The Power of Machine Learning: Boost Business, Accumulate Clicks, Fight Fraud, and Deny Deadbeats
- 4.8
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
Related Topics for further study
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
IntermediateCourse 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.
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