Overview
Machine learning is revolutionizing the way we interact with technology and utilize data daily. This article aims to offer a comprehensive introduction to machine learning, covering its fundamentals, different types of algorithms, and real – world applications in various sectors such as healthcare, banking, and retail. Additionally, we’ll clear up the confusion surrounding related terms like artificial intelligence, deep learning, statistics, and data mining.
What is Machine Learning?
To get a better understanding, let’s consider a simple experiment. A group of 10 people, completely new to analytics and unaware of machine learning, were asked what they thought it was. Responses ranged from “learning from machines” to “learning through online courses”. In reality, machine learning involves using intelligent techniques (by developing algorithms) to handle large – scale data and extract actionable insights. For example, when you search on Google, machine learning helps Google sift through a vast amount of data to serve you the most relevant search results.
Distinguishing Machine Learning from Related Concepts
Artificial Intelligence (AI): AI is about programming a computer to make rational decisions. Machine learning is a subset of AI, where the machine learns from past data. For instance, AI might include programs that monitor parameters in a process, while machine learning uses algorithms like Naïve Bayes and Support Vector Machine.
Statistics: Statistics is a branch of mathematics dealing with data analysis. In machine learning, statistical concepts are used. For example, the Naïve Bayes algorithm in machine learning uses Bayes’ theorem (conditional probability) to classify emails as spam or important.
Deep Learning: It is associated with the Artificial Neural Network (ANN) algorithm in machine learning, which mimics the human brain. ANN requires a large amount of data and can handle multiple outputs flexibly.
Data Mining: While data mining focuses on searching for specific information, machine learning is about performing a given task. Teaching someone to dance is like machine learning, and using someone to find dance centers is like data mining.
Teaching Machines and Machine Learning Steps
Teaching machines is a structured process. The overall process can be divided into three parts, and the success of a machine depends on how well it generalizes data and applies its learning. There are also five basic steps in a machine – learning task:
- Collecting data: Gathering relevant past data from various sources forms the foundation for learning.
- Preparing the data: Ensuring data quality by handling missing values and outliers through exploratory analysis.
- Training a model: Selecting an appropriate algorithm and splitting data into training and test sets.
- Evaluating the model: Using the test data to check the accuracy of the model.
- Improving the performance: Potentially changing the model or adding more variables for better efficiency.
Types of Machine Learning Algorithms
Supervised Learning / Predictive models: These models predict future outcomes based on historical data and receive clear instructions. For example, in predicting customer churn or natural disaster likelihood for insurance purposes, algorithms like Nearest Neighbour, Naïve Bayes, and Decision Trees are used.
Unsupervised learning / Descriptive models: Used when no specific target is set, like when a retailer wants to find product combinations that customers buy frequently. The K – means Clustering Algorithm is an example here.
Reinforcement learning (RL): The machine trains itself to make decisions based on the environment to maximize efficiency. Self – driving cars use RL to make decisions like route – taking and speed – setting.
Applications of Machine Learning
Banking & Financial services: Predicting loan and credit card defaults and identifying eligible customers.
Healthcare: Diagnosing diseases like cancer by comparing patient symptoms with past data.
Retail: Identifying fast – moving and slow – moving products and product combinations for loyalty initiatives.
In conclusion, this article has provided a basic understanding of machine learning, its related concepts, the learning process, algorithms, and applications. We hope it helps you get a better grip on this exciting field. Feel free to share your thoughts and questions in the comments.
