Machine Learning has wide business applications across various domains. Most popularly Machine Leaning is used in recommendation engines, fraud detection, even supply chain, inventory planning, image recognition, Amazon’s Alexa and much more. While the algorithms are stemmed from traditional data analytics, it’s the approach that makes machine learning palatable in the data age. Machine Learning focuses on prediction and can make data analysis efficient by looking at humungous amounts of data simultaneously. It monitors accuracy over statistical significance.
In traditional analytics, what we call as a target variable is referred to as label in machine learning. In Supervised Learning inductive inference is used to infer a predictive relationship between data points and labels.
Examples: – Regression Algorithms
- Classification Algorithms
- Decision Tree Learning
- SVM
- Naïve Bayes Classification
This is typically used a data mining technique to discern various patterns or structure in the data. It is not directed by any label. Example: Clustering.
This is a branch of Artificial Intelligence allowing systems to automatically determine (using trial and error) the ideal behavior within a specific context, to maximize its performance and make as accurate decisions as possible. Simple reward feedback also known as the reinforcement signal helps in the learning behavior and hence controlling a system. Example: Markov Decision Process