Machine Learning Under the Hood: The Technical Tips, Tricks, and Pitfalls from Coursera

Machine learning. Your team needs it, your boss demands it, and your career loves it. After all, LinkedIn places it as one of the top few "Skills Companies Need Most" and as the very top emerging job in the U.S.

If you want to participate in the deployment of machine learning (aka predictive analytics), you've got to learn how it works. Even if you work as a business leader rather than a hands-on practitioner – even if you won't crunch the numbers yourself – you need to grasp the underlying mechanics in order to help navigate the overall project. Whether you're an executive, decision maker, or operational manager overseeing how predictive models integrate to drive decisions, the more you know, the better.

And yet, looking under the hood will delight you. The science behind machine learning intrigues and surprises, and an intuitive understanding is not hard to come by. With its impact on the world growing so quickly, it's time to demystify the predictive power of data – and how to scientifically tap it.

This course will show you how machine learning works. It covers the foundational underpinnings, the way insights are gleaned from data, how we can trust these insights are reliable, and how well predictive models perform – which can be established with pretty straightforward arithmetic. These are things every business professional needs to know, in addition to the quants.

And this course continues beyond machine learning standards to also cover cutting-edge, advanced methods, as well as preparing you to circumvent prevalent pitfalls that seldom receive the attention they deserve. The course dives deeply into these topics, and yet remains accessible to non-technical learners and newcomers.

With this course, you'll learn what works and what doesn't – the good, the bad, and the fuzzy:

– How predictive modeling algorithms work, including decision trees, logistic regression, and neural networks

– Treacherous pitfalls such as overfitting, p-hacking, and presuming causation from correlations

– How to interpret a predictive model in detail and explain how it works

– Advanced methods such as ensembles and uplift modeling (aka persuasion modeling)

– How to pick a tool, selecting from the many machine learning software options

– How to evaluate a predictive model, reporting on its performance in business terms

– How to screen a predictive model for potential bias against protected classes – aka AI ethics

IN-DEPTH YET ACCESSIBLE. Brought to you by industry leader Eric Siegel – a winner of teaching awards when he was a professor at Columbia University – this curriculum stands out as one of the most thorough, engaging, and surprisingly accessible on the subject of machine learning.

NO HANDS-ON AND NO HEAVY MATH. Rather than a hands-on training, this course serves both business leaders and burgeoning data scientists alike with expansive coverage of the state-of-the-art techniques and the most pernicious pitfalls. There are no exercises involving coding or the use of machine learning software. However, for one of the assessments, you'll perform a hands-on exercise, creating a predictive model by hand in Excel or Google Sheets and visualizing how it improves before your eyes.

BUT TECHNICAL LEARNERS SHOULD TAKE ANOTHER LOOK. Before jumping straight into the hands-on, as quants are inclined to do, consider one thing: This curriculum provides complementary know-how that all great techies also need to master. It contextualizes the core technology with a strong conceptual framework and covers topics that are generally omitted from even the most technical of courses, including uplift modeling (aka persuasion modeling) and some particularly treacherous pitfalls.

VENDOR-NEUTRAL. This course includes illuminating software demos of machine learning in action using SAS products. However, the curriculum is vendor-neutral and universally-applicable. The contents and learning objectives apply, regardless of which machine learning software tools you end up choosing to work with.

PREREQUISITES. Before this course, learners should take the first two of this specialization's three courses, "The Power of Machine Learning" and "Launching Machine Learning."

What's inside

Syllabus

MODULE 1 - The Foundational Underpinnings of Machine Learning

In what way is bigger data more dangerous? How do we avoid being fooled by random noise and ensure scientific discoveries are trustworthy? This module covers the fundamental ways in which machine learning works – and doesn't work. First, we'll cover three prevalent, heartbreaking pitfalls: overfitting, p-hacking, and presuming causation when we have only ascertained correlation. Then we'll establish the foundational principles behind the design of machine learning methods.

Traffic lights

Read about what's good

what should give you pause

and possible dealbreakers

Builds a strong foundation in scientific understanding of ML, including how it works, how insights are gleaned, and how well models perform

Taught by Eric Seigel, a renowned ML instructor and former professor at Columbia University

Provides insights for both business leaders and practitioners, regardless of technical background

Covers advanced methods like ensembles, uplift modeling, and vendor-neutral software selection

Addresses important ethical considerations in AI, including bias screening and the right to explanation

Reviews summary

Deep dive into ml concepts and pitfalls

According to learners, this course offers a deep and accessible dive into the foundational concepts of machine learning, proving particularly valuable for those seeking to understand its underlying mechanics without heavy coding or math. Students highly praise the instructor's ability to explain complex topics clearly and engagingly. A significant strength noted is the extensive coverage of treacherous pitfalls like overfitting and P-hacking, alongside crucial discussions on AI ethics and bias. While the course is conceptual rather than hands-on, this is seen as a feature for its target audience of business leaders and data scientists needing a strong theoretical framework. Some mention the importance of prerequisite knowledge from prior specialization courses.

Benefits from prior knowledge from the specialization's earlier courses.

"I did take the prerequisites, which helped."

"A strong prerequisite knowledge from the previous courses is definitely beneficial for this one."

Focuses on theory and concepts, explicitly without coding.

"The no hands-on approach was stated clearly, but I did find myself wishing for a few more small exercises, even just conceptual ones."

"If you're looking for practical implementation, this isn't it. Better suited for pure conceptual understanding, perhaps for executives."

"I came here hoping for some insights on implementing ML, but it's all theory. The no coding part was a big disappointment."

"It's exactly as described: 'under the hood,' not 'how to code.' Perfect for those transitioning into ML management roles."

Covers advanced topics like uplift modeling, rarely found elsewhere.

"The sections on uplift modeling were a revelation – something rarely covered in other courses."

"This course still provided valuable new perspectives, especially on advanced topics like uplift modeling..."

Instructor's engaging style clarifies complex machine learning topics.

"Dr. Siegel makes complex topics surprisingly accessible and engaging."

"Eric Siegel is a fantastic instructor. His ability to explain complex machine learning ideas clearly is unparalleled."

"The instructor’s passion for the subject shines through."

Excellent coverage of common ML pitfalls and ethical considerations.

"The focus on pitfalls like overfitting and P-hacking was invaluable, and the module on AI ethics was particularly insightful."

"Excellent course that addresses crucial aspects of ML often ignored elsewhere, like bias and ethical considerations."

"This course still provided valuable new perspectives, especially on advanced topics like uplift modeling and the extensive coverage of pitfalls."

"It contextualizes the core technology and covers topics generally omitted from even the most technical of courses."

Provides a thorough, accessible understanding of ML concepts.

"This course was exactly what I needed as a business leader. It demystified machine learning without getting bogged down in math or code."

"Dr. Siegel makes complex topics surprisingly accessible and engaging."

"His ability to explain complex machine learning ideas clearly is unparalleled."

"The course truly delves into the 'why' behind machine learning, which is often overlooked."

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 Machine Learning Under the Hood: The Technical Tips, Tricks, and Pitfalls with these activities:

Participate in a study group with other students.

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Enhance your understanding of machine learning by discussing concepts with peers.

Browse courses on Machine Learning

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Find a study group to join.
Attend study group meetings regularly.
Participate in discussions and ask questions.

Read 'Machine Learning in Action' by Peter Harrington.

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Review the underlying concepts of machine learning by reading a book written by an industry expert.

View Machine Learning in Action on Amazon

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Read the first three chapters of the book.
Summarize the key concepts covered in each chapter.
Complete the exercises at the end of each chapter.

Attend a machine learning workshop.

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Gain hands-on experience with machine learning by attending a workshop.

Browse courses on Machine Learning

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Find a machine learning workshop to attend.
Register for the workshop.
Attend the workshop and participate in the activities.

Five other activities

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Practice coding machine learning algorithms.

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Reinforce your understanding of machine learning algorithms by coding them yourself.

Browse courses on Machine Learning Algorithms

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Choose a machine learning algorithm to code.
Implement the algorithm in your preferred programming language.
Test your implementation on a dataset.

Read 'Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow' by Aurélien Géron.

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Expand your knowledge of machine learning by reading a book that covers practical applications of machine learning.

View Hands-On Machine Learning with Scikit-Learn,... on Amazon

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Read the first five chapters of the book.
Summarize the key concepts covered in each chapter.
Complete the exercises at the end of each chapter.

Write a blog post about a machine learning topic.

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Share your knowledge of machine learning by writing a blog post about a topic you're interested in.

Browse courses on Machine Learning

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Choose a machine learning topic to write about.
Research the topic and gather information.
Write a blog post that explains the topic in a clear and concise way.

Create a presentation on a machine learning project.

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Demonstrate your mastery of machine learning by creating a presentation on a project you've worked on.

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Choose a machine learning project to present.
Gather data and build a model.
Analyze the results of your model.
Create a presentation that explains your project.

Build a machine learning model for a real-world problem.

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Apply your machine learning skills to solve a real-world problem by building a model.

Browse courses on Machine Learning Model

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Identify a real-world problem that you can solve with machine learning.
Gather data and build a model.
Evaluate the performance of your model.
Deploy your model and monitor its performance.

Career center

Learners who complete Machine Learning Under the Hood: The Technical Tips, Tricks, and Pitfalls will develop knowledge and skills that may be useful to these careers:

Data Scientist

Data scientists are responsible for collecting, analyzing, and interpreting data to help businesses make better decisions. This course provides a solid foundation in the principles of machine learning, which is a key technology used by data scientists. The course also covers advanced machine learning methods, such as deep learning and ensemble models, which are increasingly being used in a variety of industries. By taking this course, you will gain the skills and knowledge you need to become a successful data scientist.

See salaries and explore the career path for Data Scientist

Machine Learning Engineer

Machine learning engineers are responsible for building and deploying machine learning models. This course provides a comprehensive overview of the machine learning process, from data collection and preparation to model building and evaluation. The course also covers advanced machine learning methods, such as deep learning and ensemble models, which are increasingly being used in a variety of industries. By taking this course, you will gain the skills and knowledge you need to become a successful machine learning engineer.

See salaries and explore the career path for Machine Learning Engineer

Data Analyst

Data analysts are responsible for analyzing data to identify trends and patterns. This course provides a solid foundation in the principles of machine learning, which can be used to automate and improve the data analysis process. The course also covers advanced machine learning methods, such as deep learning and ensemble models, which can be used to solve complex data analysis problems. By taking this course, you will gain the skills and knowledge you need to become a successful data analyst.

See salaries and explore the career path for Data Analyst

Business Analyst

Business analysts are responsible for analyzing business processes and identifying opportunities for improvement. This course provides a solid foundation in the principles of machine learning, which can be used to automate and improve business processes. The course also covers advanced machine learning methods, such as deep learning and ensemble models, which can be used to solve complex business problems. By taking this course, you will gain the skills and knowledge you need to become a successful business analyst.

See salaries and explore the career path for Business Analyst

Quantitative Analyst

Quantitative analysts are responsible for developing and using mathematical and statistical models to analyze financial data. This course provides a solid foundation in the principles of machine learning, which can be used to develop more accurate and sophisticated financial models. The course also covers advanced machine learning methods, such as deep learning and ensemble models, which can be used to solve complex financial problems. By taking this course, you will gain the skills and knowledge you need to become a successful quantitative analyst.

See salaries and explore the career path for Quantitative Analyst

Research Scientist

Research scientists are responsible for conducting research and developing new technologies. This course provides a solid foundation in the principles of machine learning, which is a key technology used in a variety of research fields. The course also covers advanced machine learning methods, such as deep learning and ensemble models, which are increasingly being used to solve complex research problems. By taking this course, you will gain the skills and knowledge you need to become a successful research scientist.

See salaries and explore the career path for Research Scientist

Software Engineer

Software engineers are responsible for designing, developing, and testing software applications. This course provides a solid foundation in the principles of machine learning, which can be used to develop more intelligent and efficient software applications. The course also covers advanced machine learning methods, such as deep learning and ensemble models, which are increasingly being used to solve complex software engineering problems. By taking this course, you will gain the skills and knowledge you need to become a successful software engineer.

See salaries and explore the career path for Software Engineer

Product Manager

Product managers are responsible for developing and managing products. This course provides a solid foundation in the principles of machine learning, which can be used to develop more innovative and successful products. The course also covers advanced machine learning methods, such as deep learning and ensemble models, which can be used to solve complex product development problems. By taking this course, you will gain the skills and knowledge 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 developing and executing marketing campaigns. This course provides a solid foundation in the principles of machine learning, which can be used to develop more targeted and effective marketing campaigns. The course also covers advanced machine learning methods, such as deep learning and ensemble models, which can be used to solve complex marketing problems. By taking this course, you will gain the skills and knowledge 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 developing and executing sales strategies. This course provides a solid foundation in the principles of machine learning, which can be used to develop more effective sales strategies. The course also covers advanced machine learning methods, such as deep learning and ensemble models, which can be used to solve complex sales problems. By taking this course, you will gain the skills and knowledge 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 managing the operations of a business. This course provides a solid foundation in the principles of machine learning, which can be used to improve the efficiency and effectiveness of business operations. The course also covers advanced machine learning methods, such as deep learning and ensemble models, which can be used to solve complex operational problems. By taking this course, you will gain the skills and knowledge you need to become a successful operations manager.

See salaries and explore the career path for Operations Manager

Financial Analyst

Financial analysts are responsible for analyzing financial data and making investment recommendations. This course provides a solid foundation in the principles of machine learning, which can be used to develop more accurate and sophisticated financial models. The course also covers advanced machine learning methods, such as deep learning and ensemble models, which can be used to solve complex financial problems. By taking this course, you will gain the skills and knowledge you need to become a successful financial analyst.

See salaries and explore the career path for Financial Analyst

Actuary

Actuaries are responsible for assessing and managing risk. This course provides a solid foundation in the principles of machine learning, which can be used to develop more accurate and sophisticated risk models. The course also covers advanced machine learning methods, such as deep learning and ensemble models, which can be used to solve complex risk management problems. By taking this course, you will gain the skills and knowledge you need to become a successful actuary.

See salaries and explore the career path for Actuary

Insurance Underwriter

Insurance underwriters are responsible for assessing and pricing risk. This course provides a solid foundation in the principles of machine learning, which can be used to develop more accurate and sophisticated risk models. The course also covers advanced machine learning methods, such as deep learning and ensemble models, which can be used to solve complex insurance underwriting problems. By taking this course, you will gain the skills and knowledge you need to become a successful insurance underwriter.

See salaries and explore the career path for Insurance Underwriter

Risk Manager

Risk managers are responsible for identifying, assessing, and managing risk. This course provides a solid foundation in the principles of machine learning, which can be used to develop more accurate and sophisticated risk models. The course also covers advanced machine learning methods, such as deep learning and ensemble models, which can be used to solve complex risk management problems. By taking this course, you will gain the skills and knowledge you need to become a successful risk manager.

See salaries and explore the career path for Risk Manager