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Google Cloud Training
In this course, we define what machine learning is and how it can benefit your business. You'll see a few demos of ML in action and learn key ML terms like instances, features, and labels. In the interactive labs, you will practice invoking the pretrained ML APIs available as well as build your own Machine Learning models using just SQL with BigQuery ML.
Register for this course and see more details by visiting:
OpenCourser.com/course/4on88h/applying
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What's inside
Syllabus
Introduction
Overview of what you will learn in this course
Introduction to Machine Learning
In this module, we define what Machine Learning is and how it can benefit your business. You'll see a few demos of ML in action and learn key ML terms like instances, features, and labels.
Read more
Syllabus
Introduction
Overview of what you will learn in this course
Introduction to Machine Learning
In this module, we define what Machine Learning is and how it can benefit your business. You'll see a few demos of ML in action and learn key ML terms like instances, features, and labels.
Read more
Pre-trained ML APIs
In this module we will dive into pre-built and pre-trained ML models that we can access (like image recognition and sentiment analysis) within Cloud Datalab.
Creating ML Datasets in BigQuery
Understand how to create ML datasets with BigQuery.
Creating ML Models in BigQuery
In this module, you will learn how to create machine learning models directly inside of BigQuery. You will learn the new syntax and work through the phases of building, evaluating, and testing an ML model.
End of Course Recap
You've made it to the end! Let's review the lessons learned in the course and what resources are available for continued learning.
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Learners with interest in methods of machine learning will find this course informative
Learners who work in or want to move into the tech sector will benefit from taking this course
Individuals who wish to use the BigQuery ML feature will find the course helpful
Those already familiar with BigQuery will find this course easier to follow
Those with no background knowledge of machine learning may find some of the concepts difficult to understand
Those who plan to learn advanced ML theory will need to take additional courses
Save this course
Save Applying Machine Learning to your Data with Google Cloud to your list so you can find it easily later:
Save
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Reviews summary
Hands-on machine learning in google cloud
Learners say this course is well-structured and engaging with practical labs that provide hands-on experience. It offers a good introduction to Machine Learning (ML), covering key terms and important concepts. The instructor, Evans, is lauded for his excellent teaching skills and ability to simplify complex topics. Several students also mention the usefulness of the course for developing their skills in data analytics and ML. However, there are a few concerns raised about outdated or buggy labs, a lack of technical depth in some areas, and issues with Coursera's platform.
The course material is interesting and enjoyable to learn from.
"Nice Course"
"Great class!"
"Very good introduction to BigQuery!"
Concepts are presented in a simplified way.
"Really excellently taught course, with really engaging instruction from Evan, super high quality videos and labs and great introduction to ML using GCP"
"The instructor is very good at delivering the topic."
"Covers complex topics in simple, digestible ways"
Hands-on labs simulate real-time data analysis.
"Hands on labs provide an experience of real time analysis of data."
"The most interesting of the whole specialization. The labs are cool, but it's all copy & paste."
"Great tools and technology from Google have made this possible."
The course may not provide enough technical depth for some learners.
"Good but it is more intended to show capabilities than to teach properly"
"Overall, this course is easy to understand and practical. Not much technical jargon."
"I hope we have one more week to better analyze the parameter inside the ML function in Big Query."
Some labs may be outdated or buggy, leading to frustration.
"Some of the labs needs to be updated according to current features."
"It's a really great theoretical course, but the labs need to be bug-fixed and/or updated"
"Tive problemas no Laboratório - Training with Pre-built ML Models using Cloud Vision API and AutoML."
Activities
Be better prepared
before
your course. Deepen your understanding
during
and
after
it. Supplement your coursework and achieve mastery of the topics covered
in Applying Machine Learning to your Data with Google Cloud with these
activities:
Review linear algebra concepts
Show steps
Strengthen your understanding of linear algebra, a fundamental mathematical concept used in machine learning.
Browse courses on
Linear Algebra
Show steps
-
Review your notes or textbooks on linear algebra
-
Solve practice problems to reinforce your understanding
-
Take a refresher course or workshop on linear algebra
Compile a list of machine learning resources
Show steps
Organize and review a collection of resources related to machine learning to enhance your understanding and stay updated on the latest developments.
Show steps
-
Gather articles, videos, and tutorials on machine learning
-
Create a document or spreadsheet to organize the resources
-
Share the compilation with classmates or colleagues
Review sklearn
Show steps
Reviewing sklearn will help you to refresh your knowledge of machine learning algorithms and techniques.
Browse courses on
scikit-learn
Show steps
-
Go through the sklearn documentation
-
Do some practice exercises using sklearn
13 other activities
Expand to see all activities and additional details
Show all 16 activities
Watch videos on YouTube about machine learning
Show steps
Expand your understanding of machine learning concepts by watching videos created by experts in the field.
Show steps
-
Search for videos on YouTube related to the topics covered in the course
-
Watch the videos and take notes on key concepts
-
Summarize the main points of the videos
Follow a machine learning tutorial on YouTube or Coursera
Show steps
Following a machine learning tutorial will help you to learn new concepts and techniques.
Browse courses on
YouTube
Show steps
-
Find a machine learning tutorial that is relevant to your interests
-
Watch the tutorial and take notes
-
Try out the code examples in the tutorial
Attend a machine learning conference or meetup
Show steps
Attending a machine learning conference or meetup will allow you to connect with other machine learning professionals and learn about the latest trends.
Browse courses on
AI
Show steps
-
Find a machine learning conference or meetup that is relevant to your interests
-
Register for the event
-
Attend the event and participate in the activities
Solve coding challenges on LeetCode
Show steps
Practice solving coding challenges related to machine learning to improve your problem-solving skills and reinforce your understanding of the concepts covered in the course.
Browse courses on
Machine Learning Algorithms
Show steps
-
Choose a set of coding challenges that align with the topics covered in the course
-
Solve the challenges and review your solutions
-
Identify areas where you need additional practice
Join a study group or online forum for machine learning
Show steps
Joining a study group or online forum will allow you to connect with other machine learning learners and share knowledge.
Browse courses on
Machine Learning
Show steps
-
Find a study group or online forum that is relevant to your interests
-
Introduce yourself and ask questions
-
Participate in discussions and share your knowledge
Practice ML using Coursera Cloud Datalab
Show steps
Practice using the ML APIs and building models to solidify your understanding of ML concepts.
Browse courses on
Machine Learning Model
Show steps
-
Follow the exercises and examples in the lab
-
Try creating your own models and datasets
Build a machine learning model
Show steps
Create a machine learning model to gain hands-on experience and reinforce your understanding of the concepts covered in the course.
Show steps
-
Choose a dataset that aligns with your interests
-
Train a model using Google Cloud ML Engine
-
Evaluate the performance of your model
-
Deploy your model and make predictions
Complete the Google Cloud Machine Learning Quickstart
Show steps
Follow a guided tutorial to gain hands-on experience with Google Cloud Machine Learning and reinforce your understanding of the concepts covered in the course.
Show steps
-
Set up your Google Cloud Platform project
-
Create a dataset and import data
-
Train a machine learning model
-
Evaluate and deploy your model
Solve practice problems on Kaggle
Show steps
Solving practice problems on Kaggle will help you to improve your problem-solving skills.
Browse courses on
Kaggle
Show steps
-
Create an account on Kaggle
-
Find a practice problem that you are interested in
-
Submit a solution to the problem
Build a machine learning model for a real-world problem
Show steps
Building a machine learning model for a real-world problem will help you to apply your skills to a practical problem.
Browse courses on
Machine Learning Model
Show steps
-
Identify a real-world problem that you would like to solve
-
Collect data for your problem
-
Build a machine learning model to solve your problem
-
Evaluate the performance of your model
Build a Machine Learning Project
Show steps
Deepen your understanding by applying ML concepts to a real-world problem.
Show steps
-
Identify a problem that can be solved with ML
-
Gather data and prepare it for analysis
-
Build and train a machine learning model
-
Evaluate the model and make improvements
-
Deploy the model and monitor its performance
Write a blog post about a machine learning project that you have worked on
Show steps
Writing a blog post about a machine learning project will help you to reflect on your work and share your knowledge with others.
Show steps
-
Identify a machine learning project that you have worked on and that you would like to write about
-
Write about the problem that you were trying to solve
-
Describe the machine learning model that you built
-
Share your results and discuss what you learned
Contribute to an open-source machine learning project
Show steps
Contributing to an open-source machine learning project will allow you to learn from others and give back to the community.
Show steps
-
Find an open-source machine learning project that you are interested in
-
Identify an issue or feature that you would like to work on
-
Create a pull request with your changes
Review linear algebra concepts
Show steps
Strengthen your understanding of linear algebra, a fundamental mathematical concept used in machine learning.
Browse courses on
Linear Algebra
Show steps
Show steps
Show steps
- Review your notes or textbooks on linear algebra
- Solve practice problems to reinforce your understanding
- Take a refresher course or workshop on linear algebra
Compile a list of machine learning resources
Show steps
Organize and review a collection of resources related to machine learning to enhance your understanding and stay updated on the latest developments.
Show steps
Show steps
Show steps
- Gather articles, videos, and tutorials on machine learning
- Create a document or spreadsheet to organize the resources
- Share the compilation with classmates or colleagues
Review sklearn
Show steps
Reviewing sklearn will help you to refresh your knowledge of machine learning algorithms and techniques.
Browse courses on
scikit-learn
Show steps
Show steps
Show steps
- Go through the sklearn documentation
- Do some practice exercises using sklearn
13 other activities
Expand to see all activities and additional details
Show all 16 activities
Watch videos on YouTube about machine learning
Show steps
Expand your understanding of machine learning concepts by watching videos created by experts in the field.
Show steps
Show steps
Show steps
- Search for videos on YouTube related to the topics covered in the course
- Watch the videos and take notes on key concepts
- Summarize the main points of the videos
Follow a machine learning tutorial on YouTube or Coursera
Show steps
Following a machine learning tutorial will help you to learn new concepts and techniques.
Browse courses on
YouTube
Show steps
Show steps
Show steps
- Find a machine learning tutorial that is relevant to your interests
- Watch the tutorial and take notes
- Try out the code examples in the tutorial
Attend a machine learning conference or meetup
Show steps
Attending a machine learning conference or meetup will allow you to connect with other machine learning professionals and learn about the latest trends.
Browse courses on
AI
Show steps
Show steps
Show steps
- Find a machine learning conference or meetup that is relevant to your interests
- Register for the event
- Attend the event and participate in the activities
Solve coding challenges on LeetCode
Show steps
Practice solving coding challenges related to machine learning to improve your problem-solving skills and reinforce your understanding of the concepts covered in the course.
Browse courses on
Machine Learning Algorithms
Show steps
Show steps
Show steps
- Choose a set of coding challenges that align with the topics covered in the course
- Solve the challenges and review your solutions
- Identify areas where you need additional practice
Join a study group or online forum for machine learning
Show steps
Joining a study group or online forum will allow you to connect with other machine learning learners and share knowledge.
Browse courses on
Machine Learning
Show steps
Show steps
Show steps
- Find a study group or online forum that is relevant to your interests
- Introduce yourself and ask questions
- Participate in discussions and share your knowledge
Practice ML using Coursera Cloud Datalab
Show steps
Practice using the ML APIs and building models to solidify your understanding of ML concepts.
Browse courses on
Machine Learning Model
Show steps
Show steps
Show steps
- Follow the exercises and examples in the lab
- Try creating your own models and datasets
Build a machine learning model
Show steps
Create a machine learning model to gain hands-on experience and reinforce your understanding of the concepts covered in the course.
Show steps
Show steps
Show steps
- Choose a dataset that aligns with your interests
- Train a model using Google Cloud ML Engine
- Evaluate the performance of your model
- Deploy your model and make predictions
Complete the Google Cloud Machine Learning Quickstart
Show steps
Follow a guided tutorial to gain hands-on experience with Google Cloud Machine Learning and reinforce your understanding of the concepts covered in the course.
Show steps
Show steps
Show steps
- Set up your Google Cloud Platform project
- Create a dataset and import data
- Train a machine learning model
- Evaluate and deploy your model
Solve practice problems on Kaggle
Show steps
Solving practice problems on Kaggle will help you to improve your problem-solving skills.
Browse courses on
Kaggle
Show steps
Show steps
Show steps
- Create an account on Kaggle
- Find a practice problem that you are interested in
- Submit a solution to the problem
Build a machine learning model for a real-world problem
Show steps
Building a machine learning model for a real-world problem will help you to apply your skills to a practical problem.
Browse courses on
Machine Learning Model
Show steps
Show steps
Show steps
- Identify a real-world problem that you would like to solve
- Collect data for your problem
- Build a machine learning model to solve your problem
- Evaluate the performance of your model
Build a Machine Learning Project
Show steps
Deepen your understanding by applying ML concepts to a real-world problem.
Show steps
Show steps
Show steps
- Identify a problem that can be solved with ML
- Gather data and prepare it for analysis
- Build and train a machine learning model
- Evaluate the model and make improvements
- Deploy the model and monitor its performance
Write a blog post about a machine learning project that you have worked on
Show steps
Writing a blog post about a machine learning project will help you to reflect on your work and share your knowledge with others.
Show steps
Show steps
Show steps
- Identify a machine learning project that you have worked on and that you would like to write about
- Write about the problem that you were trying to solve
- Describe the machine learning model that you built
- Share your results and discuss what you learned
Contribute to an open-source machine learning project
Show steps
Contributing to an open-source machine learning project will allow you to learn from others and give back to the community.
Show steps
Show steps
Show steps
- Find an open-source machine learning project that you are interested in
- Identify an issue or feature that you would like to work on
- Create a pull request with your changes
Career center
Learners who complete Applying Machine Learning to your Data with Google Cloud will develop knowledge and skills
that may be useful to these careers:
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers apply machine learning algorithms to find patterns in data. They develop machine learning models that can predict future outcomes. This course will help you build a foundation in machine learning concepts and techniques. You will learn how to create and evaluate machine learning models using Google Cloud.
Data Scientist
Data Scientist
Data Scientists use machine learning and other statistical techniques to extract insights from data. They develop models that can predict future outcomes or identify trends. This course will help you build a foundation in machine learning concepts and techniques. You will learn how to create and evaluate machine learning models using Google Cloud.
Data Analyst
Data Analyst
Data Analysts collect, clean, and analyze data. They use their findings to make recommendations to businesses. This course will help you build a foundation in machine learning concepts and techniques. You will learn how to create and evaluate machine learning models using Google Cloud. This knowledge can help you identify trends and make better recommendations.
Business Analyst
Business Analyst
Business Analysts use data to identify and solve business problems. They develop recommendations for improving business processes. This course will help you build a foundation in machine learning concepts and techniques. You will learn how to create and evaluate machine learning models using Google Cloud. This knowledge can help you make better recommendations and improve business outcomes.
Marketing Analyst
Marketing Analyst
Marketing Analysts use data to understand customer behavior and trends. They develop marketing campaigns that are targeted to specific audiences. This course will help you build a foundation in machine learning concepts and techniques. You will learn how to create and evaluate machine learning models using Google Cloud. This knowledge can help you identify customer trends and develop more effective marketing campaigns.
Software Engineer
Software Engineer
Software Engineers design, develop, and maintain software systems. They use machine learning to improve the performance and efficiency of their software. This course will help you build a foundation in machine learning concepts and techniques. You will learn how to create and evaluate machine learning models using Google Cloud. This knowledge can help you develop better software systems.
Quantitative Analyst
Quantitative Analyst
Quantitative Analysts use machine learning to analyze financial data. They develop models that can predict future stock prices or economic trends. This course will help you build a foundation in machine learning concepts and techniques. You will learn how to create and evaluate machine learning models using Google Cloud. This knowledge can help you make better investment decisions.
Actuary
Actuary
Actuaries use machine learning to analyze insurance data. They develop models that can predict the likelihood of claims or other financial losses. This course will help you build a foundation in machine learning concepts and techniques. You will learn how to create and evaluate machine learning models using Google Cloud. This knowledge can help you make better insurance decisions.
Risk Analyst
Risk Analyst
Risk Analysts use machine learning to identify and mitigate risks. They develop models that can predict the likelihood of fraud or other financial losses. This course will help you build a foundation in machine learning concepts and techniques. You will learn how to create and evaluate machine learning models using Google Cloud. This knowledge can help you identify and mitigate risks more effectively.
Statistician
Statistician
Statisticians use machine learning to analyze data and draw conclusions. They develop models that can predict future outcomes or identify trends. This course will help you build a foundation in machine learning concepts and techniques. You will learn how to create and evaluate machine learning models using Google Cloud. This knowledge can help you make better decisions.
Operations Research Analyst
Operations Research Analyst
Operations Research Analysts use machine learning to improve the efficiency of business operations. They develop models that can optimize production schedules or supply chains. This course will help you build a foundation in machine learning concepts and techniques. You will learn how to create and evaluate machine learning models using Google Cloud. This knowledge can help you make better business decisions.
Biostatistician
Biostatistician
Biostatisticians use machine learning to analyze medical data. They develop models that can predict the likelihood of disease or other health outcomes. This course will help you build a foundation in machine learning concepts and techniques. You will learn how to create and evaluate machine learning models using Google Cloud. This knowledge can help you make better medical decisions.
Market Researcher
Market Researcher
Market Researchers use machine learning to analyze market data. They develop models that can predict customer behavior or trends. This course will help you build a foundation in machine learning concepts and techniques. You will learn how to create and evaluate machine learning models using Google Cloud. This knowledge can help you make better marketing decisions.
Financial Analyst
Financial Analyst
Financial Analysts use machine learning to analyze financial data. They develop models that can predict future stock prices or economic trends. This course will help you build a foundation in machine learning concepts and techniques. You will learn how to create and evaluate machine learning models using Google Cloud. This knowledge can help you make better investment decisions.
Economist
Economist
Economists use machine learning to analyze economic data. They develop models that can predict future economic trends. This course will help you build a foundation in machine learning concepts and techniques. You will learn how to create and evaluate machine learning models using Google Cloud. This knowledge can help you make better economic decisions.
For more career information including salaries, visit:
OpenCourser.com/course/4on88h/applying
Reading list
We've selected six books
that we think will supplement your
learning. Use these to
develop background knowledge, enrich your coursework, and gain a
deeper understanding of the topics covered in
Applying Machine Learning to your Data with Google Cloud.
Covers machine learning with Python, providing a practical guide to implementing ML models using Python code.
Provides a practical guide to machine learning using popular Python libraries such as Scikit-Learn, Keras, and TensorFlow.
Covers deep learning using R, providing a practical guide to implementing deep learning models in R.
Focuses on the business applications of data science, providing insights into how machine learning can be used to solve business problems.
Save
Provides a German-language introduction to machine learning, making it accessible to learners who prefer to read in their native language.
Provides a non-technical introduction to machine learning, making it accessible to beginners.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/4on88h/applying
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Effort
1 week of study, 5-7 hours/week
Level
Introductory
Via
Coursera
Institution
Google Cloud
Instructor
Google Cloud Training
Language
English
This course belongs to a
collection
called
From Data to Insights with Google Cloud. It's comprised of five courses:
- Géopolitique de l'Europe
- Applying Machine Learning to your Data with Google Cloud (viewing)
- Achieving Advanced Insights with BigQuery
- Creating New BigQuery Datasets and Visualizing Insights
- Exploring and Preparing your Data with BigQuery
Good to know
Learners with interest in methods of machine learning will find this course informative
Learners who work in or want to move into the tech sector will benefit from taking this course
Individuals who wish to use the BigQuery ML feature will find the course helpful
Those already familiar with BigQuery will find this course easier to follow
Those with no background knowledge of machine learning may find some of the concepts difficult to understand
Those who plan to learn advanced ML theory will need to take additional courses
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