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Launching into Machine Learning

Google Cloud Training

4.6

Based on 4,318 ratings

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Google Cloud Training

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.

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What's inside

Syllabus

Introduction

This module provides an overview of the course and its objectives.

Get to Know Your Data: Improve Data through Exploratory Data Analysis

Traffic lights

Read about what's good

what should give you pause

and possible dealbreakers

Suitable for learners who need general exposure to machine learning concepts

Explores data quality and exploratory data analysis, useful for data-driven decision-making

Covers topics in machine learning, model training, and optimization, providing a solid foundation for beginners

Taught by Google Cloud Training, recognized for its expertise in cloud computing and machine learning

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Reviews summary

Practical introduction to ml on google cloud

According to learners, this course serves as a positive and solid introduction to the field of machine learning, particularly emphasizing implementation on Google Cloud Platform. Students appreciate that it covers foundational concepts such as data quality and exploratory data analysis, providing a good overview of the ML lifecycle. A significant highlight for many is the practical component, featuring useful labs that offer hands-on experience with Google Cloud Platform tools like Vertex AI AutoML and BigQuery ML. While seen as a great starting point, reviewers note that the course is best suited for beginners and might lack depth for those with prior ML experience, focusing more on GCP tool usage than general theoretical concepts.

Strongly oriented towards Google Cloud Platform.

"Great if you plan to use Vertex AI and BigQuery ML."

"The course is heavily focused on GCP services, which is exactly what I needed."

"Wish there was more on general ML algorithms, not just GCP specific tools."

Hands-on labs reinforce learning effectively.

"The practical labs using Vertex AI were incredibly helpful."

"I appreciated the hands-on exercises with BigQuery ML."

"Doing the labs really solidified the concepts from the lectures."

Provides a clear, foundational understanding.

"A great starting point for absolute beginners in machine learning."

"The course really helped me grasp the basic ML lifecycle concepts."

"Explains core ideas like data quality and EDA simply and effectively."

Most beneficial for those new to machine learning.

"Too basic if you already have some ML experience."

"Good for a first step, but not enough depth for intermediate learners."

"If you know the basics, this course might move too slowly."

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 Launching into Machine Learning with these activities:

Review research methods

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Review topics in research methods to ensure a strong foundation for building models.

Browse courses on Research Design

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Read the course syllabus thoroughly and make note of the grading rubric and assignment due dates.
Review your notes, assignments, quizzes, and exams from previous courses in research methods.
Do practice questions to test your understanding of research methods.

Solve coding challenges

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Practice writing code to reinforce your understanding of machine learning concepts.

Browse courses on Coding

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Find a coding challenge website or platform that is appropriate for your skill level.
Start with easier challenges and gradually work your way up to more difficult ones.
Review your solutions and identify areas where you can improve your coding skills.

Follow online tutorials

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Follow online tutorials to supplement your understanding of machine learning concepts.

Browse courses on Machine Learning

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Find online tutorials that are relevant to the topics you are learning in the course.
Watch the tutorials and take notes on the key concepts.
Try out the examples and exercises provided in the tutorials.

Five other activities

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Join a study group

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Join a study group to collaborate with other students and enhance your learning.

Browse courses on Machine Learning

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Find a study group that meets regularly to discuss the course material.
Participate in group discussions and share your understanding of the concepts.
Work together on assignments and projects to reinforce your learning.

Volunteer at a machine learning organization

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Volunteer at a machine learning organization to gain experience and contribute to the field.

Browse courses on Machine Learning

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Find a machine learning organization that you are interested in volunteering for.
Contact the organization and inquire about volunteer opportunities.
Attend training and orientation sessions to learn about the organization's mission and goals.
Participate in volunteer activities, such as data collection, data analysis, or model development.
Share your expertise and knowledge with other volunteers and staff members.

Attend workshops

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Attend workshops on machine learning topics to gain hands-on experience and learn from experts.

Browse courses on Machine Learning

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Find workshops that are relevant to your interests and learning goals.
Register for the workshops and attend them regularly.
Participate in the activities and discussions during the workshops.

Write a blog post

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Write a blog post on a machine learning topic to enhance your understanding and communication skills.

Browse courses on Machine Learning

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Choose a topic that you are interested in and that you have learned about in the course.
Research the topic thoroughly and gather relevant information.
Write a clear and concise blog post that explains the topic in a way that is easy to understand.
Publish your blog post on a platform like Medium or your own website.

Build a machine learning project

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Build a machine learning project to apply your skills and gain practical experience.

Browse courses on Machine Learning

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Identify a problem that you want to solve using machine learning.
Gather and prepare the data that you will use to train your model.
Choose and train a machine learning model.
Evaluate the performance of your model and make adjustments as needed.
Deploy your model and monitor its performance.

Career center

Learners who complete Launching into Machine Learning will develop knowledge and skills that may be useful to these careers:

Machine Learning Engineer

Machine Learning Engineers are responsible for designing, developing, and maintaining machine learning models. They use their knowledge of machine learning algorithms and data analysis techniques to create models that can solve real-world problems. This course provides a strong foundation in the fundamentals of machine learning, including data analysis, model training, and model evaluation. It also covers some of the most popular machine learning algorithms, such as linear regression, logistic regression, and decision trees. This course can help you build the skills you need to become a successful Machine Learning Engineer.

See salaries and explore the career path for Machine Learning Engineer

Data Scientist

Data Scientists use their knowledge of statistics, machine learning, and data analysis to extract insights from data. They work with businesses to identify problems that can be solved with data-driven solutions. This course provides a strong foundation in the fundamentals of data analysis, including data cleaning, data exploration, and data visualization. It also covers some of the most popular machine learning algorithms, such as linear regression, logistic regression, and decision trees. This course can help you build the skills you need to become a successful Data Scientist.

See salaries and explore the career path for Data Scientist

Data Analyst

Data Analysts use their knowledge of data analysis techniques to extract insights from data. They work with businesses to identify problems that can be solved with data-driven solutions. This course provides a strong foundation in the fundamentals of data analysis, including data cleaning, data exploration, and data visualization. It also covers some of the most popular machine learning algorithms, such as linear regression, logistic regression, and decision trees. This course can help you build the skills you need to become a successful Data Analyst.

See salaries and explore the career path for Data Analyst

Software Engineer

Software Engineers design, develop, and maintain software applications. They use their knowledge of programming languages and software development methodologies to create software that meets the needs of users. This course provides a strong foundation in the fundamentals of software development, including object-oriented programming, data structures, and algorithms. It also covers some of the most popular programming languages, such as Python, Java, and C++. This course can help you build the skills you need to become a successful Software Engineer.

See salaries and explore the career path for Software Engineer

Web Developer

Web Developers design, develop, and maintain websites. They use their knowledge of HTML, CSS, and JavaScript to create websites that are visually appealing and easy to use. This course provides a strong foundation in the fundamentals of web development, including HTML, CSS, and JavaScript. It also covers some of the most popular web development frameworks, such as React, Angular, and Vue.js. This course can help you build the skills you need to become a successful Web Developer.

See salaries and explore the career path for Web Developer

Business Analyst

Business Analysts use their knowledge of business processes and data analysis techniques to identify problems and opportunities for improvement. They work with businesses to develop data-driven solutions that can help them achieve their goals. This course provides a strong foundation in the fundamentals of business analysis, including data analysis, process improvement, and project management. It also covers some of the most popular business analysis tools, such as Microsoft Excel, Power BI, and Tableau. This course can help you build the skills you need to become a successful Business Analyst.

See salaries and explore the career path for Business Analyst

Product Manager

Product Managers are responsible for the development and launch of new products. They work with engineers, designers, and marketers to create products that meet the needs of users. This course provides a strong foundation in the fundamentals of product management, including product development, marketing, and customer success. It also covers some of the most popular product management tools, such as Jira, Asana, and Trello. This course can help you build the skills you need to become a successful Product Manager.

See salaries and explore the career path for Product Manager

Marketing Manager

Marketing Managers are responsible for the development and execution of marketing campaigns. They work with sales, product, and engineering teams to create marketing campaigns that reach the right audience and achieve the desired results. This course provides a strong foundation in the fundamentals of marketing, including marketing strategy, customer segmentation, and campaign measurement. It also covers some of the most popular marketing tools, such as Google Analytics, HubSpot, and Salesforce. This course can help you build the skills you need to become a successful Marketing Manager.

See salaries and explore the career path for Marketing Manager

Sales Manager

Sales Managers are responsible for the development and execution of sales strategies. They work with sales teams to identify and close deals. This course provides a strong foundation in the fundamentals of sales, including sales strategy, customer relationship management, and negotiation. It also covers some of the most popular sales tools, such as Salesforce, HubSpot, and LinkedIn Sales Navigator. This course can help you build the skills you need to become a successful Sales Manager.

See salaries and explore the career path for Sales Manager

Operations Manager

Operations Managers are responsible for the day-to-day operations of an organization. They work with teams across the organization to ensure that operations are running smoothly and efficiently. This course provides a strong foundation in the fundamentals of operations management, including process improvement, project management, and supply chain management. It also covers some of the most popular operations management tools, such as Microsoft Excel, Power BI, and Tableau. This course can help you build the skills you need to become a successful Operations Manager.

See salaries and explore the career path for Operations Manager

Financial Analyst

Financial Analysts use their knowledge of financial data to make investment recommendations. They work with clients to develop investment portfolios that meet their financial goals. This course provides a strong foundation in the fundamentals of financial analysis, including financial statement analysis, valuation, and portfolio management. It also covers some of the most popular financial analysis tools, such as Bloomberg, Capital IQ, and FactSet. This course can help you build the skills you need to become a successful Financial Analyst.

See salaries and explore the career path for Financial Analyst

Actuary

Actuaries use their knowledge of mathematics and statistics to assess risk and uncertainty. They work with insurance companies, pension funds, and other financial institutions to develop products and services that help clients manage risk. This course provides a strong foundation in the fundamentals of actuarial science, including probability, statistics, and financial mathematics. It also covers some of the most popular actuarial science tools, such as R, SAS, and Excel. This course can help you build the skills you need to become a successful Actuary.

See salaries and explore the career path for Actuary

Statistician

Statisticians use their knowledge of statistics to collect, analyze, and interpret data. They work with businesses, governments, and other organizations to make informed decisions. This course provides a strong foundation in the fundamentals of statistics, including probability, inference, and regression. It also covers some of the most popular statistical software packages, such as R, SAS, and SPSS. This course can help you build the skills you need to become a successful Statistician.

See salaries and explore the career path for Statistician

Data Engineer

Data Engineers design, build, and maintain data pipelines. They work with data scientists and other data professionals to ensure that data is available in a timely and accurate manner. This course provides a strong foundation in the fundamentals of data engineering, including data modeling, data integration, and data quality. It also covers some of the most popular data engineering tools, such as Apache Hadoop, Apache Spark, and Apache Flink. This course can help you build the skills you need to become a successful Data Engineer.

See salaries and explore the career path for Data Engineer

Database Administrator

Database Administrators are responsible for the design, implementation, and maintenance of databases. They work with data engineers and other data professionals to ensure that databases are running smoothly and efficiently. This course provides a strong foundation in the fundamentals of database administration, including database design, database optimization, and database security. It also covers some of the most popular database management systems, such as MySQL, PostgreSQL, and Oracle Database. This course can help you build the skills you need to become a successful Database Administrator.

See salaries and explore the career path for Database Administrator

Reading list

We've selected 18 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 Launching into Machine Learning.