Introduction to AI and Machine Learning on Google Cloud from Coursera

What's inside

Syllabus

Introduction

This module covers the course objective of helping learners navigate the AI development tools on Google Cloud. It also provides an overview of the course structure, which is based on a three-layer AI framework including AI foundations, development, and solutions.

AI Foundations

This module begins with a use case demonstrating the AI capabilities. It then focuses on the AI foundations including cloud infrastructure like compute and storage. It also explains the primary data and AI development products on Google Cloud. Finally, it demonstrates how to use BigQuery ML to build an ML model, which helps transition from data to AI.

AI Development Options

This module explores the various options for developing an ML project on Google Cloud, from ready-made solutions like pre-trained APIs, to no-code and low-code solutions like AutoML, and code-based solutions like custom training. It compares the advantages and disadvantages of each option to help decide the right development tools.

AI Development Workflow

This module walks through the ML workflow from data preparation, to model development, and to model serving on Vertex AI. It also illustrates how to convert the workflow into an automated pipeline using Vertex AI Pipelines.

Generative AI

This module introduces generative AI (gen AI), the newest advancement in AI, and the essential toolkits for developing gen AI projects. It starts by examining the gen AI workflow on Google Cloud. It then investigates how to use Gen AI Studio and Model Garden to access Gemini multimodal, design prompt, and tune models. Finally, it explores the built-in gen AI capabilities of AI solutions.

Summary

This module provides a summary of the entire course by covering the most important concepts, tools, technologies, and products.

Good to know

Know what's good

, what to watch for

, and possible dealbreakers

Builds a strong foundation in AI development on Google Cloud, which is standard in the industry

Taught by Google Cloud Training, who are recognized for their work in AI and machine learning

Develops skills in AI foundations, AI development, and AI solutions, which are core skills for data scientists, AI developers, and ML engineers

Examines generative AI, which is highly relevant to the latest advancements in AI

Explores the latest tools and technologies for AI development on Google Cloud

This course may require learners to have some prior knowledge of AI and machine learning

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 Introduction to AI and Machine Learning on Google Cloud with these activities:

Review the prerequisites for the course

Show steps

Refreshing your knowledge of the prerequisites will ensure that you have a solid foundation before starting the course.

Browse courses on Machine Learning

Show steps

Review your notes or textbooks from previous courses
Complete any refresher materials provided by the instructor

Organize and review your notes, assignments, and quizzes

Show steps

Organizing your materials will help you prepare for assessments and retain the information better.

Show steps

Gather all of your notes, assignments, and quizzes
Sort and organize the materials by topic
Review the materials regularly

Review The Hundred-Page Machine Learning Book

Show steps

This book provides a solid overview of foundational concepts, including algorithms, models, and use cases.

View Machine Learning Engineering on Amazon

Show steps

Read one chapter per week
Complete the end-of-chapter exercises
Summarize the key concepts in your own words

Five other activities

Expand to see all activities and additional details

Show all eight activities

Follow a TensorFlow tutorial

Show steps

TensorFlow is one of the most popular deep learning libraries. Completing a tutorial will familiarize you with its API and core concepts.

Browse courses on TensorFlow

Show steps

Choose a tutorial that aligns with your skill level and interests
Follow the tutorial step-by-step
Experiment with different hyperparameters and model architectures

Solve LeetCode problems on data structures and algorithms

Show steps

Solving LeetCode problems will strengthen your programming skills and problem-solving abilities.

Browse courses on Data Structures

Show steps

Start with easy problems and gradually increase the difficulty
Analyze the problem and come up with an efficient solution
Implement and test your solution

Write a blog post or article on a topic related to the course

Show steps

Writing a blog post or article will help you organize your thoughts, reinforce your understanding, and solidify your knowledge.

Browse courses on Machine Learning

Show steps

Choose a topic that you are passionate about and knowledgeable in
Research and gather information from credible sources
Outline your article and write a draft
Edit and proofread your article carefully
Publish your article on a relevant platform

Mentor a junior student in machine learning

Show steps

Mentoring others will help you solidify your understanding of the concepts and develop your communication skills.

Browse courses on Mentoring

Show steps

Identify a junior student who is interested in learning about machine learning
Establish a regular meeting schedule
Discuss concepts and provide guidance on projects
Provide feedback and encouragement

Build a machine learning model and deploy it to a cloud platform

Show steps

Building and deploying a model will give you hands-on experience with the entire machine learning workflow.

Browse courses on Model Development

Show steps

Choose a dataset and define the problem you want to solve
Train and evaluate different machine learning models
Deploy the best model to a cloud platform
Monitor and evaluate the performance of your model

Career center

Learners who complete Introduction to AI and Machine Learning on Google Cloud will develop knowledge and skills that may be useful to these careers:

AI Researcher

AI Researchers push the boundaries of AI and ML by developing new algorithms, techniques, and applications. Their work contributes to advancements in areas such as natural language processing, computer vision, and robotics. This course provides AI Researchers with a solid foundation in AI and ML, empowering them to conduct cutting-edge research and contribute to the field.

See salaries and explore the career path for AI Researcher

Machine Learning Engineer

Machine Learning Engineers utilize AI and ML to develop intelligent systems that learn from vast amounts of data. Whether it's building predictive models, automating processes, or creating tailored customer experiences, Machine Learning Engineers solve complex problems with data-driven solutions. This course provides a comprehensive overview of ML development on Google Cloud, empowering learners to excel in this in-demand field.

See salaries and explore the career path for Machine Learning Engineer

AI Developer

AI Developers possess a deep understanding of AI algorithms and technologies, enabling them to design and implement AI systems. They use AI to automate processes, create intelligent applications, and solve real-world problems. This course helps AI Developers build a strong foundation in AI and ML, preparing them for success in this rapidly growing field.

See salaries and explore the career path for AI Developer

AI Product Manager

AI Product Managers oversee the development and launch of AI-powered products and services. They work closely with engineers, designers, and business stakeholders to ensure that AI solutions meet market needs and deliver value. This course equips AI Product Managers with a deep understanding of AI and ML, enabling them to make informed decisions and effectively manage AI product development.

See salaries and explore the career path for AI Product Manager

Data Scientist

Data Scientists research and develop techniques for extracting actionable insights from large datasets. Organizations leverage Data Scientists' expertise to understand their customers, improve personalized marketing, maintain and innovate products, and much more. This course provides a solid foundation in essential AI and ML topics, setting learners up for success in this data-driven field.

See salaries and explore the career path for Data Scientist

Cloud Architect

Cloud Architects design, develop, and manage scalable, reliable, and secure cloud computing solutions. They collaborate with other stakeholders to optimize performance, ensure data security, and align cloud strategies with business goals. This course provides Cloud Architects with a deep understanding of AI and ML on Google Cloud, empowering them to effectively incorporate AI into cloud solutions.

See salaries and explore the career path for Cloud Architect

Data Engineer

Data Engineers design, build, and maintain data pipelines that collect, transform, and store large volumes of data. Their expertise enables organizations to derive valuable insights from their data. This course equips Data Engineers with a solid foundation in AI and ML, enhancing their ability to develop and manage efficient data pipelines for AI applications.

See salaries and explore the career path for Data Engineer

Business Analyst

Business Analysts help organizations understand their business needs and develop solutions to improve operations. They leverage data analysis and technology to identify areas for improvement, streamline processes, and drive data-informed decision-making. This course provides Business Analysts with a strong understanding of AI and ML, empowering them to make data-driven recommendations and contribute to strategic planning.

See salaries and explore the career path for Business Analyst

Data Analyst

Data Analysts collect, analyze, and interpret data to extract meaningful insights. They use statistical techniques and tools to identify trends, patterns, and anomalies in data. This course may be useful for Data Analysts who want to leverage AI and ML to automate and enhance their data analysis processes, enabling them to derive more value from data.

See salaries and explore the career path for Data Analyst

Software Engineer

Software Engineers design, develop, and maintain software applications. They work on a wide range of projects, from developing mobile apps to building enterprise systems. This course may be useful for Software Engineers who want to incorporate AI and ML into their software solutions, enhancing their capabilities and creating innovative applications.

See salaries and explore the career path for Software Engineer

Tech Lead

Tech Leads mentor and guide development teams, ensuring that projects are executed efficiently and effectively. Their technical expertise enables them to make informed decisions and resolve complex issues. This course may be useful for Tech Leads who want to enhance their understanding of AI and ML, enabling them to lead teams in developing innovative AI-powered solutions.

See salaries and explore the career path for Tech Lead

Product Owner

Product Owners define the vision and roadmap for software products. They work closely with development teams to ensure that products meet customer needs and deliver value. This course may be useful for Product Owners who want to incorporate AI and ML into their product strategies, enabling them to create innovative and competitive products.

See salaries and explore the career path for Product Owner

Data Architect

Data Architects design and manage data infrastructure and systems. They ensure that data is stored, processed, and accessed efficiently and securely. This course may be useful for Data Architects who want to leverage AI and ML to optimize data management and derive more value from data.

See salaries and explore the career path for Data Architect

Quantitative Analyst

Quantitative Analysts use mathematical and statistical models to analyze financial data and make investment recommendations. They play a crucial role in risk management, portfolio optimization, and trading strategies. This course may be useful for Quantitative Analysts who seek to incorporate AI and ML into their financial models and enhance their analytical capabilities.

See salaries and explore the career path for Quantitative Analyst

Project Manager

Project Managers plan, execute, and deliver projects within defined constraints of scope, time, and budget. They coordinate with stakeholders, manage resources, and ensure that projects meet business objectives. This course may be useful for Project Managers who want to incorporate AI and ML into their project plans and leverage these technologies to enhance project outcomes.

See salaries and explore the career path for Project Manager