VOOZH about

URL: https://www.coursera.org/learn/machine-learning-applied

⇱ Introduction to Applied Machine Learning | Coursera

Introduction to Applied Machine Learning

Keep adding new skills with 10,000+ programs for $239 (usually $399). Save now.

Introduction to Applied Machine Learning

27,886 already enrolled

Included with

Ask Coursera

Gain insight into a topic and learn the fundamentals.
4.7

747 reviews

Intermediate level
Some related experience required
Flexible schedule
7 hours to complete
Learn at your own pace
98%
Most learners liked this course
Gain insight into a topic and learn the fundamentals.
4.7

747 reviews

Intermediate level
Some related experience required
Flexible schedule
7 hours to complete
Learn at your own pace
98%
Most learners liked this course

Build your subject-matter expertise

This course is part of the Machine Learning: Algorithms in the Real World Specialization
When you enroll in this course, you'll also be enrolled in this Specialization.
  • Learn new concepts from industry experts
  • Gain a foundational understanding of a subject or tool
  • Develop job-relevant skills with hands-on projects
  • Earn a shareable career certificate

There are 4 modules in this course

This course is for professionals who have heard the buzz around machine learning and want to apply machine learning to data analysis and automation. Whether finance, medicine, engineering, business or other domains, this course will introduce you to problem definition and data preparation in a machine learning project.

By the end of the course, you will be able to clearly define a machine learning problem using two approaches. You will learn to survey available data resources and identify potential ML applications. You will learn to take a business need and turn it into a machine learning application. You will prepare data for effective machine learning applications. This is the first course of the Applied Machine Learning Specialization brought to you by Coursera and the Alberta Machine Intelligence Institute.

This week, you will learn about what machine learning (ML) actually is, contrast different problem scenarios, and explore some common misconceptions about ML. You will apply this knowledge by identifying different components essential to a machine learning business solution.

What's included

12 videos6 readings2 assignments3 discussion prompts

12 videos•Total 44 minutes
  • Introduction to the Applied Machine Learning Specialization•3 minutes
  • Instructor Introduction•1 minute
  • Introduction to Course 1•2 minutes
  • What is Artificial Intelligence and Machine Learning?•5 minutes
  • What about Data Science?•4 minutes
  • The Machine Learning Process•5 minutes
  • The Three Kinds of Machine Learning•3 minutes
  • Classification: What is it and how does it work?•4 minutes
  • Regression: Fitting lines and predicting numbers•4 minutes
  • Unsupervised Learning•4 minutes
  • Reinforcement Learning•6 minutes
  • Weekly Summary•2 minutes
6 readings•Total 60 minutes
  • What about Deep Learning? (supplemental)•10 minutes
  • Fooling Neural Networks (supplemental)•10 minutes
  • How to Curate A Ground Truth For Your Business Dataset (Required)•10 minutes
  • Learning From Multiple Annotators: A Survey (supplemental)•10 minutes
  • Inferring the Ground Truth Through Crowdsourcing (supplemental)•10 minutes
  • Semi Supervised Learning (required)•10 minutes
2 assignments•Total 30 minutes
  • Concepts and Definitions•20 minutes
  • Identifying Machine Learning Techniques•10 minutes
3 discussion prompts•Total 30 minutes
  • Artificial Intelligence Viewpoints•10 minutes
  • Meet and Greet!•10 minutes
  • Misunderstandings surrounding AI and ML•10 minutes

This week, you will learn how to translate a business need into a machine learning problem. We'll walk through some applied examples so you can get a feel for what makes a well-defined question for your QuAM. Narrowing down your question and making sure you have the data necessary to learn is critical to ML success!

What's included

8 videos4 readings1 assignment2 discussion prompts

8 videos•Total 34 minutes
  • Generalization and how machines actually learn•6 minutes
  • Features and transformations of raw data•6 minutes
  • Farmer Betty and Her Precision Agriculture Plans•4 minutes
  • What to consider when using your QuAM•3 minutes
  • Broad Examples Narrowed Down•5 minutes
  • Identify Business Evaluation•4 minutes
  • Everything is a Proxy•4 minutes
  • Weekly Summary•2 minutes
4 readings•Total 40 minutes
  • A Brief Introduction into Precision Agriculture•10 minutes
  • Farmer Betty Tried Unsupervised Learning (required)•10 minutes
  • Data is Central to Your ML Problem (required)•10 minutes
  • Martin Zinkevich's Rules for ML (supplemental)•10 minutes
1 assignment
  • Machine Learning in the Real World Review•0 minutes
2 discussion prompts•Total 20 minutes
  • Explainability and Accuracy for a QuAM•10 minutes
  • All About Proxies•10 minutes

This week is all about data. You will learn about data acquisition and understand the various sources of training data. We'll talk about how much data you need and what pitfalls might arise, including ethical issues.

What's included

9 videos2 readings1 assignment2 discussion prompts

9 videos•Total 34 minutes
  • Sources of Training Data•3 minutes
  • How Much Data Do I Need?•4 minutes
  • Ethical Issues•4 minutes
  • Bias in Data Sources•3 minutes
  • Noise and Sources of Randomness•5 minutes
  • Image Classification Example•3 minutes
  • Data Cleaning: Everybody's favourite task•5 minutes
  • Why you need to set up a Data Pipeline•5 minutes
  • Weekly Summary•2 minutes
2 readings•Total 20 minutes
  • Data Protection Laws (required)•10 minutes
  • Government readings on data privacy (supplemental)•10 minutes
1 assignment
  • Understanding Data for ML•0 minutes
2 discussion prompts•Total 20 minutes
  • Sources of Data•10 minutes
  • Bias and Noise•10 minutes

This week you will learn about the Machine Learning Process Lifecycle (MLPL). After understanding the definitions and components of the MLPL you will analyze the application of the MLPL on a case study.

What's included

7 videos2 readings1 assignment2 discussion prompts

7 videos•Total 35 minutes
  • MLPL Overview•5 minutes
  • MLPL as experienced by Farmer Betty•3 minutes
  • Exploring the process of problem definition•7 minutes
  • Assessing your QuAM for use in your Business•6 minutes
  • Technically Assessing the Strength of your QuAM•6 minutes
  • Different Kinds of Wrong•4 minutes
  • Weekly Summary•3 minutes
2 readings•Total 20 minutes
  • Machine Learning Process Lifecycle Explained•10 minutes
  • Deep Learning for Identifying Metastatic Breast Cancer (advanced supplemental)•10 minutes
1 assignment
  • Understanding Machine Learning Projects•0 minutes
2 discussion prompts•Total 20 minutes
  • What task can machine learning help you with?•10 minutes
  • False Positives and False Negatives•10 minutes

Earn a career certificate

Add this credential to your LinkedIn profile, resume, or CV. Share it on social media and in your performance review.

Instructor

Instructor ratings
4.7 (191 ratings)
Alberta Machine Intelligence Institute
5 Courses•41,381 learners

Explore more from Machine Learning

Why people choose Coursera for their career

👁 Image

Felipe M.

Learner since 2018
"To be able to take courses at my own pace and rhythm has been an amazing experience. I can learn whenever it fits my schedule and mood."
👁 Image

Jennifer J.

Learner since 2020
"I directly applied the concepts and skills I learned from my courses to an exciting new project at work."
👁 Image

Larry W.

Learner since 2021
"When I need courses on topics that my university doesn't offer, Coursera is one of the best places to go."
👁 Image

Chaitanya A.

"Learning isn't just about being better at your job: it's so much more than that. Coursera allows me to learn without limits."

Learner reviews

  • 5 stars

    74.56%

  • 4 stars

    20.08%

  • 3 stars

    4.41%

  • 2 stars

    0.26%

  • 1 star

    0.66%

Showing 3 of 747

MM
·

Reviewed on Oct 28, 2019

I have really got benefit from this course as a beginner to ML, it gives me the best understanding of ML. I m looking forward to getting into it more efficiently with more practices.

AA
·

Reviewed on Sep 11, 2019

very comprehensive course on applied machine learning. the most interesting information in this course is business needs for ML and what it's requirement to have a good QuAM.

LM
·

Reviewed on Jun 21, 2020

An excellent introduction to the fascinating world of machine learning and its endless applications. Loved the emphasis on the evaluation of the business prospect of ML as well.

Frequently asked questions

To access the course materials, assignments and to earn a Certificate, you will need to purchase the Certificate experience when you enroll in a course. You can try a Free Trial instead, or apply for Financial Aid. The course may offer 'Full Course, No Certificate' instead. This option lets you see all course materials, submit required assessments, and get a final grade. This also means that you will not be able to purchase a Certificate experience.

When you enroll in the course, you get access to all of the courses in the Specialization, and you earn a certificate when you complete the work. Your electronic Certificate will be added to your Accomplishments page - from there, you can print your Certificate or add it to your LinkedIn profile.

Yes. In select learning programs, you can apply for financial aid or a scholarship if you can’t afford the enrollment fee. If fin aid or scholarship is available for your learning program selection, you’ll find a link to apply on the description page.

Financial aid available,