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The Power of Machine Learning: Boost Business, Accumulate Clicks, Fight Fraud, and Deny Deadbeats

Approx. 14 hours to complete

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Course Summary

This course teaches the power of machine learning and how it can be used to solve complex problems in various industries. It covers the fundamentals of machine learning, data preprocessing, feature engineering, model selection, and evaluation.

Key Learning Points

Learn how to use machine learning to solve real-world problems
Understand the fundamentals of machine learning and data preprocessing
Explore different techniques for feature engineering and model selection

Job Positions & Salaries of people who have taken this course might have

Machine Learning Engineer
- USA: $112,000
- India: ₹1,200,000
- Spain: €40,000
Data Scientist
- USA: $120,000
- India: ₹1,500,000
- Spain: €45,000
AI Researcher
- USA: $150,000
- India: ₹2,400,000
- Spain: €50,000

Learning Outcomes

Understand the fundamentals of machine learning and its real-world applications
Learn how to preprocess data and engineer features for machine learning models
Gain knowledge in model selection and evaluation

Prerequisites or good to have knowledge before taking this course

Basic programming knowledge
Familiarity with linear algebra and statistics

Course Difficulty Level

Intermediate

Course Format

Self-paced
Online

Similar Courses

Machine Learning by Andrew Ng
Applied Data Science with Python

Related Education Paths

Notable People in This Field

Co-founder of Coursera and Google Brain
Chief AI Scientist at Facebook

Related Books

Description

It's the age of machine learning. Companies are seizing upon the power of this technology to combat risk, boost sales, cut costs, block fraud, streamline manufacturing, conquer spam, toughen crime fighting, and win elections.

Want to tap that potential? It's best to start with a holistic, business-oriented course on machine learning – no matter whether you’re more on the tech or the business side. After all, successfully deploying machine learning relies on savvy business leadership just as much as it relies on technical skill. And for that reason, data scientists aren't the only ones who need to learn the fundamentals. Executives, decision makers, and line of business managers must also ramp up on how machine learning works and how it delivers business value. And the reverse is true as well: Techies need to look beyond the number crunching itself and become deeply familiar with the business demands of machine learning. This way, both sides speak the same language and can collaborate effectively. This course will prepare you to participate in the deployment of machine learning – whether you'll do so in the role of enterprise leader or quant. In order to serve both types, this course goes further than typical machine learning courses, which cover only the technical foundations and core quantitative techniques. This curriculum uniquely integrates both sides – both the business and tech know-how – that are essential for deploying machine learning. It covers: – How launching machine learning – aka predictive analytics – improves marketing, financial services, fraud detection, and many other business operations – A concrete yet accessible guide to predictive modeling methods, delving most deeply into decision trees – Reporting on the predictive performance of machine learning and the profit it generates – What your data needs to look like before applying machine learning – Avoiding the hype and false promises of “artificial intelligence” – AI ethics: social justice concerns, such as when predictive models blatantly discriminate by protected class NO HANDS-ON AND NO HEAVY MATH. This concentrated entry-level program is totally accessible to business leaders – and yet totally vital to data scientists who want to secure their business relevance. It's for anyone who wishes to participate in the commercial deployment of machine learning, no matter whether you'll play a role on the business side or the technical side. This includes business professionals and decision makers of all kinds, such as executives, directors, line of business managers, and consultants – as well as data scientists. 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, guiding you on the end-to-end process required to successfully deploy a predictive model so that it delivers a business impact. LIKE A UNIVERSITY COURSE. This course is also a good fit for college students, or for those planning for or currently enrolled in an MBA program. The breadth and depth of the overall three-course specialization is equivalent to one full-semester MBA or graduate-level course. 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. 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.

Knowledge

Participate in the deployment of machine learning
Identify potential machine learning deployments that will generate value for your organization
Report on the predictive performance of machine learning and the profit it generates
Understand the potential of machine learning and avoid the false promises of “artificial intelligence”

Outline

MODULE 0 - Introduction
Machine learning in 20 seconds
Specialization overview
Why this course isn't "hands-on" & why it's still good for techies anyway
What you'll learn: topics covered and learning objectives
Vendor-neutral courses with complementary demos from SAS
DEMO - Exploring SAS® Visual Data Mining and Machine Learning (optional)
Deep learning: your path towards leveraging the hottest ML method
A tour of this specialization's courses
About your instructor, Eric Siegel
About the problem-solving challenges
The Machine Learning Glossary
One-question survey

MODULE 1 - The Impact of Machine Learning
Predicting the president: two common misconceptions about forecasting
The Obama example: forecasting vs. predictive analytics
The full definitions of machine learning and predictive analytics
Buzzword heyday: putting big data and data science in their place
The two stages of machine learning: modeling and scoring
Targeting marketing with response modeling
The Prediction effect: A little prediction goes a long way
Targeted customer retention with churn modeling
Why targeting ads is like the movie "Groundhog Day"
Another application: financial credit risk
Myriad opportunities: the great range of application areas
"Non-predictive" applications: detection, classification, and diagnosis
Why ML is the latest evolutionary step of the Information Age
Nate Silver on misunderstanding election forecasts (optional)
Predictive analytics overview (optional)
Detailed profit calculations for targeted marketing (optional)
More information about named examples (optional)
Predictive analytics applications (optional)
White paper overviewing the organizational value of predictive analytics
Predicting the president: two common misconceptions about forecasting
The Obama example: forecasting vs. predictive analytics
The full definitions of machine learning and predictive analytics
Buzzword heyday: putting big data and data science in their place
The two stages of machine learning: modeling and scoring
Targeting marketing with response modeling
The Prediction effect: A little prediction goes a long way
Targeted customer retention with churn modeling
Why targeting ads is like the movie "Groundhog Day"
Another application: financial credit risk
Myriad opportunities: the great range of application areas
"Non-predictive" applications: detection, classification, and diagnosis
Why ML is the latest evolutionary step of the Information Age
A question about the reading – the organizational value of predictive analytics
Module 1 Review

MODULE 2 - Data: the New Oil
The big deal about big data
A paradigm shift for scientific discovery: its automation
Example discoveries from data
The Data Effect: Data is always predictive
Training data -- what it looks like
Predicting with one single variable
Growing a decision tree to combine variables
More on decision trees
The light bulb puzzle
Measuring predictive performance: lift
DEMO - Training a simple decision tree model (optional)
How spending habits reveal debtor reliability (optional)
The big deal about big data
A paradigm shift for scientific discovery: its automation
Example discoveries from data
The Data Effect: Data is always predictive
Training data -- what it looks like
Predicting with one single variable
Growing a decision tree to combine variables
More on decision trees
The light bulb puzzle
Measuring predictive performance: lift
Module 2 Review

MODULE 3 - Predictive Models: What Gets Learned from Data
The principles of predictive modeling
How can you trust a predictive model (train/test)?
More predictive modeling principles
Visually comparing modeling methods - decision boundaries
DEMO - Training and comparing multiple models (optional)
Deploying a predictive model
The profit curve of a model
Deployment results in targeting marketing and sales
Deep learning - application areas and limitations
Labeled data: a source of great power, yet a major limitation
Talking computers -- natural language processing and text analytics
Prescriptive vs. Predictive Analytics – A Distinction without a Difference (optional)
Predictive analytics deployment and profit (optional)
More on deep learning (optional)
The difference between Watson and Siri (optional)
The principles of predictive modeling
How can you trust a predictive model (train/test)?
More predictive modeling principles
Visually comparing modeling methods - decision boundaries
Deploying a predictive model
The profit curve of a model
Deployment results in targeting marketing and sales
Deep learning - application areas and limitations
Labeled data: a source of great power, yet a major limitation
Talking computers – natural language processing and text analytics
Module 3 Review

MODULE 4 - Industry Perspective: AI Myths and Real Ethical Risks
Why machine learning isn't becoming superintelligent
Dismantling the logical fallacy that is AI
Why legitimizing AI as a field incurs great cost
Ethics overview: five ways ML threatens social justice
Blatantly discriminatory models
The trend towards discriminatory models
The argument against discriminatory models
Five myths about "evil" big data
Defending machine learning -- how it does good
Course wrap-up
AI is a big fat lie (optional)
AI is an ideology, not a technology (optional)
Book Review: Weapons of Math Destruction by Cathy O'Neil
Coded gaze on speech recognition (optional)
Why machine learning isn't becoming superintelligent
Dismantling the logical fallacy that is AI
Why legitimizing AI as a field incurs great cost
Ethics overview: five ways ML threatens social justice
Blatantly discriminatory models
The trend towards discriminatory models
The argument against discriminatory models
Five myths about "evil" big data
Defending machine learning -- how it does good
Module 4 Review

Summary of User Reviews

Discover the power of machine learning with this comprehensive course from Coursera. Students have praised the program for its insightful content and expert instruction. One key aspect that many users appreciated was the course's emphasis on real-world applications of machine learning.

Pros from User Reviews

Insightful content
Expert instruction
Real-world applications
Clear explanations
Engaging assignments

Cons from User Reviews

Some technical concepts may be challenging for beginners
Lengthy course materials
Limited interaction with instructors
Some exercises may be too difficult for learners with no coding experience
Not suitable for those seeking an overview of machine learning

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