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Data for Machine Learning

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.

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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.

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

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

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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

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Sharpen your Python programming skills through practice drills to ensure you have the necessary proficiency for this course.

Browse courses on Python Programming

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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

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Read and summarize key chapters of the book.
Identify and explain the core concepts discussed in the book.

Join a Study Group

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Connect with peers to discuss course concepts, exchange ideas, and reinforce your learning through collaborative problem-solving.

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Join or form a study group with classmates.
Meet regularly to discuss course materials, ask questions, and work on assignments together.

Four other activities

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Show all seven activities

Write a Blog Post on Feature Engineering

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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

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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

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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

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Apply the concepts learned in the course by building a machine learning model that addresses a real-world problem.

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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

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Reinforce your understanding and solidify your knowledge by mentoring other students or individuals interested in learning about data and machine learning.

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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 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.

See salaries and explore the career path for Data Scientist

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.

See salaries and explore the career path for Machine Learning Engineer

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.

See salaries and explore the career path for Data Analyst

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.

See salaries and explore the career path for Data Engineer

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.

See salaries and explore the career path for Statistician

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.

See salaries and explore the career path for Business Analyst

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.

See salaries and explore the career path for Software Engineer

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.

See salaries and explore the career path for Quantitative Analyst

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.

See salaries and explore the career path for Actuary

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.

See salaries and explore the career path for Computer Scientist

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.

See salaries and explore the career path for Operations Research Analyst

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.

See salaries and explore the career path for Data Architect

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.

See salaries and explore the career path for Mathematician

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.

See salaries and explore the career path for Physicist

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.

See salaries and explore the career path for Chemist

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.

Hands-On Machine Learning with Scikit-Learn, Keras,...

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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.

Hands-On Machine Learning with Scikit-Learn, Keras,...

Paperback

Data for Machine Learning

Three deals to help you save

What's inside

Syllabus

Good to know

Save this course

Reviews summary

Data for machine learning: well-received course

Activities

Career center

Reading list

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