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Matt Swaffer
Enroll in Udacity's Small Data course and learn how to identify small data and apply transfer learning and synthetic data generation to datasets.
Prerequisite details
To optimize your success in this program, we've created a list of prerequisites and recommendations to help you prepare for the curriculum. Prior to enrolling, you should have the following knowledge:
- Probability and statistics
- Intermediate Python
- Basic machine learning
You will also need to be able to communicate fluently and professionally in written and spoken English.
Register for this course and see more details by visiting:
OpenCourser.com/course/mz2dsw/small
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What's inside
Syllabus
Learn about small data and what you'll accomplish. Check your prerequisite knowledge and overview the tools and environment you'll be using.
Read more
Syllabus
Learn about small data and what you'll accomplish. Check your prerequisite knowledge and overview the tools and environment you'll be using.
Read more
You will learn to identify small data as opposed to big data. You will learn about some small data techniques understand the types of problems that can be solved with small datasets.
You will learn the basics of transfer learning as well as how to decide when to use transfer learning. You will see a demo of how transfer learning works.
You will learn the difference between synthetic data and fake data and when you should use synthetic data. You will learn the basics of how to generate synthetic data.
In this project, you'll determine when to use different small data strategies using transfer learning and synthetic data to solve small data problems.
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Offers an introduction to small data, transfer learning, and synthetic data, which are core concepts in data science
Taught by Matt Swaffer, who has extensive experience in data science and machine learning, ensuring high-quality instruction
Practical course with a project component, allowing learners to apply their knowledge and skills to real-world problems
Covers the basics of transfer learning and synthetic data generation techniques, making it suitable for beginners in these areas
Provides a solid foundation for learners who want to explore small data problems and solutions
Save this course
Save Small Datasets in Machine Learning to your list so you can find it easily later:
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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 Small Datasets in Machine Learning with these
activities:
Review concepts of probability and statistics
Show steps
Strengthen your foundation in probability and statistics, enhancing your comprehension of small data concepts.
Browse courses on
Probability
Show steps
-
Review lecture notes or textbooks on probability and statistics
-
Solve practice problems and exercises
Brush up on intermediate Python programming
Show steps
Sharpen your Python skills, ensuring you have the technical foundation to excel in this course.
Browse courses on
Python Programming
Show steps
-
Review Python syntax and data structures
-
Practice coding exercises and challenges
Engage in peer discussions on small data strategies
Show steps
Collaborate with peers, exchange perspectives, and deepen your understanding of small data strategies.
Show steps
-
Join a peer study group or online forum
-
Participate in discussions on small data strategies
-
Share your knowledge and experiences with peers
-
Learn from others' insights and perspectives
Five other activities
Expand to see all activities and additional details
Show all eight activities
Explore case studies on successful transfer learning applications
Show steps
Gain insights from real-world examples, solidifying your comprehension of transfer learning in practice.
Browse courses on
Transfer Learning
Show steps
-
Find case studies on successful transfer learning applications
-
Analyze the case studies, identifying key strategies and techniques
-
Summarize your findings and discuss them with peers
Explore online resources and tutorials on small data applications
Show steps
Expand your knowledge by exploring additional resources, deepening your comprehension of small data applications.
Show steps
-
Search for online resources and tutorials on small data
-
Review and explore the materials
-
Summarize your findings and share them with peers
Practice generating synthetic data using various techniques
Show steps
Refine your synthetic data generation skills through hands-on practice, solidifying your understanding.
Browse courses on
Synthetic Data Generation
Show steps
-
Review different synthetic data generation techniques
-
Implement synthetic data generation techniques
-
Evaluate the quality of the synthetic data
-
Apply synthetic data in a practical scenario
Implement transfer learning to solve regression problem
Show steps
Sharpen your transfer learning skills by applying it to a practical problem, reinforcing your understanding.
Browse courses on
Transfer Learning
Show steps
-
Identify a regression dataset
-
Load and preprocess the dataset
-
Select an appropriate pre-trained model
-
Fine-tune the model on the regression dataset
-
Evaluate the model's performance
Develop a prototype solution to a small data problem using transfer learning
Show steps
Demonstrate your mastery by building a practical solution, applying your knowledge of transfer learning.
Browse courses on
Transfer Learning
Show steps
-
Identify a small data problem
-
Design a solution using transfer learning
-
Implement the solution
-
Test and evaluate the solution
-
Document your prototype
Review concepts of probability and statistics
Show steps
Strengthen your foundation in probability and statistics, enhancing your comprehension of small data concepts.
Browse courses on
Probability
Show steps
Show steps
Show steps
- Review lecture notes or textbooks on probability and statistics
- Solve practice problems and exercises
Brush up on intermediate Python programming
Show steps
Sharpen your Python skills, ensuring you have the technical foundation to excel in this course.
Browse courses on
Python Programming
Show steps
Show steps
Show steps
- Review Python syntax and data structures
- Practice coding exercises and challenges
Engage in peer discussions on small data strategies
Show steps
Collaborate with peers, exchange perspectives, and deepen your understanding of small data strategies.
Show steps
Show steps
Show steps
- Join a peer study group or online forum
- Participate in discussions on small data strategies
- Share your knowledge and experiences with peers
- Learn from others' insights and perspectives
Five other activities
Expand to see all activities and additional details
Show all eight activities
Explore case studies on successful transfer learning applications
Show steps
Gain insights from real-world examples, solidifying your comprehension of transfer learning in practice.
Browse courses on
Transfer Learning
Show steps
Show steps
Show steps
- Find case studies on successful transfer learning applications
- Analyze the case studies, identifying key strategies and techniques
- Summarize your findings and discuss them with peers
Explore online resources and tutorials on small data applications
Show steps
Expand your knowledge by exploring additional resources, deepening your comprehension of small data applications.
Show steps
Show steps
Show steps
- Search for online resources and tutorials on small data
- Review and explore the materials
- Summarize your findings and share them with peers
Practice generating synthetic data using various techniques
Show steps
Refine your synthetic data generation skills through hands-on practice, solidifying your understanding.
Browse courses on
Synthetic Data Generation
Show steps
Show steps
Show steps
- Review different synthetic data generation techniques
- Implement synthetic data generation techniques
- Evaluate the quality of the synthetic data
- Apply synthetic data in a practical scenario
Implement transfer learning to solve regression problem
Show steps
Sharpen your transfer learning skills by applying it to a practical problem, reinforcing your understanding.
Browse courses on
Transfer Learning
Show steps
Show steps
Show steps
- Identify a regression dataset
- Load and preprocess the dataset
- Select an appropriate pre-trained model
- Fine-tune the model on the regression dataset
- Evaluate the model's performance
Develop a prototype solution to a small data problem using transfer learning
Show steps
Demonstrate your mastery by building a practical solution, applying your knowledge of transfer learning.
Browse courses on
Transfer Learning
Show steps
Show steps
Show steps
- Identify a small data problem
- Design a solution using transfer learning
- Implement the solution
- Test and evaluate the solution
- Document your prototype
Career center
Learners who complete Small Datasets in Machine Learning will develop knowledge and skills
that may be useful to these careers:
Artificial Intelligence Engineer
Artificial Intelligence Engineer
An Artificial Intelligence Engineer designs, develops, and maintains artificial intelligence systems. This course can help you build a foundation in machine learning and learn techniques for working with small datasets. These skills are essential for success as an Artificial Intelligence Engineer.
Machine Learning Researcher
Machine Learning Researcher
A Machine Learning Researcher develops new machine learning algorithms and techniques. This course can help you build a foundation in machine learning and learn techniques for working with small datasets. These skills are essential for success as a Machine Learning Researcher.
Computer Vision Engineer
Computer Vision Engineer
A Computer Vision Engineer designs and develops computer vision systems. This course can help you build a foundation in machine learning and learn techniques for working with small datasets. These skills are essential for success as a Computer Vision Engineer.
Machine Learning Engineer
Machine Learning Engineer
A Machine Learning Engineer is responsible for developing and implementing machine learning models to solve business problems. This course can help you build a foundation in machine learning and learn techniques for working with small datasets. These skills are essential for success as a Machine Learning Engineer.
Natural Language Processing Engineer
Natural Language Processing Engineer
A Natural Language Processing Engineer designs and develops natural language processing systems. This course can help you build a foundation in machine learning and learn techniques for working with small datasets. These skills are essential for success as a Natural Language Processing Engineer.
Data Engineer
Data Engineer
A Data Engineer builds and maintains data pipelines. This course can help you build a foundation in machine learning and learn techniques for working with small datasets. These skills can be useful for a Data Engineer who wants to build and maintain data pipelines for machine learning applications.
Data Architect
Data Architect
A Data Architect designs and builds data systems. This course can help you build a foundation in machine learning and learn techniques for working with small datasets. These skills can be useful for a Data Architect who wants to design and build data systems for machine learning applications.
Data Scientist
Data Scientist
A Data Scientist uses data to solve business problems. This course can help you build a foundation in machine learning and learn techniques for working with small datasets. These skills are essential for success as a Data Scientist.
Software Engineer
Software Engineer
A Software Engineer designs, develops, and maintains software applications. This course can help you build a foundation in machine learning and learn techniques for working with small datasets. These skills can be useful for a Software Engineer who wants to work on machine learning projects.
Business Analyst
Business Analyst
A Business Analyst uses data to analyze business problems and identify opportunities for improvement. This course can help you build a foundation in machine learning and learn techniques for working with small datasets. These skills can be useful for a Business Analyst who wants to use machine learning to analyze business data.
Robotics Engineer
Robotics Engineer
A Robotics Engineer designs and develops robots. This course can help you build a foundation in machine learning and learn techniques for working with small datasets. These skills can be useful for a Robotics Engineer who wants to design and develop robots that use machine learning.
Quantitative Analyst
Quantitative Analyst
A Quantitative Analyst uses mathematical and statistical models to analyze financial data. This course can help you build a foundation in machine learning and learn techniques for working with small datasets. These skills can be useful for a Quantitative Analyst who wants to use machine learning to analyze financial data.
Product Manager
Product Manager
A Product Manager develops and manages software products. This course can help you build a foundation in machine learning and learn techniques for working with small datasets. These skills can be useful for a Product Manager who wants to develop and manage machine learning products.
Consultant
Consultant
A Consultant provides advice and guidance to businesses. This course can help you build a foundation in machine learning and learn techniques for working with small datasets. These skills can be useful for a Consultant who wants to provide advice and guidance to businesses on how to use machine learning.
Teacher
Teacher
A Teacher teaches students about a particular subject. This course can help you build a foundation in machine learning and learn techniques for working with small datasets. These skills can be useful for a Teacher who wants to teach students about machine learning.
For more career information including salaries, visit:
OpenCourser.com/course/mz2dsw/small
Reading list
We've selected ten books
that we think will supplement your
learning. Use these to
develop background knowledge, enrich your coursework, and gain a
deeper understanding of the topics covered in
Small Datasets in Machine Learning.
Provides a practical guide to machine learning for non-experts, offering hands-on experience with real-world datasets.
Offers a practical guide to machine learning using popular Python libraries, covering key concepts and providing hands-on experience with real-world datasets.
Focuses on deep learning techniques and their implementation in Python, providing practical insights for applying these methods to real-world problems.
This widely used textbook offers a comprehensive introduction to machine learning concepts and algorithms, providing a valuable reference for understanding the fundamentals.
This widely used textbook offers a comprehensive introduction to reinforcement learning, providing a solid foundation for understanding this important area of machine learning.
Save
Provides an overview of generative adversarial networks (GANs), exploring their principles, applications, and challenges.
Offers a rigorous treatment of probability theory from a machine learning perspective, providing a strong foundation for understanding probabilistic models.
Provides a solid foundation in probability and statistics, which are essential concepts for understanding the theory behind machine learning algorithms.
Provides a comprehensive introduction to convex optimization, which fundamental technique used in machine learning for solving optimization problems.
Save
This comprehensive textbook offers an in-depth coverage of deep learning techniques and architectures, providing advanced insights into the field.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/mz2dsw/small
Share
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Effort
4 weeks
Via
Udacity
Instructor
Matt Swaffer
Language
English
Good to know
Offers an introduction to small data, transfer learning, and synthetic data, which are core concepts in data science
Taught by Matt Swaffer, who has extensive experience in data science and machine learning, ensuring high-quality instruction
Practical course with a project component, allowing learners to apply their knowledge and skills to real-world problems
Covers the basics of transfer learning and synthetic data generation techniques, making it suitable for beginners in these areas
Provides a solid foundation for learners who want to explore small data problems and solutions
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