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Introduction to Machine Learning

Art of the Possible

4.1 Filled star Filled star Filled star Filled star Empty star
Based on 17 ratings
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Nikki Gibson

The Introduction to Machine Learning: Art of the Possible course provides best practices and recommendations for machine learning (ML) competency and a roadmap for integrating ML into your business processes. This course focuses on business leaders and other decision makers currently or potentially involved in ML projects.

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What's inside

Syllabus

Traffic lights

Read about what's good
what should give you pause
and possible dealbreakers
Provides best practices and recommendations for machine learning (ML) competency and integrates ML
Examines the roadmap for integrating machine learning into business processes
Focuses on business leaders and other decision-makers involved in ML projects

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Reviews summary

Ml overview for business leaders

According to learners, this course is a highly effective introduction to Machine Learning specifically tailored for business leaders and decision makers. Students consistently praise its ability to provide a clear, non-technical overview of ML concepts, focusing on the strategic implications and business applications. Many appreciate that it avoids deep technical jargon, making complex ideas accessible and easy to understand. The practical case studies and examples are frequently highlighted as valuable for understanding the 'art of the possible' with ML and for bridging the gap between business and technical teams. A common point of divergence is from those seeking technical implementation details, indicating it's not suited for engineers.
Provides a high-level, sometimes generic, perspective on ML.
"It's an okay introduction... Good for absolute beginners in management, perhaps."
"Found it too generic. While it promises to be for business leaders, I felt it didn't go deep enough into specific industry applications."
"It's a broad overview, but perhaps too broad."
Real-world examples enhance understanding and application.
"The case studies were particularly insightful and practical."
"I think the case studies were very helpful in illustrating concepts."
"The pace was good, and the examples were relevant."
Explains complex ML concepts clearly for a broad audience.
"Excellent course for business leaders! It clarified how ML can be applied without getting bogged down in math."
"It's not a technical deep dive, which is perfect for me. The instructor clearly explained complex ideas in an accessible way."
"This course nailed it for non-technical professionals. It covers the 'what' and 'why' of ML, not the 'how'."
Guides business leaders on ML integration and strategy.
"As a project manager, this course was exactly what I needed. It provided a great overview of ML concepts and explained the 'art of the possible' very well. I appreciated the strategic focus..."
"I now feel confident discussing ML initiatives with our technical teams. The case studies were particularly insightful and practical."
"It really focuses on the strategic implications of ML, which is what I needed as a marketing director."
"I now understand the potential of ML and how to identify opportunities. The practical business applications were the highlight."
Lacks depth for engineers or data scientists seeking hands-on skills.
"If you already have some tech background, it might be too high-level. I was hoping for a bit more depth on implementation challenges."
"Disappointing. I expected more technical detail on how to *do* ML. This is just a conceptual overview. Not for engineers or data scientists."
"Not much on the technical side, which is fine for the stated audience."

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 Introduction to Machine Learning: Art of the Possible with these activities:
Review basic statistics and probability
Ensure a solid foundation in statistics and probability, which are essential for machine learning.
Browse courses on Probability
Show steps
  • Review your notes or textbooks on basic statistics and probability
  • Take a practice quiz or exam to test your understanding
  • Identify areas where you need additional review
Read 'Machine Learning For Dummies'
Gain a foundational understanding of machine learning concepts.
View Data Analytics & Visualization All-in-One For... on Amazon
Show steps
  • Read Chapter 1: What is Machine Learning?
  • Read Chapter 2: Types of Machine Learning
  • Read Chapter 3: Machine Learning Algorithms
Join a machine learning study group
Collaborate with peers, discuss concepts, and learn from each other.
Show steps
  • Find a study group or create your own
  • Meet regularly to discuss machine learning topics
  • Work together on projects and assignments
Two other activities
Expand to see all activities and additional details
Show all five activities
Build a machine learning application
Create a practical application of machine learning concepts to reinforce your understanding.
Browse courses on Machine Learning Projects
Show steps
  • Identify a problem that can be solved with machine learning
  • Collect and prepare data
  • Choose and train a machine learning model
  • Deploy your model and evaluate its performance
Volunteer for a machine learning project
Gain practical experience and contribute to the machine learning community.
Show steps
  • Find a machine learning project or organization to volunteer with
  • Contact the organization and express your interest
  • Contribute to the project as directed by the organization

Career center

Learners who complete Introduction to Machine Learning: Art of the Possible will develop knowledge and skills that may be useful to these careers:

Reading list

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Rating
4.1 Filled star Filled star Filled star Filled star Empty star
Effort
1 hour to complete
Level
Introductory
Via
Coursera
Institution
Amazon Web Services
Instructor
Nikki Gibson
Language
English
Traffic lights
Read about what's good
what should give you pause
and possible dealbreakers
Provides best practices and recommendations for machine learning (ML) competency and integrates ML
Examines the roadmap for integrating machine learning into business processes
Focuses on business leaders and other decision-makers involved in ML projects
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