We may earn an affiliate commission when you visit our partners.
Anna Koop
This course is all about data and how it is critical to the success of your applied machine learning model. Completing this course will give learners the skills to:
Understand the critical elements of data in the learning, training and operation phases
Understand biases and sources of data
Implement techniques to improve the generality of your model
Explain the consequences of overfitting and identify mitigation measures
Implement appropriate test and validation measures.
Demonstrate how the accuracy of your model can be improved with thoughtful feature engineering.
Read more
This course is all about data and how it is critical to the success of your applied machine learning model. Completing this course will give learners the skills to:
Understand the critical elements of data in the learning, training and operation phases
Understand biases and sources of data
Implement techniques to improve the generality of your model
Explain the consequences of overfitting and identify mitigation measures
Implement appropriate test and validation measures.
Demonstrate how the accuracy of your model can be improved with thoughtful feature engineering.
Read more
This course is all about data and how it is critical to the success of your applied machine learning model. Completing this course will give learners the skills to:
Understand the critical elements of data in the learning, training and operation phases
Understand biases and sources of data
Implement techniques to improve the generality of your model
Explain the consequences of overfitting and identify mitigation measures
Implement appropriate test and validation measures.
Demonstrate how the accuracy of your model can be improved with thoughtful feature engineering.
Explore the impact of the algorithm parameters on model strength
To be successful in this course, you should have at least beginner-level background in Python programming (e.g., be able to read and code trace existing code, be comfortable with conditionals, loops, variables, lists, dictionaries and arrays). You should have a basic understanding of linear algebra (vector notation) and statistics (probability distributions and mean/median/mode).
This is the third 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/28pobz/data
What's inside
Syllabus
What Does Good Data look like?
We all know that data is important for machine learning success, but what does it really look like? What steps do you need to take to get from scattered, unprocessed data to nice clean learning data? This week takes an overarching view to describe how your problem and data needs interact, and what processes need to be in place for successful data preparation.
Read more
Syllabus
What Does Good Data look like?
We all know that data is important for machine learning success, but what does it really look like? What steps do you need to take to get from scattered, unprocessed data to nice clean learning data? This week takes an overarching view to describe how your problem and data needs interact, and what processes need to be in place for successful data preparation.
Read more
Preparing your Data for Machine Learning Success
Now that you have your data sources identified, you need to bring it all together. This week describes what you need to prepare data overall.
Feature Engineering for MORE Fun & Profit
Data is particular to a problem. This week we'll discuss how to turn generic data into successful fuel for specific machine learning projects.
Bad Data
There are so many ways data can go wrong! This week discussed some of the pitfalls in data identification and processing.
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Introduces learners to the significance of data preparation for machine learning projects
Guides learners in transitioning raw data into structured, model-ready data
Develops critical skills in feature engineering, which enhance model performance
Emphasizes the importance of data quality control and error detection for successful machine learning outcomes
Lays the groundwork for effective model building and evaluation
Save this course
Save Data for Machine Learning to your list so you can find it easily later:
Save
Save
Reviews summary
Data for machine learning: well-received course
Learners say this is a well-received course that provides good programming assignments. The theory and content are described as great, but the course does have some technical challenges. Especially noteworthy are the extensive notebook assignments requiring Jupyter Notebook.
The course has good topics.
"deeply theoretical but excellent assignment file (good review for pandas library )"
"covers all the content related to data in Machine learning."
"the instructor have made the entire learning process very smooth"
The programming assignments are tough, but instructive.
"The programming assignment was tough, the instructions were a bit misleading."
"You'll learn how to be aware of your data and address different problems that could significantly affect your machine learning model."
"Plus, the practical assignment was really enjoyable."
There are some technical challenges with the notebooks.
"The experience with the programming assignment was very bad."
"the Notebook with assignment is broken."
"After an hour or so every keystroke was slow."
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 Data for Machine Learning with these
activities:
Solve Python Coding Challenges
Show steps
Sharpen your Python programming skills through practice drills to ensure you have the necessary proficiency for this course.
Browse courses on
Python Programming
Show steps
-
Work through coding exercises on platforms like LeetCode or HackerRank.
-
Solve Python coding challenges and puzzles.
Read 'Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow'
Show steps
Enhance your understanding of machine learning algorithms, techniques, and implementation through a comprehensive book review.
View
Hands-On Machine Learning with Scikit-Learn,...
on Amazon
Show steps
-
Read and summarize key chapters of the book.
-
Identify and explain the core concepts discussed in the book.
Join a Study Group
Show steps
Connect with peers to discuss course concepts, exchange ideas, and reinforce your learning through collaborative problem-solving.
Show steps
-
Join or form a study group with classmates.
-
Meet regularly to discuss course materials, ask questions, and work on assignments together.
Four other activities
Expand to see all activities and additional details
Show all seven activities
Write a Blog Post on Feature Engineering
Show steps
Solidify your understanding of feature engineering by creating a blog post that explains the concepts, techniques, and benefits of feature engineering in machine learning.
Browse courses on
Feature Engineering
Show steps
-
Research and gather information on feature engineering.
-
Outline the key concepts and techniques involved in feature engineering.
-
Write a comprehensive blog post explaining the importance and benefits of feature engineering.
Explore Advanced Machine Learning Techniques
Show steps
Expand your knowledge by exploring advanced machine learning techniques and algorithms to deepen your understanding of the subject matter.
Browse courses on
Advanced Machine Learning
Show steps
-
Follow online tutorials or enroll in courses on advanced machine learning topics.
-
Experiment with different machine learning algorithms and techniques.
Build a Machine Learning Model
Show steps
Apply the concepts learned in the course by building a machine learning model that addresses a real-world problem.
Show steps
-
Define a problem statement and gather relevant data.
-
Choose and implement a suitable machine learning algorithm.
-
Train and evaluate the model using appropriate metrics.
Volunteer as a Mentor
Show steps
Reinforce your understanding and solidify your knowledge by mentoring other students or individuals interested in learning about data and machine learning.
Show steps
-
Offer support and guidance to students or individuals in their learning journey.
-
Answer questions, provide feedback, and share your experiences.
Solve Python Coding Challenges
Show steps
Sharpen your Python programming skills through practice drills to ensure you have the necessary proficiency for this course.
Browse courses on
Python Programming
Show steps
Show steps
Show steps
- Work through coding exercises on platforms like LeetCode or HackerRank.
- Solve Python coding challenges and puzzles.
Read 'Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow'
Show steps
Enhance your understanding of machine learning algorithms, techniques, and implementation through a comprehensive book review.
View
Hands-On Machine Learning with Scikit-Learn,...
on Amazon
Show steps
Show steps
Show steps
- Read and summarize key chapters of the book.
- Identify and explain the core concepts discussed in the book.
Join a Study Group
Show steps
Connect with peers to discuss course concepts, exchange ideas, and reinforce your learning through collaborative problem-solving.
Show steps
Show steps
Show steps
- Join or form a study group with classmates.
- Meet regularly to discuss course materials, ask questions, and work on assignments together.
Four other activities
Expand to see all activities and additional details
Show all seven activities
Write a Blog Post on Feature Engineering
Show steps
Solidify your understanding of feature engineering by creating a blog post that explains the concepts, techniques, and benefits of feature engineering in machine learning.
Browse courses on
Feature Engineering
Show steps
Show steps
Show steps
- Research and gather information on feature engineering.
- Outline the key concepts and techniques involved in feature engineering.
- Write a comprehensive blog post explaining the importance and benefits of feature engineering.
Explore Advanced Machine Learning Techniques
Show steps
Expand your knowledge by exploring advanced machine learning techniques and algorithms to deepen your understanding of the subject matter.
Browse courses on
Advanced Machine Learning
Show steps
Show steps
Show steps
- Follow online tutorials or enroll in courses on advanced machine learning topics.
- Experiment with different machine learning algorithms and techniques.
Build a Machine Learning Model
Show steps
Apply the concepts learned in the course by building a machine learning model that addresses a real-world problem.
Show steps
Show steps
Show steps
- Define a problem statement and gather relevant data.
- Choose and implement a suitable machine learning algorithm.
- Train and evaluate the model using appropriate metrics.
Volunteer as a Mentor
Show steps
Reinforce your understanding and solidify your knowledge by mentoring other students or individuals interested in learning about data and machine learning.
Show steps
Show steps
Show steps
- Offer support and guidance to students or individuals in their learning journey.
- Answer questions, provide feedback, and share your experiences.
Career center
Learners who complete Data for Machine Learning will develop knowledge and skills
that may be useful to these careers:
Data Scientist
Data Scientist
Data Scientists are in charge of preparing, analyzing, and interpreting data in order to draw meaningful conclusions from it. These conclusions are used for making informed business decisions, developing models to drive AI and machine learning applications, and much more. The Data for Machine Learning course from Coursera and the Alberta Machine Intelligence Institute can be of great help for Data Scientists because it teaches them how to clean, prepare, and engineer data in order to improve the accuracy of machine learning models. Additionally, the course can increase a Data Scientist’s understanding of how data can affect the learning, training, and operation phases of machine learning models. Those who wish to become Data Scientists will find that this course can greatly increase their chances of success.
Machine Learning Engineer
Machine Learning Engineer
A Machine Learning Engineer works with large amounts of data to build machine learning models and oversee their production. They also analyze data, identify patterns, and implement algorithms to solve complex problems. The Data for Machine Learning course can be very useful for Machine Learning Engineers because it dives deep into the steps necessary to acquire, prepare, and engineer data in order to maximize the accuracy of a machine learning model. Those who wish to become Machine Learning Engineers will find that this course can help them develop the skills necessary to build high-accuracy machine learning models and enter the field as quickly as possible.
Data Analyst
Data Analyst
Data Analysts play an important role in transforming raw data into actionable insights that can guide business decisions. They collect, analyze, and interpret data, and then share their findings with stakeholders in a clear and concise way. The Data for Machine Learning course can greatly benefit Data Analysts in their understanding of how data can affect the learning, training, and operation phases of machine learning models. This course will also teach analysts how to clean, prepare, and engineer data in order to improve the accuracy of machine learning models.
Data Engineer
Data Engineer
Data Engineers make sure that their companies have the right data available to them to make informed decisions. They design, build, and maintain the systems that collect, store, and process data. The Data for Machine Learning course can greatly benefit Data Engineers as it dives deep into the steps necessary to acquire, prepare, and engineer data in order to maximize the accuracy of a machine learning model. Those who wish to work as Data Engineers will find that this course can help them develop the skills necessary to build high-accuracy machine learning models and enter the field as quickly as possible.
Statistician
Statistician
Statisticians use mathematical and statistical methods to collect, analyze, interpret, and present data. They work in a variety of industries, including finance, healthcare, and government. The Data for Machine Learning course can be very helpful for Statisticians as it teaches how to clean, prepare, and engineer data to improve the accuracy of machine learning models. Those who wish to become Statisticians will find that this course can teach them many of the skills necessary to succeed in this field.
Business Analyst
Business Analyst
Business Analysts use their knowledge of business processes and data analysis to help organizations make better decisions. They gather and analyze data, and then use that data to identify problems and opportunities. The Data for Machine Learning course can greatly benefit Business Analysts in their understanding of how data can affect the learning, training, and operation phases of machine learning models. This course will also teach analysts how to clean, prepare, and engineer data in order to improve the accuracy of machine learning models.
Software Engineer
Software Engineer
Software Engineers design, develop, and maintain software applications. They work with a variety of programming languages and technologies to create software that meets the needs of users. While the Data for Machine Learning course does not teach students to code, it can still be very helpful for Software Engineers who want to work on machine learning projects. The course teaches students how to prepare and engineer data for machine learning models, which is a critical skill for Software Engineers who want to build high-accuracy machine learning models.
Quantitative Analyst
Quantitative Analyst
Quantitative Analysts use mathematical and statistical models to analyze data and make predictions. They work in a variety of industries, including finance, insurance, and healthcare. The Data for Machine Learning course can be very helpful for Quantitative Analysts as it teaches how to clean, prepare, and engineer data to improve the accuracy of machine learning models. Those who wish to become Quantitative Analysts will find that this course can teach them many of the skills necessary to succeed in this field.
Actuary
Actuary
Actuaries use mathematical and statistical methods to assess risk and uncertainty. They work in a variety of industries, including insurance, finance, and healthcare. The Data for Machine Learning course can be very helpful for Actuaries as it teaches how to clean, prepare, and engineer data to improve the accuracy of machine learning models. While the course does not teach students about actuarial science, it can still be helpful for Actuaries who want to use machine learning in their work.
Computer Scientist
Computer Scientist
Computer Scientists design and develop computer software and systems. They work with a variety of programming languages and technologies to create software that meets the needs of users. While the Data for Machine Learning course does not teach students to code, it can still be very helpful for Computer Scientists who want to work on machine learning projects. The course teaches students how to prepare and engineer data for machine learning models, which is a critical skill for Computer Scientists who want to build high-accuracy machine learning models.
Operations Research Analyst
Operations Research Analyst
Operations Research Analysts use mathematical and analytical methods to solve complex problems. They work in a variety of industries, including manufacturing, logistics, and healthcare. The Data for Machine Learning course can be very helpful for Operations Research Analysts as it teaches how to clean, prepare, and engineer data to improve the accuracy of machine learning models. While the course does not teach students about operations research, it can still be helpful for Operations Research Analysts who want to use machine learning in their work.
Data Architect
Data Architect
Data Architects design and build data management systems. They work with a variety of data sources and technologies to create systems that meet the needs of users. While the Data for Machine Learning course does not teach students to code, it can still be very helpful for Data Architects who want to work on machine learning projects. The course teaches students how to prepare and engineer data for machine learning models, which is a critical skill for Data Architects who want to build high-accuracy machine learning models.
Mathematician
Mathematician
Mathematicians use mathematical methods to solve problems in a wide variety of fields, including science, engineering, and business. While the Data for Machine Learning course may not be necessary for those who wish to enter the field of Mathematics, it can still be beneficial. The course teaches students how to prepare and engineer data for machine learning models, which is a skill that can be applied to a variety of mathematical problems.
Physicist
Physicist
Physicists use the principles of physics to study the behavior of matter and energy. While the Data for Machine Learning course may not be necessary for those who wish to enter the field of Physics, it can still be helpful. The course teaches students how to prepare and engineer data for machine learning models, which is a skill that can be applied to a variety of problems in physics.
Chemist
Chemist
Chemists use the principles of chemistry to study the composition, structure, and properties of matter. While the Data for Machine Learning course may not be necessary for those who wish to enter the field of Chemistry, it can still be helpful. The course teaches students how to prepare and engineer data for machine learning models, which is a skill that can be applied to a variety of problems in chemistry.
For more career information including salaries, visit:
OpenCourser.com/course/28pobz/data
Reading list
We've selected 15 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
Data for Machine Learning.
Practical guide to machine learning using Python, covering the most important concepts and techniques. It good resource for beginners who want to learn how to build and deploy machine learning models.
Save
Provides a comprehensive overview of pattern recognition and machine learning, including supervised and unsupervised learning, statistical modeling, and Bayesian inference.
Provides a probabilistic introduction to machine learning, making it a valuable resource for those with a strong background in mathematics and statistics.
Focuses on how to use data science techniques to solve business problems, making it a valuable resource for those interested in applying machine learning in the real world.
Covers Python machine learning in depth, including data preprocessing, feature engineering, model training, and evaluation.
Save
Provides a comprehensive overview of data mining techniques, including data preprocessing, clustering, classification, and association rule mining.
Provides a comprehensive overview of reinforcement learning, making it a valuable resource for those interested in developing self-learning systems.
Provides a comprehensive overview of statistical methods for machine learning, making it a valuable resource for those interested in understanding the theoretical foundations of machine learning.
Save
Comprehensive introduction to deep learning, covering the most important concepts and techniques. It good resource for beginners who want to learn the basics of deep learning.
Comprehensive introduction to statistical learning, covering the most important concepts and techniques. It good resource for beginners who want to learn the basics of statistical learning.
Comprehensive introduction to data science, covering the entire process from data collection to model deployment. It good resource for beginners who want to learn the basics of data science.
Provides practical instructions on how to use machine learning to solve real-world problems, making it a valuable resource for those interested in applying machine learning in a variety of domains.
Provides a concise overview of machine learning, making it a valuable resource for those interested in getting started with the field.
Provides a concise overview of machine learning, making it a valuable resource for those interested in getting started with the field.
Classic work on data-oriented programming, covering the most important concepts and techniques. It good resource for beginners who want to learn how to write efficient and effective data-oriented programs.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/28pobz/data
Share
Similar courses
Here are nine courses similar to
Data for Machine Learning.
Optimizing Machine Learning Performance
OpenCourser.com/course/agiw9v/optimizing
Optimizing Machine Learning Performance
Building Features from Nominal and Numeric Data in Microsoft Azure
OpenCourser.com/course/xuupl5/building
Building Features from Nominal and Numeric Data in...
Data Wrangling with Pandas for Machine Learning Engineers
OpenCourser.com/course/ttpdut/data
Data Wrangling with Pandas for Machine Learning Engineers
Machine Learning for Financial Services
OpenCourser.com/course/6psi1z/machine
Machine Learning for Financial Services
Machine Learning Foundations for Product Managers
OpenCourser.com/course/ciz3vy/machine
Machine Learning Foundations for Product Managers
Fintech: AI & Machine Learning in the Financial Industry
OpenCourser.com/course/2slqut/fintech
Fintech: AI & Machine Learning in the Financial Industry
Machine Learning with XGBoost Using scikit-learn in Python
OpenCourser.com/course/64aetr/machine
Machine Learning with XGBoost Using scikit-learn in Python
Machine Learning for Retail
OpenCourser.com/course/1vrjw6/machine
Machine Learning for Retail
Applied Data Science Capstone
OpenCourser.com/course/vd8bcc/applied
Applied Data Science Capstone
Level
Intermediate
Via
Coursera
Institution
Alberta Machine Intelligence Institute
Instructor
Anna Koop
Language
English
Transcripts
Español
Français
Português
Deutsch
Italiano
中文
العربية
한국어
日本語
हिंदी
Nederlands
Svenska
Pусский
Türkçe
Polski
Ελληνικά
українська мова
magyar nyelv
Bahasa Indonesia
ไทย
қазақша
اُردُو,
فارسی
বাংলা
tiếng Việt
پښتو
آذربایجان دیلی
This course belongs to a
collection
called
Machine Learning: Algorithms in the Real World. It's comprised of four courses:
- Data for Machine Learning (viewing)
- Machine Learning Algorithms: Supervised Learning Tip to Tail
- Introduction to Applied Machine Learning
- Optimizing Machine Learning Performance
Good to know
Introduces learners to the significance of data preparation for machine learning projects
Guides learners in transitioning raw data into structured, model-ready data
Develops critical skills in feature engineering, which enhance model performance
Emphasizes the importance of data quality control and error detection for successful machine learning outcomes
Lays the groundwork for effective model building and evaluation
Our mission
OpenCourser helps millions of learners each year. People visit us to learn workspace skills, ace their exams, and nurture their curiosity.
Our extensive catalog contains over 50,000 courses and twice as many books. Browse by search, by topic, or even by career interests. We'll match you to the right resources quickly.
Find this site helpful? Tell a friend about us.
Affiliate disclosure
We're supported by our community of learners. When you purchase or subscribe to courses and programs or purchase books, we may earn a commission from our partners.
Your purchases help us maintain our catalog and keep our servers humming without ads.
Thank you for supporting OpenCourser.
© 2016 - 2024 OpenCourser