Learn what machine learning is by defining foundational concepts like supervised and unsupervised learning.
- [Voiceover] Machine learning is based…on self-learning or self-improving algorithms.…In machine learning, a computer starts with a model,…and continues to enhance it through trial and error.…It can then provide meaningful insight…in the form of classification, prediction, and clustering.…There are two types of machine learning.…One is supervised and the other is unsupervised.…
Supervised learning is reinforced by feedback…in the form of training data.…Suppose that you have a basket of apples and oranges,…you'd like to separate them…into two distinct groups of fruits.…There is apples and oranges.…In the supervised learning environment,…you already have training data…which can tell your machine learning algorithm…what fruit belongs to which group…after it makes its decision.…In this scenario the algorithm already knows…that there are two labels to be used…in its attempts to separate the fruits.…
Therefore, this process is accumulative classification,…a concept used in the data mining and analytics domain.…In the unsupervised learning environment,…
Jungwoo Ryoo is a professor of information science and technology at Penn State. Here he reviews the history of data science and its subfields, explores the marketplaces for these fields, and reveals the five main skills areas: data mining, machine learning, natural language processing (NLP), statistics, and visualization. This leads to a discussion of the five biggest career opportunities, the six leading industry-recognized certifications available, and the most exciting emerging technologies. Along the way, Jungwoo discusses the importance of ethics and professional development, and provides pointers to online resources for learning more.
- A history of data science
- Why data analytics is important
- How data science is used in fraud detection, disease control, network security, and other fields
- Data science skills
- Data science roles
- Data science certifications
- The future of data science
Skill Level Beginner
Insights on Data Science: Lillian Piersonwith Lillian Pierson, P.E.23m 51s Intermediate
1. Define Data Science
6. Future of Data Science
- Mark as unwatched
- Mark all as unwatched
Are you sure you want to mark all the videos in this course as unwatched?
This will not affect your course history, your reports, or your certificates of completion for this course.Cancel
Take notes with your new membership!
Type in the entry box, then click Enter to save your note.
1:30Press on any video thumbnail to jump immediately to the timecode shown.
Notes are saved with you account but can also be exported as plain text, MS Word, PDF, Google Doc, or Evernote.