Introduction to Applied Machine Learning
3.0 /5- Offered byCoursera
Introduction to Applied Machine Learning at Coursera Overview
Duration | 7 hours |
Start from | Start Now |
Total fee | Free |
Mode of learning | Online |
Difficulty level | Intermediate |
Official Website | Explore Free Course  |
Credential | Certificate |
Introduction to Applied Machine Learning at Coursera Highlights
- Shareable Certificate Earn a Certificate upon completion
- 100% online Start instantly and learn at your own schedule.
- Course 1 of 4 in the Machine Learning: Algorithms in the Real World Specialization
- Flexible deadlines Reset deadlines in accordance to your schedule.
- Intermediate Level
- Approx. 7 hours to complete
- English Subtitles: Arabic, French, Portuguese (European), Italian, Vietnamese, German, Russian, English, Spanish
Introduction to Applied Machine Learning at Coursera Course details
- 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.
Introduction to Applied Machine Learning at Coursera Curriculum
Introduction to Machine Learning Applications
Introduction to the Applied Machine Learning Specialization
Instructor Introduction
Introduction to Course 1
What is Artificial Intelligence and Machine Learning?
What about Data Science?
The Machine Learning Process
The Three Kinds of Machine Learning
Classification: What is it and how does it work?
Regression: Fitting lines and predicting numbers
Unsupervised Learning
Reinforcement Learning
Weekly Summary
What about Deep Learning? (supplemental)
Fooling Neural Networks (supplemental)
How to Curate A Ground Truth For Your Business Dataset (Required)
Learning From Multiple Annotators: A Survey (supplemental)
Inferring the Ground Truth Through Crowdsourcing (supplemental)
Semi Supervised Learning (required)
Concepts and Definitions
Identifying Machine Learning Techniques
Machine Learning in the Real World
Generalization and how machines actually learn
Features and transformations of raw data
Farmer Betty and Her Precision Agriculture Plans
What to consider when using your QuAM
Broad Examples Narrowed Down
Identify Business Evaluation
Everything is a Proxy
Weekly Summary
A Brief Introduction into Precision Agriculture
Farmer Betty Tried Unsupervised Learning (required)
Data is Central to Your ML Problem (required)
Martin Zinkevich's Rules for ML (supplemental)
Machine Learning in the Real World Review
Learning Data
Sources of Training Data
How Much Data Do I Need?
Ethical Issues
Bias in Data Sources
Noise and Sources of Randomness
Image Classification Example
Data Cleaning: Everybody's favourite task
Why you need to set up a Data Pipeline
Weekly Summary
Data Protection Laws (required)
Government readings on data privacy (supplemental)
Understanding Data for ML
Machine Learning Projects
MLPL Overview
MLPL as experienced by Farmer Betty
Exploring the process of problem definition
Assessing your QuAM for use in your Business
Technically Assessing the Strength of your QuAM
Different Kinds of Wrong
Weekly Summary
Machine Learning Process Lifecycle Explained
Deep Learning for Identifying Metastatic Breast Cancer (advanced supplemental)
Understanding Machine Learning Projects
Introduction to Applied Machine Learning at Coursera Admission Process
Important Dates
Other courses offered by Coursera
Introduction to Applied Machine Learning at Coursera Students Ratings & Reviews
1 Rating- 2-31
Student Forum
Anything you would want to ask experts?