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Google Cloud
Incorporating machine learning into data pipelines increases the ability to extract insights from data. This course covers ways machine learning can be included in data pipelines on Google Cloud. For little to no customization, this course covers AutoML. For more tailored machine learning capabilities, this course introduces Notebooks and BigQuery machine learning (BigQuery ML). Also, this course covers how to productionalize machine learning solutions by using Vertex AI.
Read more
Incorporating machine learning into data pipelines increases the ability to extract insights from data. This course covers ways machine learning can be included in data pipelines on Google Cloud. For little to no customization, this course covers AutoML. For more tailored machine learning capabilities, this course introduces Notebooks and BigQuery machine learning (BigQuery ML). Also, this course covers how to productionalize machine learning solutions by using Vertex AI.
Read more
Incorporating machine learning into data pipelines increases the ability to extract insights from data. This course covers ways machine learning can be included in data pipelines on Google Cloud. For little to no customization, this course covers AutoML. For more tailored machine learning capabilities, this course introduces Notebooks and BigQuery machine learning (BigQuery ML). Also, this course covers how to productionalize machine learning solutions by using Vertex AI.
Incorporating machine learning into data pipelines increases the ability to extract insights from data. This course covers ways machine learning can be included in data pipelines on Google Cloud. For little to no customization, this course covers AutoML. For more tailored machine learning capabilities, this course introduces Notebooks and BigQuery machine learning (BigQuery ML). Also, this course covers how to productionalize machine learning solutions by using Vertex AI.
Register for this course and see more details by visiting:
OpenCourser.com/course/tkhtpu/smart
What's inside
Syllabus
Introduction
Introduction to Analytics and AI
Prebuilt ML Model APIs for Unstructured Data
Big Data Analytics with Notebooks
Read more
Syllabus
Introduction
Introduction to Analytics and AI
Prebuilt ML Model APIs for Unstructured Data
Big Data Analytics with Notebooks
Read more
Production ML Pipelines
Custom Model Building with SQL in BigQuery ML
Custom Model Building with AutoML
Summary
Course Resources
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Reinforces basic skills and knowledge
Covers standard skills used in the field of analytics
Instructors for this course are Google Cloud
Helps learners understand analytics and AI
Provides overview of tools from Google Cloud
Assumes some familiarity
Save this course
Save Smart Analytics, Machine Learning, and AI on Google Cloud to your list so you can find it easily later:
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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 Smart Analytics, Machine Learning, and AI on Google Cloud with these
activities:
Watch tutorials on Intro to Machine Learning
Show steps
This activity will enhance your understanding of the fundamentals of machine learning, which will help you better follow along with the course material.
Browse courses on
Machine Learning
Show steps
-
Search for tutorials on YouTube or Coursera.
-
Watch at least 3 tutorials.
-
Take notes on the key concepts.
Join a study group or online forum for machine learning
Show steps
This activity will provide you with opportunities to connect with other learners, discuss course material, and receive support from peers, which can enhance your overall learning experience.
Browse courses on
Machine Learning
Show steps
-
Search for study groups or online forums related to machine learning.
-
Join a group that aligns with your interests and learning style.
-
Participate in discussions and ask questions.
Practice building and training machine learning models
Show steps
This activity will refine your practical skills in building and training machine learning models, which will be essential for completing the course assignments and projects.
Browse courses on
Machine Learning Models
Show steps
-
Find a dataset that interests you.
-
Choose a machine learning algorithm.
-
Build and train a model using the algorithm.
-
Evaluate the model's performance.
Four other activities
Expand to see all activities and additional details
Show all seven activities
Develop a machine learning application
Show steps
This activity will provide you with hands-on experience in applying machine learning to solve real-world problems, which is the ultimate goal of this course.
Browse courses on
Machine Learning Applications
Show steps
-
Identify a problem that can be solved using machine learning.
-
Collect and prepare a dataset.
-
Choose and train a machine learning model.
-
Deploy the model and evaluate its performance.
Contribute to an open-source machine learning project
Show steps
This activity will expose you to real-world machine learning applications and development practices, and it will also give you an opportunity to contribute to the broader machine learning community.
Browse courses on
Machine Learning
Show steps
-
Find an open-source machine learning project that interests you.
-
Identify a way to contribute to the project.
-
Submit a pull request with your contribution.
Create a blog post or presentation on a machine learning topic
Show steps
This activity will allow you to synthesize your understanding of machine learning and communicate it effectively, which will benefit not only your own learning but also that of others.
Browse courses on
Machine Learning
Show steps
-
Choose a topic that you are interested in or knowledgeable about.
-
Research the topic thoroughly.
-
Write a blog post or create a presentation that explains the topic clearly and concisely.
-
Share your blog post or presentation with others.
Mentor other students who are struggling with machine learning concepts
Show steps
This activity will strengthen your understanding of machine learning concepts by requiring you to explain them to others, and it will also allow you to help others succeed in their learning journey.
Browse courses on
Machine Learning
Show steps
-
Identify students who are struggling with machine learning concepts.
-
Offer your help and support.
-
Explain concepts in a clear and concise way.
-
Provide encouragement and motivation.
Watch tutorials on Intro to Machine Learning
Show steps
This activity will enhance your understanding of the fundamentals of machine learning, which will help you better follow along with the course material.
Browse courses on
Machine Learning
Show steps
Show steps
Show steps
- Search for tutorials on YouTube or Coursera.
- Watch at least 3 tutorials.
- Take notes on the key concepts.
Join a study group or online forum for machine learning
Show steps
This activity will provide you with opportunities to connect with other learners, discuss course material, and receive support from peers, which can enhance your overall learning experience.
Browse courses on
Machine Learning
Show steps
Show steps
Show steps
- Search for study groups or online forums related to machine learning.
- Join a group that aligns with your interests and learning style.
- Participate in discussions and ask questions.
Practice building and training machine learning models
Show steps
This activity will refine your practical skills in building and training machine learning models, which will be essential for completing the course assignments and projects.
Browse courses on
Machine Learning Models
Show steps
Show steps
Show steps
- Find a dataset that interests you.
- Choose a machine learning algorithm.
- Build and train a model using the algorithm.
- Evaluate the model's performance.
Four other activities
Expand to see all activities and additional details
Show all seven activities
Develop a machine learning application
Show steps
This activity will provide you with hands-on experience in applying machine learning to solve real-world problems, which is the ultimate goal of this course.
Browse courses on
Machine Learning Applications
Show steps
Show steps
Show steps
- Identify a problem that can be solved using machine learning.
- Collect and prepare a dataset.
- Choose and train a machine learning model.
- Deploy the model and evaluate its performance.
Contribute to an open-source machine learning project
Show steps
This activity will expose you to real-world machine learning applications and development practices, and it will also give you an opportunity to contribute to the broader machine learning community.
Browse courses on
Machine Learning
Show steps
Show steps
Show steps
- Find an open-source machine learning project that interests you.
- Identify a way to contribute to the project.
- Submit a pull request with your contribution.
Create a blog post or presentation on a machine learning topic
Show steps
This activity will allow you to synthesize your understanding of machine learning and communicate it effectively, which will benefit not only your own learning but also that of others.
Browse courses on
Machine Learning
Show steps
Show steps
Show steps
- Choose a topic that you are interested in or knowledgeable about.
- Research the topic thoroughly.
- Write a blog post or create a presentation that explains the topic clearly and concisely.
- Share your blog post or presentation with others.
Mentor other students who are struggling with machine learning concepts
Show steps
This activity will strengthen your understanding of machine learning concepts by requiring you to explain them to others, and it will also allow you to help others succeed in their learning journey.
Browse courses on
Machine Learning
Show steps
Show steps
Show steps
- Identify students who are struggling with machine learning concepts.
- Offer your help and support.
- Explain concepts in a clear and concise way.
- Provide encouragement and motivation.
Career center
Learners who complete Smart Analytics, Machine Learning, and AI on Google Cloud will develop knowledge and skills
that may be useful to these careers:
Data Analyst
Data Analyst
Data Analysts mine structured and unstructured data for actionable insights to help organizations make better decisions. They use the data gathered to make predictions and identify trends that can help organizations predict customer behavior. This course can help foster the skills you need to identify, collect, process, and analyze large datasets, making you a more valuable candidate in this field.
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers design, build, and maintain machine learning models. They use their expertise in machine learning algorithms and software engineering to create models that can learn from data and make predictions. This course can help you build the skills you need to design and implement machine learning models, making you a more competitive candidate for this role.
Data Scientist
Data Scientist
Data Scientists use models and algorithms to extract insights from data that can help make predictions about the future. They use machine learning, statistics, and other forms of advanced data analysis to help organizations leverage data more effectively. This course can help you learn the fundamentals of machine learning and how to apply it in practical situations, making you a more competitive candidate in this field. While this course does not cover data science in its entirety, it can help you build a foundation or add to your credentials.
Model Builder
Model Builder
Model Builders construct mathematical or statistical models to help businesses and organizations analyze performance and forecast future trends. This course can teach you the basics of model building, including how to collect data, prepare it for analysis, and build and evaluate models.
Statistician
Statistician
Statisticians use statistical methods to collect, analyze, and interpret data. They work in a variety of industries, including finance, healthcare, and marketing. This course can teach you the fundamentals of statistics, which can help you succeed in this role.
Data Architect
Data Architect
Data Architects design and manage data systems. They work with stakeholders to understand business needs, and then design and implement data systems that meet those needs. This course can teach you the fundamentals of data architecture, which can help you succeed in this role.
Operations Research Analyst
Operations Research Analyst
Operations Research Analysts use mathematical and analytical methods to help organizations make better decisions. They work with stakeholders to identify business needs, and then use data to develop solutions that meet those needs. This course can teach you the fundamentals of operations research, which can help you succeed in this role.
Risk Analyst
Risk Analyst
Risk Analysts use data and analytical tools to help organizations identify and manage risks. They work with stakeholders to identify risks, and then use data to develop strategies to mitigate those risks. This course can teach you the fundamentals of risk analysis, which can help you succeed in this role.
Data Engineer
Data Engineer
Data Engineers build and maintain the infrastructure that processes and stores data. They use their expertise in data management and architecture to ensure that data is available, reliable, and secure. This course can help teach you how to create and manage data pipelines, which is an essential skill for Data Engineers.
Quantitative Analyst
Quantitative Analyst
Quantitative Analysts use mathematical and statistical methods to analyze financial data. They work with stakeholders to identify investment opportunities, and then use data to develop models that can help make investment decisions. This course can teach you the fundamentals of quantitative analysis, which can help you succeed in this role.
Actuary
Actuary
Actuaries use mathematical and statistical methods to assess and manage financial risks. They work with stakeholders to identify financial risks, and then use data to develop models that can help manage those risks. This course can teach you the fundamentals of actuarial science, which can help you succeed in this role.
Credit Analyst
Credit Analyst
Credit Analysts use data and analytical tools to help organizations make lending decisions. They work with stakeholders to identify credit risks, and then use data to develop models that can help make lending decisions. This course can teach you the fundamentals of credit analysis, which can help you succeed in this role.
Insurance Analyst
Insurance Analyst
Insurance Analysts use data and analytical tools to help organizations assess and manage insurance risks. They work with stakeholders to identify insurance risks, and then use data to develop models that can help manage those risks. This course can teach you the fundamentals of insurance analysis, which can help you succeed in this role.
Fraud Analyst
Fraud Analyst
Fraud Analysts use data and analytical tools to help organizations identify and prevent fraud. They work with stakeholders to identify fraud risks, and then use data to develop strategies to mitigate those risks. This course can teach you the fundamentals of fraud analysis, which can help you succeed in this role.
Business Analyst
Business Analyst
Business Analysts use data and analytical tools to help organizations make better decisions. They work with stakeholders to identify business needs, and then use data to develop solutions that meet those needs. This course can teach you the fundamentals of data analysis, which can help you succeed in this role.
For more career information including salaries, visit:
OpenCourser.com/course/tkhtpu/smart
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
Smart Analytics, Machine Learning, and AI on Google Cloud.
Starts with a solid foundation in the fundamentals of machine learning before moving on to more advanced topics like deep learning and text processing. It's a great resource for anyone who wants to go deeper into machine learning, especially if you're interested in using scikit-learn, Keras, or TensorFlow.
Hands-on guide to machine learning, with lots of practical examples and code snippets. It's a great resource for anyone who wants to get started with machine learning quickly and easily.
Practical guide to machine learning using Python. It covers a wide range of topics, from data preprocessing to model evaluation. It's a great resource for anyone who wants to learn how to use Python for machine learning.
Uses engaging visuals to explain the concepts of deep learning in a clear and concise way. It's a great resource for anyone who wants to learn about deep learning without getting bogged down in the math.
Save
This comprehensive book covers a wide range of data mining topics, from data cleaning and preprocessing to machine learning and data visualization. It's a great resource for anyone who wants to learn about data mining in depth.
Aimed at people with no programming experience, this book teaches the basics of Python programming in a fun and engaging way. It's a great resource for anyone who wants to learn how to automate tasks and write simple Python scripts.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/tkhtpu/smart
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Time
75 hours
Level
Intermediate
Via
Pluralsight
Instructor
Google Cloud
Language
English
Good to know
Reinforces basic skills and knowledge
Covers standard skills used in the field of analytics
Instructors for this course are Google Cloud
Helps learners understand analytics and AI
Provides overview of tools from Google Cloud
Assumes some familiarity
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