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.

MODULE 2 - Standard, Go-To Machine Learning Methods

This module covers four standard machine learning methods: decision trees, Naive Bayes, linear regression, and logistic regression. We'll show you how they work, checking their predictive performance over example datasets and visualizing their decision boundaries as a way to compare and contrast their capabilities. You'll also see how to evaluate these models in terms of lift and profit, and why improving model probability estimates is so important.

MODULE 3 - Advanced Methods, Comparing Methods, & Modeling Software

When should you turn to deep learning, the leading advanced machine learning method, and when is its complexity overkill? And is there a way to advance model capability and performance that's elegant and simple, without involving the complexity of neural networks? In this module, we'll cover more advanced modeling methods, including neural networks, deep learning, and ensemble models. Then we'll compare and contrast the full range of modeling methods, and we'll overview the many machine learning software tool options you have at your disposal. We'll then turn to a special, advanced method called uplift modeling (aka persuasion modeling), which goes beyond predicting an outcome to actually predicting the influence that a decision would have on that outcome. We'll explore the marketing applications of uplift modeling and see success stories from the likes of US Bank and President Obama's 2012 reelection campaign.

MODULE 4 – Pitfalls, Bias, and Conclusions

Crime-predicting models cannot on their own realize racial equity. It turns out that models that are racially equitable in one sense are not in another. This is often referred to as machine bias. This quandary also applies for other kinds of consequential decisions driven by predictive models, including loan approvals, insurance pricing, HR decisions, and medical triage. This module dives deep into understanding the machine bias conundrum and what recourses could be considered in response to it. We'll also ramp up on a related, emerging movement in support of model transparency, explainable machine learning, and the right to explanation. We'll then wrap up the overall three-course specialization with a summary of the ethical issues, the technical pitfalls, and your options for continuing your learning and career path in machine learning.

Good to know

Know what's good

, what to watch for

, 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

Machine learning under the hood

Learners say this course is largely positive and that Professor Eric Siegel is an excellent instructor who delivers clear explanations of complex algorithms. They report that the course is engaging and enjoyable for all levels of expertise. Students emphasize the importance of understanding both the technical and business aspects of Machine Learning and that this course provides valuable insights into both. Several students also note the practicality of the course's approach, with hands-on exercises providing opportunities to apply the lessons learned. Overall, students strongly recommend this course, especially for those who want to understand how to implement ML projects.

The course takes a practical approach with hands-on exercises.

"the course even includes a hands on exercise based on Excel/Google Sheet which helps to consolidate what has been learnt."

"This is a unique and engaging course that tackles a very interesting yet complex topic."

The course covers the ethical considerations of Machine Learning.

"Two such things are P-hacking and Uplift Modeling"

"This is a unique and engaging course that tackles a very interesting yet complex topic. I really enjoyed how Professor Eric demystifies many wrong assumptions about machine learning, how he explains the different business and technical requirements and details and gives and due needed attention to the ethical aspect of ML which is very critical to consider for leading ML projects everywhere."

Professor Eric Siegel is an excellent instructor.

"Eric is a great instructor!"

"Eric Siegel is very insightful in this 3 course machine learning specialization for data oriented business leaders!"

It's important to understand both the technical and business aspects of Machine Learning.

"Quite some depth and subtly technical, but not outside the understanding of a good business-side executive."

"This is increasingly essential knowledge for both business leaders who know that data must be tapped for competitive advantage and analytics professionals who need to understand how to help businesses tap that power."

Professor Siegel delivers clear explanations of complex algorithms.

"Eric makes fairly complex algorithms/ideas easy to understand."

"What a clear course."

"A very enjoyable and worthwhile course for ML novices as well as techies"

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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Show all eight activities

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:

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

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

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

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

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

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

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

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

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

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

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

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

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