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Applying Machine Learning to your Data with Google Cloud

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.

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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.

Traffic lights

Read about what's good

what should give you pause

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

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Reviews summary

Applying ml to data on google cloud

According to learners, this course provides a solid introduction to applying machine learning concepts on Google Cloud, particularly emphasizing the use of BigQuery ML. Students found the explanation of basic ML terms and concepts to be clear, making it suitable for beginners or those new to cloud-based ML. Many highlighted the practical value gained from the course's focus on leveraging SQL within BigQuery for model creation and the overview of pre-trained ML APIs. While largely seen as a positive starting point, some learners with prior experience noted it might be too foundational and could benefit from more advanced topics.

Practical experience with Google Cloud tools.

"The interactive labs were essential for practicing with the tools like BigQuery ML and pretrained APIs."

"Doing the labs helped solidify the concepts taught in the lectures."

"I learned the most by actively participating in the hands-on exercises provided."

"Labs were useful for getting practical exposure to the Google Cloud environment for ML."

Excellent coverage of using BigQuery for ML tasks.

"The section on using BigQuery ML was incredibly useful and demonstrated a practical way to build models with just SQL."

"Learning how to train and evaluate ML models directly within BigQuery was a major takeaway for me."

"I really appreciated the focus on BigQuery ML; it feels very accessible."

"Understanding the BigQuery ML syntax and workflow is a valuable skill learned here."

Offers a clear foundation for ML on Google Cloud.

"This course gave me a solid foundation for understanding machine learning and how it applies to data on Google Cloud."

"Great introduction to the basics of ML and how to utilize Google Cloud tools."

"I found the introductory concepts well explained and easy to grasp as a beginner."

"Provides a really good starting point for anyone wanting to get into ML on GCP."

Background in SQL is helpful for BigQuery ML.

"Understanding SQL is definitely a prerequisite for getting the most out of the BigQuery ML modules."

"While they teach the ML part, being comfortable with SQL queries is important for the labs."

"Some sections assume familiarity with BigQuery and SQL syntax."

"Basic SQL knowledge is recommended for the BigQuery parts of the course."

May be too simple for those with ML experience.

"For someone with prior machine learning experience, this course might feel a bit too introductory."

"I was hoping for more depth on model tuning and advanced techniques; this is very surface level."

"Good for absolute beginners, but not challenging enough if you already know ML basics."

"Might be too basic if you have already taken several intro ML courses."

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

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Strengthen your understanding of linear algebra, a fundamental mathematical concept used in machine learning.

Browse courses on Linear Algebra

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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

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Organize and review a collection of resources related to machine learning to enhance your understanding and stay updated on the latest developments.

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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

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Reviewing sklearn will help you to refresh your knowledge of machine learning algorithms and techniques.

Browse courses on scikit-learn

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Go through the sklearn documentation
Do some practice exercises using sklearn

13 other activities

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Watch videos on YouTube about machine learning

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Expand your understanding of machine learning concepts by watching videos created by experts in the field.

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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

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Following a machine learning tutorial will help you to learn new concepts and techniques.

Browse courses on YouTube

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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

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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

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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

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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

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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

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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

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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

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Practice using the ML APIs and building models to solidify your understanding of ML concepts.

Browse courses on Machine Learning Model

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Follow the exercises and examples in the lab
Try creating your own models and datasets

Build a machine learning model

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Create a machine learning model to gain hands-on experience and reinforce your understanding of the concepts covered in the course.

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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

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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.

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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

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Solving practice problems on Kaggle will help you to improve your problem-solving skills.

Browse courses on Kaggle

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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

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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

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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

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Deepen your understanding by applying ML concepts to a real-world problem.

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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

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Writing a blog post about a machine learning project will help you to reflect on your work and share your knowledge with others.

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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

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Contributing to an open-source machine learning project will allow you to learn from others and give back to the community.

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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 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.

See salaries and explore the career path for Machine Learning Engineer

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.

See salaries and explore the career path for Data Scientist

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.

See salaries and explore the career path for Data 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.

See salaries and explore the career path for Business 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.

See salaries and explore the career path for Marketing Analyst

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.

See salaries and explore the career path for Software Engineer

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.

See salaries and explore the career path for Quantitative 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.

See salaries and explore the career path for Risk Analyst

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.

See salaries and explore the career path for Actuary

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.

See salaries and explore the career path for Biostatistician

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.

See salaries and explore the career path for Operations Research Analyst

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.

See salaries and explore the career path for Statistician

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.

See salaries and explore the career path for Financial Analyst

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.

See salaries and explore the career path for Economist

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.

See salaries and explore the career path for Market Researcher

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.