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Launching into Machine Learning

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

In this module, we look at how to improve the quality of our data and how to explore our data by performing exploratory data analysis. We look at the importance of tidy data in Machine Learning and show how it impacts data quality. For example, missing values can skew our results. You will also learn the importance of exploring your data. Once we have the data tidy, you will then perform exploratory data analysis on the dataset.

Machine Learning in Practice

In this module, we will introduce some of the main types of machine learning so that you can accelerate your growth as an ML practitioner.

Training AutoML Models Using Vertex AI

In this module, we will introduce training AutoML Models using Vertex AI.

BigQuery Machine Learning: Develop ML Models Where Your Data Lives

In this module, we will introduce BigQuery ML and its capabilities.

Optimization

In this module we will walk you through how to optimize your ML models.

Generalization and Sampling

Now it’s time to answer a rather weird question: when is the most accurate ML model not the right one to pick? As we hinted at in the last module on Optimization -- simply because a model has a loss metric of 0 for your training dataset does not mean it will perform well on new data in the real world. You will learn how to create repeatable training, evaluation, and test datasets and establish performance benchmarks.

Summary

This module is a summary of the Launching into Machine Learning course

Good to know

Know what's good

, what to watch for

, 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

Launch into machine learning fundamentals

Learners say this course is a largely positively received resource for learning the fundamentals of machine learning with Google Cloud Platform. Cloud SQL, Vertex AI, and TensorFlow are among the topics covered. Students remark that the labs help drive concepts home, but also mention that access to course datasets and resources can occasionally be a challenge. Overall, this introductory course is well-regarded for the quality and accessibility of its content.

Labs help reinforce learning.

"The Lab and the Assignment helps to understand the Machine Learning."

"Excellent content - actually explained now NNs function with internal features sets. great labs to experiment"

"Great hands-on exercises with Tensor Flow Big-Query and Jupyter Labs"

Content is accessible to those with limited experience.

"It was an amazing experience. The instructors were too good and the quality of lectures and resources is way more better."

"Just stick with it! The course is easy to follow, and the labs get you into tweaking ML related code without having to know the underlying math. LOVE IT!"

"Very thorough review of the historical development of Neural Network."

Content is generally well received.

"Amazing course. For a beginner like me, it was a shot in the arm. Excellent presentation very lively and engaging."

"Excellent course to understand how machine learning is done , classification, regression, RMSE, cross entropy, gradient descent, loss functions, performance scores, splitting data into training, validation and test data sets consistently/repeatedly using mod and hash functions, exploring, cleaning data."

"This course good for learner that's want to gain more in depth knowledge about machine learning, but still in the shallow part."

Resources can occasionally be inaccessible.

"Cannot access resources after completing the course"

"In the labs, I kept getting disconnected from the Jupyter notebooks, and had to keep reloading them."

Datasets can be hard to access.

"The links to Big Query datasets need to be updated since they changed on Oct 1 2020"

"Someone really needs to proofread the quiz questions, and fix the links to datasets in the lab."

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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Show all eight activities

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

Show steps

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.