment
In this module, we will introduce BigQuery ML and its capabilities.
What's included
7 videos1 reading1 assignment1 app item
In this module we will walk you through how to optimize your ML models.
What's included
12 videos1 reading1 assignment
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.
What's included
5 videos1 reading1 assignment
This module is a summary of the Launching into Machine Learning course
What's included
4 readings
Instructor
Instructor ratings
4.7 (282 ratings)Offered by
Recommended if you're interested in Machine Learning
Google Cloud
Google Cloud
Google Cloud
Google Cloud
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4.6
4,325 reviews
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Showing 3 of 4325
GS
4
Reviewed on Aug 18, 2018
Liked the way the datasplit using BigQuery is explained, but would appreciate if more references and links to explore BigQuery is provided at end of the video.
AM
4
Reviewed on Jul 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.
AR
4
Reviewed on Aug 16, 2019
I got a whole idea on how to work on data from scratch. Model selection, generalization, splitting of data and performance metric were few things I learned from this course.
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Frequently asked questions
Yes, you can preview the first video and view the syllabus before you enroll. You must purchase the course to access content not included in the preview.
If you decide to enroll in the course before the session start date, you will have access to all of the lecture videos and readings for the course. You’ll be able to submit assignments once the session starts.
Once you enroll and your session begins, you will have access to all videos and other resources, including reading items and the course discussion forum. You’ll be able to view and submit practice assessments, and complete required graded assignments to earn a grade and a Course Certificate.