Machine Learning: Introduction
Capturing the Essence of Machine Learning
What is Machine Learning ?
Machine learning is the field of study that gives computers the ability to learn without being explicitly programmed. — Arthur Samuel
Machine Learning represents an intersection of different disciplines, combining elements like Linear Algebra, Statistics, and Optimization Theories to create a rich blend of knowledge.
In Technical terms: It's like making a smoothie with Linear Algebra, Statistics, and a dash of Optimization Theories.
In theoretical words: It's the study of algorithms designed to improve performance (P) at specific tasks(T) with experience(E). These tasks range from classification (e.g., identifying spam emails), regression (predicting housing prices), to clustering (grouping similar customer profiles).
T. Tasks- refer to the specific problems or challenges that machine learning models are designed to address: Classification, Regression and Clustering
P. Performance - defined as the capability of the algorithm to effectively and accurately execute its designated tasks. Metrics are used to measure performances.
E. Experience - refers to the data(or history) from which the algorithms learn and derive insights.
Why Machine Learning ?
Machine Learning marks a pivotal shift in problem-solving techniques:
Traditional Approach: Previously, problem-solving in computing involved programming explicit rules along with the data to generate answers. A classic example is using fixed rules to identify spam emails based on specific keywords.
Machine Learning Approach: Now, we input data and the desired outcomes into algorithms, which then learn to discern patterns and rules independently. An example of this is providing a machine learning algorithm with a dataset of emails tagged as 'spam' or 'not spam,' allowing it to learn how to classify new emails accurately.
Conclusion
Fundamentally, machine learning revolves around teaching algorithms to learn from data (experience) to perform tasks like classification, regression, or clustering with greater efficiency (performance).