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Anna Koop
This course is for professionals who have heard the buzz around machine learning and want to apply machine learning to data analysis and automation. Whether finance, medicine, engineering, business or other domains, this course will introduce you to problem definition and data preparation in a machine learning project.
Read more
This course is for professionals who have heard the buzz around machine learning and want to apply machine learning to data analysis and automation. Whether finance, medicine, engineering, business or other domains, this course will introduce you to problem definition and data preparation in a machine learning project.
Read more
This course is for professionals who have heard the buzz around machine learning and want to apply machine learning to data analysis and automation. Whether finance, medicine, engineering, business or other domains, this course will introduce you to problem definition and data preparation in a machine learning project.
By the end of the course, you will be able to clearly define a machine learning problem using two approaches. You will learn to survey available data resources and identify potential ML applications. You will learn to take a business need and turn it into a machine learning application. You will prepare data for effective machine learning applications.
This is the first course of the Applied Machine Learning Specialization brought to you by Coursera and the Alberta Machine Intelligence Institute.
Register for this course and see more details by visiting:
OpenCourser.com/course/hd9vxr/introduction
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What's inside
Syllabus
Introduction to Machine Learning Applications
This week, you will learn about what machine learning (ML) actually is, contrast different problem scenarios, and explore some common misconceptions about ML. You will apply this knowledge by identifying different components essential to a machine learning business solution.
Read more
Syllabus
Introduction to Machine Learning Applications
This week, you will learn about what machine learning (ML) actually is, contrast different problem scenarios, and explore some common misconceptions about ML. You will apply this knowledge by identifying different components essential to a machine learning business solution.
Read more
Machine Learning in the Real World
This week, you will learn how to translate a business need into a machine learning problem. We'll walk through some applied examples so you can get a feel for what makes a well-defined question for your QuAM. Narrowing down your question and making sure you have the data necessary to learn is critical to ML success!
Learning Data
This week is all about data. You will learn about data acquisition and understand the various sources of training data. We'll talk about how much data you need and what pitfalls might arise, including ethical issues.
Machine Learning Projects
This week you will learn about the Machine Learning Process Lifecycle (MLPL). After understanding the definitions and components of the MLPL you will analyze the application of the MLPL on a case study.
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Explores machine learning applications, which is standard in many industries
Teaches data acquisition and preparation, which are foundational skills for machine learning projects
Presents a case study, which helps learners apply the Machine Learning Process Lifecycle (MLPL)
Introduces learners to machine learning problem definition, which is a key skill for successful machine learning projects
Provides practical examples for translating business needs into machine learning projects, which is valuable for learners in any industry
Taught by Anna Koop, who are recognized for their expertise in machine learning
Save this course
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Reviews summary
Well-received introduction to applied machine learning
According to students, this introductory course on applied machine learning is largely positive and well-received. Learners say the instructor is exceptional and explains complex information in an understandable way, with many reviewers praising her teaching style. The course is praised for its focus on both the business and technical sides of ML. Learners will not find in-depth technical details or coding instruction in this course. Instead, it provides a good introduction to key concepts and terminology that is relevant to beginners and business professionals. The course is well structured and the lectures are clear and easy to follow.
Learners often say this course provided a great introduction to basic machine learning concepts.
"Really a great course to learn about the machine learning using real-world examples"
"Really a useful course for starters to know about basic terminology and concepts in ML which are more realistic to real-world problems."
"An excellent introduction to the mechanics of ML. Not so deep that coding is required, but simultaneously not so high-level as to be abstract. A very nice intro - thanks for this!"
Learners say the course includes engaging assignments and examples that are relevant to the real world.
"It is an excellent approach to how machine learning should be applied to real life!!"
"The course is well structured and clearly taught. The instructor Anna Koop seems to be a very informed expert in the field and a good educator."
"The course is well structured and clearly taught."
The instructor is frequently praised for her clear and engaging teaching style.
"The lectures are very clear and easy to follow. More importantly, it gives me a big picture of how Machine Learning can be applied to the real-world business."
"This is a course that gives a much-needed overview that is rarely mentioned in other resources. Ana is a very cool teacher..."
"one of the best ways to get started in machine learning is by this course."
This course is criticized for its lack of coding and practice exercises.
"I love the way you presented. It is better if we get hands on project to apply. Thank you so much for your time and efforts. "
"Good! The course has a very simple content, easy to understand and well structured, it can be completed very quickly and not in the 4 weeks proposed. "
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 Applied Machine Learning with these
activities:
Organize and review your notes, assignments, quizzes, and exams
Show steps
Organizing and reviewing your notes, assignments, quizzes, and exams will help you retain the information you have learned and improve your overall understanding of the course material.
Show steps
-
Gather your notes, assignments, quizzes, and exams
-
Organize your materials by topic
-
Review your materials regularly
-
Make notes and annotations
-
Create a study guide
Find a mentor who can provide guidance on your machine learning career
Show steps
Finding a mentor who can provide guidance on your machine learning career will help you accelerate your progress and achieve your goals.
Show steps
-
Identify your goals and objectives
-
Research potential mentors
-
Reach out to potential mentors
-
Establish a mentoring relationship
-
Meet with your mentor regularly
Review Introduction to Machine Learning 4th Edition
Show steps
Reviewing this book will help you refresh your understanding of the fundamentals of machine learning, which will be essential for success in this course.
View
Machine Learning, revised and updated edition
on Amazon
Show steps
-
Read the preface and introduction
-
Review the chapters on supervised learning
-
Review the chapters on unsupervised learning
-
Review the chapters on ensemble methods
-
Review the chapters on deep learning
Five other activities
Expand to see all activities and additional details
Show all eight activities
Practice data preprocessing and feature engineering
Show steps
Practicing data preprocessing and feature engineering will help you develop the skills necessary to prepare data for machine learning models.
Browse courses on
Data Preprocessing
Show steps
-
Find a dataset to work with
-
Clean the data
-
Preprocess the data
-
Engineer features
-
Train a machine learning model
Volunteer at a local AI organization
Show steps
Volunteering at a local AI organization will provide you with hands-on experience and help you network with other professionals in the field.
Show steps
-
Find a local AI organization
-
Contact the organization and inquire about volunteer opportunities
-
Attend volunteer orientation
-
Complete volunteer training
-
Participate in volunteer activities
Create a presentation on a machine learning topic
Show steps
Creating a presentation on a machine learning topic will help you deepen your understanding of the topic and improve your communication skills.
Show steps
-
Choose a topic
-
Research the topic
-
Create an outline
-
Write the presentation
-
Practice the presentation
Mentor a junior machine learning engineer
Show steps
Mentoring a junior machine learning engineer will help you solidify your own understanding of the field and contribute to the development of the next generation of engineers.
Show steps
-
Find a junior machine learning engineer to mentor
-
Establish a mentoring relationship
-
Meet with your mentee regularly
-
Provide guidance and support
-
Help your mentee set and achieve goals
Contribute to an open-source machine learning project
Show steps
Contributing to an open-source machine learning project will help you learn about the latest developments in the field and contribute to the community.
Show steps
-
Find an open-source machine learning project to contribute to
-
Read the project documentation
-
Identify an area where you can contribute
-
Make a contribution
-
Submit a pull request
Organize and review your notes, assignments, quizzes, and exams
Show steps
Organizing and reviewing your notes, assignments, quizzes, and exams will help you retain the information you have learned and improve your overall understanding of the course material.
Show steps
Show steps
Show steps
- Gather your notes, assignments, quizzes, and exams
- Organize your materials by topic
- Review your materials regularly
- Make notes and annotations
- Create a study guide
Find a mentor who can provide guidance on your machine learning career
Show steps
Finding a mentor who can provide guidance on your machine learning career will help you accelerate your progress and achieve your goals.
Show steps
Show steps
Show steps
- Identify your goals and objectives
- Research potential mentors
- Reach out to potential mentors
- Establish a mentoring relationship
- Meet with your mentor regularly
Review Introduction to Machine Learning 4th Edition
Show steps
Reviewing this book will help you refresh your understanding of the fundamentals of machine learning, which will be essential for success in this course.
View
Machine Learning, revised and updated edition
on Amazon
Show steps
Show steps
Show steps
- Read the preface and introduction
- Review the chapters on supervised learning
- Review the chapters on unsupervised learning
- Review the chapters on ensemble methods
- Review the chapters on deep learning
Five other activities
Expand to see all activities and additional details
Show all eight activities
Practice data preprocessing and feature engineering
Show steps
Practicing data preprocessing and feature engineering will help you develop the skills necessary to prepare data for machine learning models.
Browse courses on
Data Preprocessing
Show steps
Show steps
Show steps
- Find a dataset to work with
- Clean the data
- Preprocess the data
- Engineer features
- Train a machine learning model
Volunteer at a local AI organization
Show steps
Volunteering at a local AI organization will provide you with hands-on experience and help you network with other professionals in the field.
Show steps
Show steps
Show steps
- Find a local AI organization
- Contact the organization and inquire about volunteer opportunities
- Attend volunteer orientation
- Complete volunteer training
- Participate in volunteer activities
Create a presentation on a machine learning topic
Show steps
Creating a presentation on a machine learning topic will help you deepen your understanding of the topic and improve your communication skills.
Show steps
Show steps
Show steps
- Choose a topic
- Research the topic
- Create an outline
- Write the presentation
- Practice the presentation
Mentor a junior machine learning engineer
Show steps
Mentoring a junior machine learning engineer will help you solidify your own understanding of the field and contribute to the development of the next generation of engineers.
Show steps
Show steps
Show steps
- Find a junior machine learning engineer to mentor
- Establish a mentoring relationship
- Meet with your mentee regularly
- Provide guidance and support
- Help your mentee set and achieve goals
Contribute to an open-source machine learning project
Show steps
Contributing to an open-source machine learning project will help you learn about the latest developments in the field and contribute to the community.
Show steps
Show steps
Show steps
- Find an open-source machine learning project to contribute to
- Read the project documentation
- Identify an area where you can contribute
- Make a contribution
- Submit a pull request
Career center
Learners who complete Introduction to Applied Machine Learning will develop knowledge and skills
that may be useful to these careers:
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers design, develop, and deploy machine learning models for a variety of applications. They typically have a strong understanding of machine learning algorithms, as well as the software engineering skills necessary to implement and deploy these models. Taking Introduction to Applied Machine Learning can help Machine Learning Engineers to gain a deeper understanding of machine learning theory and how to apply it to real-world problems. This can help them to become more effective in their roles and to develop more innovative and effective machine learning solutions.
Artificial Intelligence Engineer
Artificial Intelligence Engineer
Artificial Intelligence Engineers design, develop, and deploy artificial intelligence systems. They typically have a strong understanding of machine learning, computer science, and mathematics, allowing them to develop and implement intelligent systems that can solve complex problems. Taking Introduction to Applied Machine Learning can help Artificial Intelligence Engineers to gain a deeper understanding of machine learning algorithms and how to use them to develop intelligent systems. This can help them to become more effective in their roles and to develop more innovative and effective artificial intelligence solutions.
Data Scientist
Data Scientist
Data Scientists use data to help businesses make better decisions. They typically have a strong understanding of machine learning, statistics, and computer science, allowing them to collect, clean, analyze, and visualize data to identify trends and patterns. Taking Introduction to Applied Machine Learning can help Data Scientists to gain a deeper understanding of machine learning algorithms and how to use them to analyze data. This can help them to become more effective in their roles and to provide more valuable insights to their businesses.
Operations Research Analyst
Operations Research Analyst
Operations Research Analysts develop and apply analytical techniques to help businesses make better decisions. They typically have a strong understanding of mathematics, statistics, and computer science, allowing them to analyze data and identify solutions to complex problems. Taking Introduction to Applied Machine Learning can help Operations Research Analysts to gain a deeper understanding of machine learning algorithms and how to use them to solve complex problems. This can help them to become more effective in their roles and to provide more valuable insights to their businesses.
Data Analyst
Data Analyst
Data Analysts collect, clean, and analyze data to help businesses make better decisions. They typically have a strong understanding of statistics and machine learning, allowing them to identify trends and patterns that can help businesses make better decisions. Taking Introduction to Applied Machine Learning can help Data Analysts to gain a deeper understanding of machine learning algorithms and how to use them to analyze data. This can help them to become more effective in their roles and to provide more valuable insights to their businesses.
Statistician
Statistician
Statisticians work with data to help businesses and organizations make better decisions. They typically have a strong understanding of probability and statistics, allowing them to analyze data and identify trends and patterns. Taking Introduction to Applied Machine Learning can help Statisticians to gain a deeper understanding of machine learning algorithms and how to use them to analyze data. This can help them to become more effective in their roles and to provide more valuable insights to their organizations.
Business Intelligence Analyst
Business Intelligence Analyst
Business Intelligence Analysts analyze data to help businesses make better decisions. They typically have a strong understanding of business intelligence tools and techniques, as well as the skills necessary to collect, clean, and analyze data. Taking Introduction to Applied Machine Learning can help Business Intelligence Analysts to gain a deeper understanding of machine learning algorithms and how to use them to analyze data. This can help them to become more effective in their roles and to provide more valuable insights to their businesses.
Computer Scientist
Computer Scientist
Computer Scientists research and develop new computing technologies. They typically have a strong understanding of computer science, as well as the skills necessary to design and implement new computing solutions. Taking Introduction to Applied Machine Learning can help Computer Scientists to gain a deeper understanding of machine learning algorithms and how to use them to develop new computing technologies. This can help them to become more effective in their roles and to contribute to the development of new and innovative computing solutions.
Product Manager
Product Manager
Product Managers are responsible for the development and launch of new products. They typically have a strong understanding of product management methodologies, as well as the skills necessary to market and sell new products. Taking Introduction to Applied Machine Learning can help Product Managers to gain a deeper understanding of machine learning algorithms and how to use them to develop new products. This can help them to become more effective in their roles and to develop more innovative and successful products.
Financial Analyst
Financial Analyst
Financial Analysts analyze financial data to help businesses make better decisions. They typically have a strong understanding of financial analysis tools and techniques, as well as the skills necessary to collect, clean, and analyze financial data. Taking Introduction to Applied Machine Learning can help Financial Analysts to gain a deeper understanding of machine learning algorithms and how to use them to analyze financial data. This can help them to become more effective in their roles and to provide more valuable insights to their businesses.
Software Engineer
Software Engineer
Software Engineers design, develop, and deploy software applications. They typically have a strong understanding of computer science, as well as the skills necessary to implement and deploy software solutions. Taking Introduction to Applied Machine Learning can help Software Engineers to gain a deeper understanding of machine learning algorithms and how to use them to develop innovative software solutions. This can help them to become more effective in their roles and to develop more valuable products for their businesses.
Business Analyst
Business Analyst
Business Analysts look at an organization's business processes and help to improve them. They typically have a strong understanding of data and technology, allowing them to make better decisions that can lead to improved efficiency and productivity within the business. Taking Introduction to Applied Machine Learning can help Business Analysts to gain a deeper understanding of machine learning techniques and how to apply them to business problems. This can help them to make more informed decisions about how to use data and technology to improve business processes.
Sales Manager
Sales Manager
Sales Managers are responsible for the development and execution of sales strategies. They typically have a strong understanding of sales principles and techniques, as well as the skills necessary to develop and execute effective sales strategies. Taking Introduction to Applied Machine Learning can help Sales Managers to gain a deeper understanding of machine learning algorithms and how to use them to target and engage customers. This can help them to become more effective in their roles and to develop more successful sales strategies.
Marketing Manager
Marketing Manager
Marketing Managers are responsible for the development and execution of marketing campaigns. They typically have a strong understanding of marketing principles and techniques, as well as the skills necessary to develop and execute effective marketing campaigns. Taking Introduction to Applied Machine Learning can help Marketing Managers to gain a deeper understanding of machine learning algorithms and how to use them to target and engage customers. This can help them to become more effective in their roles and to develop more successful marketing campaigns.
Management Consultant
Management Consultant
Management Consultants help businesses improve their performance. They typically have a strong understanding of business principles and techniques, as well as the skills necessary to analyze businesses and develop and implement improvement plans. Taking Introduction to Applied Machine Learning can help Management Consultants to gain a deeper understanding of machine learning algorithms and how to use them to analyze businesses and develop improvement plans. This can help them to become more effective in their roles and to help their clients achieve their business goals.
For more career information including salaries, visit:
OpenCourser.com/course/hd9vxr/introduction
Reading list
We've selected 12 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
Introduction to Applied Machine Learning.
Provides a practical guide to machine learning using Python. It valuable resource for anyone looking to apply machine learning techniques to real-world problems.
Provides a comprehensive overview of machine learning concepts and algorithms. It valuable resource for anyone looking to gain a deeper understanding of the field.
Provides a probabilistic perspective on machine learning. It valuable resource for anyone looking to gain a deeper understanding of the field.
Save
Provides a comprehensive overview of deep learning. It valuable resource for anyone looking to gain a deeper understanding of the field.
Save
Provides a comprehensive overview of pattern recognition and machine learning. It valuable resource for anyone looking to gain a deeper understanding of the field.
Provides an algorithmic perspective on machine learning. It valuable resource for anyone looking to gain a deeper understanding of the field.
Provides a practical guide to machine learning. It valuable resource for anyone looking to apply machine learning techniques to real-world problems.
Save
Provides a comprehensive overview of machine learning. It valuable resource for anyone looking to gain a deeper understanding of the field.
Provides a case study approach to machine learning. It valuable resource for anyone looking to gain a deeper understanding of the field.
Provides a cookbook of machine learning recipes in Python. It valuable resource for anyone looking to apply machine learning techniques to real-world problems.
Provides a comprehensive overview of machine learning for finance. It valuable resource for anyone looking to apply machine learning techniques to financial data.
Provides a comprehensive overview of machine learning for signal processing. It valuable resource for anyone looking to apply machine learning techniques to signal processing data.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/hd9vxr/introduction
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Level
Intermediate
Via
Coursera
Institution
Alberta Machine Intelligence Institute
Instructor
Anna Koop
Language
English
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This course belongs to a
collection
called
Machine Learning: Algorithms in the Real World. It's comprised of four courses:
- Data for Machine Learning
- Machine Learning Algorithms: Supervised Learning Tip to Tail
- Introduction to Applied Machine Learning (viewing)
- Optimizing Machine Learning Performance
Good to know
Explores machine learning applications, which is standard in many industries
Teaches data acquisition and preparation, which are foundational skills for machine learning projects
Presents a case study, which helps learners apply the Machine Learning Process Lifecycle (MLPL)
Introduces learners to machine learning problem definition, which is a key skill for successful machine learning projects
Provides practical examples for translating business needs into machine learning projects, which is valuable for learners in any industry
Taught by Anna Koop, who are recognized for their expertise in machine learning
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