Managing Machine Learning Projects with Google Cloud from Coursera

What's inside

Syllabus

Module 1: Introduction

Welcome to the course! In this module, you'll meet the instructor and learn about the course content and how to get started.

Module 2: Identifying business value for using ML

This module begins by defining machine learning at a high level and then helps you gain a thorough understanding of its value for business by reviewing several real-world examples. It then introduces machine learning projects and provides practice using a tool to assess the feasibility of several ML problems.

Module 3: Defining ML as a practice

Module 4: Building and evaluating ML models

After you have assessed the feasibility of your supervised ML problem, you're ready to move to the next phase of an ML project. This module explores the various considerations and requirements for building a complete dataset in preparation for training, evaluating, and deploying an ML model. It also includes two demos—Vision API and AutoML Vision—as relevant tools that you can easily access yourself or in partnership with a data scientist. You'll also have the opportunity to try out AutoML Vision with the first hands-on lab.

Module 5: Using ML responsibly and ethically

Data in the world is inherently biased, and that bias can be amplified through ML solutions. In this module, you'll learn about some of the most common biases and how they can disproportionately affect or harm an individual or groups of individuals. You'll also be given guidelines for uncovering possible biases at each phase of an ML project and strategies for achieving ML fairness as much as possible.

Module 6: Discovering ML use cases in day-to-day business

This module explores 5 general themes for discovering ML use cases within day-to-day business, followed by concrete customer examples. You'll learn about creative applications of ML, such as improving the resolution of images or generating music.

Module 7: Managing ML projects successfully

When you thoroughly understand the fundamentals of machine learning and considerations within in each phase of the project, you're ready to learn about the best practices for managing an ML project. This module describes 5 key considerations for successfully managing an ML project end-to-end: identifying the business value, developing a data strategy, establishing data governance, building successful ML teams, and enabling a culture of innovation. You'll also have an opportunity to gain further exposure to one of Google Cloud's tools by completing a final hands-on lab: Evaluate an ML Model with BigQuery ML.

Module 8: Summary

This module provides a summary of the key points covered in each of the modules in the course.

Good to know

Know what's good

, what to watch for

, and possible dealbreakers

Explores real-world applications of ML, making it relevant to business professionals

Teaches how to identify feasible ML projects and evaluate their business impact

Develops business acumen in ML, enabling professionals to lead and influence ML projects

Introduces industry-standard tools and practices, such as Google Cloud's Vision API and AutoML Vision

Highlights ethical considerations in ML, fostering a responsible approach to its use

Provides hands-on labs to reinforce key concepts and practical skills

Reviews summary

Ml for business pros: insights

learners say this course gives a solid introduction to machine learning (ML) from a business perspective. Coursework explains how ML works, why it is important, and how to implement ML projects with Google Cloud Platform tools. According to students, the course is well-paced and engaging. Hands-on labs help non-technical participants understand and practice the concepts. However, some assessments may be confusing to complete, and some labs may not run smoothly. Overall, students say this is a great resource for business professionals who wish to better understand ML and its potential applications.

The course does not focus on a specific industry, making it relevant to business professionals from various backgrounds.

"The course provided an insightful introduction to machine learning."

"It was inspiring to recognize the importance of domain expertise in reaping maximum benefit from ML models."

ML has a wide range of applications across various industries, and it can be used to improve business outcomes.

"The explanations were very clear."

"Great course, highly recommended."

The course is well-paced and engaging, with clear explanations and relevant examples.

"This course is really thoughtful for any data engineer who wants to learn about ML,"

"This course has really given me a better insight into where it is appropriate to apply machine learning and where it is not appropriate to do so."

Ethical concerns and data privacy are critical considerations when deploying ML models.

"Provides hands-on training on some ML tools."

"The course presents various use cases for ML across industries and helps you think in terms of how it can be applied in your business."

Google Cloud Platform (GCP) offers many tools businesses can use to implement ML.

"The course content is very well presented."

"The lab exercises are a big bonus, giving us a flair of how simple using GCP could be."

"This course serves as a very good introductory course into the ML world. Kudos to Google!"

Interactive labs provide learners with practical experience using GCP tools to implement ML.

"The course lives up to the expectations it sets - machine learning for beginners."

"Leaves me craving for more --- would probably enroll in another course about ML now."

This course is designed for business professionals with little to no prior knowledge of ML.

"I was working with ML."

"This course showed me that an ML project could be more complex than one would think it is, and also more simple in other aspects like the execution of it."

ML uses data to create predictions. A step-by-step process aids in implementing an ML model.

"This greatly helps people understand the basics of Machine Learning."

"The course has a good time. the demos are incredible."

"All the course is excellent (from videos, trainers to business cases, examples)."

The course materials are accessible to non-technical learners, and no coding experience is required.

"This is a nice introduction to Google's Cloud Platform ML basic functionality."

"Very good introduction of ML For non developers, I guess it’s the only course around helping people understand what ML is and how it can be used in business without throwing you Into a loop of insane amount of math."

Some assessments may be confusing or unclear.

"The final module about building team is interesting to me, but I don't recommend other ambiguous/ethical/philosophical topics for non-natives like me."

"Even after starting the course after 15 days, every assessment asks for payment."

Some labs may experience technical difficulties or unclear instructions.

"The Qwiklabs graded assignments used in this course has numerous bugs that can use up your attempts at performing the assignments."

"The Qwiklabs set up used can also make it difficult to pass for people with certain disabilities."

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 Managing Machine Learning Projects with Google Cloud with these activities:

Compile a collection of resources related to machine learning for future reference

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Stay organized and efficient by gathering relevant materials such as lecture notes, articles, tutorials, and datasets in one easily accessible location.

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Create a dedicated folder or digital notebook to store your resources
Systematically gather and categorize materials based on topics or themes
Use descriptive naming conventions for easy retrieval later on

Review fundamentals of machine learning and data science basics

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Solidify your foundational knowledge of machine learning and data science principles, which will support topics in this course such as data analysis and model building.

Browse courses on Machine Learning

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Revisit core concepts of machine learning algorithms, such as supervised and unsupervised learning.
Refresh your understanding of data manipulation and preprocessing techniques, such as data cleaning, feature engineering, and normalization.
Review statistical concepts such as probability, hypothesis testing, and regression analysis.

Follow Google's ML Crash Course

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Solidify the basics of ML and get an overview of the most common ML algorithms and techniques.

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Go to https://developers.google.com/machine-learning/crash-course
Complete all the tutorials in the crash course
Take the quiz at the end of each tutorial to test your understanding

Nine other activities

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Refresh knowledge of machine learning fundamentals

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Review the basic concepts of ML before starting the course to strengthen your understanding throughout the rest of the course.

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Review the course syllabus and identify the topics that cover ML fundamentals.
Read through your notes or study materials from previous courses or resources on ML fundamentals.
Complete practice questions or exercises related to ML fundamentals.

Explore online tutorials or workshops to supplement your learning

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Enrich your knowledge by accessing a wealth of online tutorials and workshops tailored to different aspects of machine learning, broadening your understanding.

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Identify areas where you want to enhance your knowledge or skills
Search for reputable online tutorials or workshops that align with your learning objectives
Follow the tutorials step-by-step, taking notes and experimenting with the provided code examples

Explore tutorials on specific ML use cases

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Seek out and follow tutorials that demonstrate real-world applications of ML to gain a deeper understanding of its capabilities and limitations.

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Identify specific business problems or industries that interest you.
Search for tutorials or articles that showcase ML solutions for these problems or industries.
Follow the tutorials, paying attention to the implementation details and the results achieved.

Create an ML use case proposal

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Start thinking about how you can use ML to solve real-world problems by creating a proposal for an ML use case.

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Identify a business problem that can be solved with ML
Research different ML techniques and algorithms
Develop a plan for implementing the ML solution
Create a presentation or document to pitch your ML use case proposal

Practice applying machine learning algorithms to real-world datasets

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Supplement your theoretical knowledge by actively working through real-world examples of machine learning algorithm implementation.

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Choose a machine learning algorithm to practice (e.g. regression, classification, clustering)
Find a suitable dataset for your chosen algorithm
Implement the algorithm using a programming language of your choice
Evaluate the performance of your model on the dataset
Tweak the algorithm's parameters and observe the impact on performance

Build an ML model with AutoML

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Gain hands-on experience with ML model building using Google's AutoML platform.

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Go to https://cloud.google.com/automl
Create an AutoML account
Choose a dataset to train your model on
Select the type of ML model you want to build
Train your model
Evaluate the performance of your model
Deploy your model

Participate in a study group or discussion forum to exchange knowledge and insights

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Engage with peers to strengthen your understanding and gain diverse perspectives on machine learning topics and applications.

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Join or create a study group consisting of fellow students enrolled in this course
Participate actively in group discussions, sharing ideas and asking questions to deepen your understanding
Attend online or in-person discussion forums to engage with a broader community of machine learning enthusiasts and practitioners

Write a blog post or article to share your insights on a machine learning topic

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Solidify your understanding by articulating your learning in written form, fostering critical thinking and effective communication.

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Choose a specific machine learning topic that you are knowledgeable about
Conduct research to gather relevant information and insights
Organize your thoughts and structure your content logically
Write a draft, paying attention to clarity, conciseness, and accuracy
Proofread and edit your work carefully before publishing

Build a machine learning model to solve a specific business problem

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Apply your learning by creating a complete machine learning solution that addresses a genuine business challenge, such as customer churn prediction or fraud detection.

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Define the business problem and the desired outcome of your model
Collect and prepare the necessary data for your model
Select and train an appropriate machine learning algorithm
Evaluate and refine your model's performance based on metrics relevant to the business problem
Create a report or presentation to communicate your findings and insights

Career center

Learners who complete Managing Machine Learning Projects with Google Cloud will develop knowledge and skills that may be useful to these careers:

Data Scientist

Data Scientists analyze data with a variety of tools from statistics and machine learning to uncover insights and make predictions.

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Data Analyst

Data Analysts collect, analyze, interpret, and present data. This course can help Data Analysts develop a foundational understanding of machine learning, which can be leveraged to identify patterns and trends in data, and build predictive models. This course also provides insights into the ethical considerations when utilizing ML.

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Operations Research Analyst

Operations Research Analysts use mathematical and analytical techniques to solve complex business problems. This course may be helpful for an Operations Research Analyst who wants to learn more about the applications of machine learning in operations research. For example, Module 7, which covers managing ML projects successfully, may be helpful for managing ML projects in an operations research context.

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Data Architect

Data Architects design and implement data management solutions. This course can provide a foundational understanding of machine learning and its applications, which can be leveraged to design and implement data management solutions that support ML initiatives. For example, knowledge from Module 7 about best practices for managing ML projects may be helpful.

See salaries and explore the career path for Data Architect

AI Engineer

Artificial Intelligence Engineers research, design, and develop artificial intelligence systems. This course will provide a foundational understanding of the concepts and challenges involved in building machine learning applications.

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Machine Learning Engineer

Machine Learning Engineers design, develop, deploy, and maintain machine learning systems. This course may be useful for someone pursuing this career path, as it introduces the basics of machine learning, and covers the considerations for building a dataset, evaluating a model, and managing an ML project, which are important tasks for an ML engineer.

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Quantitative Analyst

Quantitative Analysts use mathematical and statistical modeling to analyze and predict financial markets. This course may be helpful for a Quantitative Analyst who wants to learn more about the applications of machine learning in finance. For example, Module 4 covers building and evaluating ML models, which is a key skill for developing trading strategies.

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Risk Analyst

Risk Analysts identify, assess, and mitigate risks. This course may be helpful for a Risk Analyst who wants to learn more about the applications of machine learning in risk management. For example, Module 5 covers using ML responsibly and ethically, which is critical for building and deploying ML models that are fair and unbiased.

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Business Analyst

Business Analysts identify and define business needs and recommend solutions that leverage technology. This course would allow a Business Analyst to become familiar with machine learning, which is increasingly being used to solve business problems. This may be helpful for identifying opportunities for the application of ML, as well as for understanding the capabilities and limitations of ML when evaluating solutions.

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Market Research Analyst

Market Research Analysts collect and analyze data to understand customer needs and trends. This course may be helpful for a Market Research Analyst who wants to learn more about the applications of machine learning in market research. For example, Module 3, which covers defining ML as a practice, may be helpful for understanding how to use ML to collect and analyze data.

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Product Manager

Product Managers are responsible for defining the vision, roadmap, and feature set of a product. This course provides Product Managers with an understanding of the business value and considerations within each phase of an ML project, which would allow them to make more informed decisions about how to utilize ML in their products.

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Business Consultant

Business Consultants provide advice and guidance to businesses on how to improve their operations. This course may be useful for a Business Consultant who wants to better understand the potential applications of machine learning and how it can be used to solve business problems.

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UX Designer

UX Designers create user interfaces and experiences for websites and applications. This course may be helpful for a UX Designer who is interested in incorporating machine learning into their work. For example, knowledge from Module 4 about evaluating ML models can help ensure that the ML features are user-friendly and effective.

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Software Engineer

Software Engineers apply their knowledge of computers and mathematics to develop and implement software. This course may be helpful for somebody in this role who wants to become involved in developing machine learning applications, but is not planning to become a dedicated ML Engineer.

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Data Engineer

Data Engineers work on designing, installing, maintaining, and managing big data systems. This course teaches some of the core concepts of machine learning that may be helpful for a Data Engineer who has to work with ML algorithms. For example, insights about bias in ML models from Module 5 may be helpful for building and monitoring a data pipeline that serves ML models.

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