Machine learning is a data analytics technique that teaches
computers to do what comes naturally to humans and animals: learn from
experience. Machine learning algorithms use computational methods to “learn”
information directly from data without relying on a predetermined equation as a
model. The algorithms adaptively improve their performance as the number of
samples available for learning increases. Deep learning is a specialized form
of machine learning.
Why
Machine Learning Matters
1.
Computational finance, for credit scoring and algorithmic trading
2. Image processing and computer vision, for
face recognition, motion detection, and object detection
3. Computational biology, for tumor detection,
drug discovery, and DNA sequencing
4. Energy production, for price and load
forecasting
5. Automotive, aerospace, and manufacturing,
for predictive maintenance
6.
Natural language processing, for voice recognition applications
How Machine Learning Works
Machine learning uses two types of techniques: supervised
learning, which trains a model on known input and output data so that it can
predict future outputs, and unsupervised learning, which finds hidden patterns
or intrinsic structures in input data.
How Do You Decide Which Machine Learning
Algorithm to Use?
Choosing the right algorithm can seem overwhelming—there are dozens of
supervised and unsupervised machine learning algorithms, and each takes a
different approach to learning.There is no best method or one size fits all.
Finding the right algorithm is partly just trial and error—even highly
experienced data scientists can’t tell whether an algorithm will work without
trying it out. But algorithm selection also depends on the size and type of
data you’re working with, the insights you want to get from the data, and how
those insights will be used.
