Course Summary
This course provides an introduction to machine learning and its real-world applications. Students will learn how to apply machine learning algorithms to real-world problems, as well as how to evaluate and optimize their performance.Key Learning Points
- Gain a practical understanding of machine learning algorithms and their applications
- Develop the skills necessary to apply machine learning to real-world problems
- Learn how to evaluate and optimize machine learning models
Job Positions & Salaries of people who have taken this course might have
- USA: $113,000
- India: ₹1,013,000
- Spain: €34,000
- USA: $113,000
- India: ₹1,013,000
- Spain: €34,000
- USA: $142,000
- India: ₹1,609,000
- Spain: €54,000
- USA: $113,000
- India: ₹1,013,000
- Spain: €34,000
- USA: $142,000
- India: ₹1,609,000
- Spain: €54,000
- USA: $132,000
- India: ₹1,188,000
- Spain: €40,000
Related Topics for further study
- Machine Learning Algorithms
- Real-World Applications
- Model Evaluation
- Performance Optimization
- Data Preprocessing
Learning Outcomes
- Apply machine learning algorithms to real-world problems
- Evaluate and optimize machine learning models
- Develop a practical understanding of machine learning algorithms and their applications
Prerequisites or good to have knowledge before taking this course
- Basic understanding of programming concepts
- Familiarity with linear algebra and statistics
Course Difficulty Level
IntermediateCourse Format
- Online self-paced
- Video lectures
- Hands-on exercises
Similar Courses
- Applied Data Science with Python
- Applied Machine Learning
Related Education Paths
Notable People in This Field
- Co-founder of Coursera, Professor at Stanford
- Professor of Computer Science at Stanford
Related Books
Description
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.
Outline
- 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)
- 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)
- 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)
Summary of User Reviews
This machine learning course is a great way to learn about the practical applications of ML. Many users praised the instructors' ability to explain complex topics with ease.Pros from User Reviews
- Instructors explain complex topics clearly
- Real-world applications of ML are covered
- Assignments and quizzes are challenging but rewarding
Cons from User Reviews
- Some users felt that the course was too fast-paced
- Some assignments were difficult to complete without prior experience
- The course requires a significant time commitment
Anna Koop
4.7 Raiting
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