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, there is no training data. In this case the machine learning algorithm solely depends on clustering and keep enhancing its algorithm without external feedback.
Jungwoo Ryoo is a professor of information science and technology at Penn State. Here he reviews the history of data science and analytics, explores which markets are using big data the most, 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 four 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 analytics is important
- How data science is used in social media, climate research, and more
- Data science skills
- Data science certifications
- The future of big data