What Are the Essential Steps and Types of Machine Learning?

What are the steps of machine learning?

Executing a machine learning task involves five basic steps:

Collect data: Whether from Excel, Access, text files, etc., gathering past data forms the foundation for future learning. Greater diversity, density, and volume of relevant data improve learning prospects.

Prepare data: The quality of any analysis depends on the data used. Time must be spent assessing data quality and addressing issues such as missing values and outliers.

Train model: This step involves selecting an appropriate algorithm and representing data as a model. Cleaned data are split into training and testing sets (ratio depends on prerequisites); the training set builds the model, while the testing set serves as a reference.

Evaluate model: The second part of the data (testing set) is used to assess accuracy. This step determines the precision of the chosen algorithm based on results. A better test checks performance on data never seen during model construction.

Improve model: This may involve choosing a completely different model or adding more variables to increase efficiency, highlighting why extensive time is spent on data collection and preparation.

Regardless of the model, these five steps can be used to construct techniques, and you will see them appear in every model when discussing algorithms.

What types of machine learning algorithms exist?

Supervised learning / predictive models

Predictive models aim to forecast future outcomes based on historical data. They start with clear instructions about what to learn and how. This class of algorithms is called supervised learning. For example, marketing firms use it to identify customers likely to churn, and it can predict earthquakes, tornadoes, or insurance values. Common algorithms include k‑Nearest Neighbors, Naïve Bayes, decision trees, and regression.

Unsupervised learning / descriptive models

Unsupervised learning trains descriptive models without predefined targets or feature importance. Retailers may use it to discover product bundles frequently bought together, while pharma can predict diseases that co‑occur with diabetes. An example algorithm is K‑means clustering.

Reinforcement learning (RL)

Reinforcement learning trains machines to make decisions based on business needs, aiming to maximize efficiency. An RL agent continuously learns from its environment and applies accumulated knowledge to solve problems, reducing the need for extensive human expertise. The Markov Decision Process is an example of an RL algorithm.

Important note: A subtle difference exists between supervised learning and reinforcement learning. RL learns through interaction with the environment, whereas supervised learning relies on external guidance. An autonomous vehicle illustrates RL by continuously deciding routes and speeds, while supervised learning might simply predict fare between two locations.

What are the applications of machine learning?

Understanding machine learning applications is fascinating. Companies like Google and Facebook use it extensively to deliver targeted ads. Some notable applications include:

Banking and financial services: ML predicts which customers may default on loans or credit cards, helping institutions identify eligible borrowers.

Healthcare: Combining patient symptoms with historical data aids in diagnosing critical diseases such as cancer.

Retail: ML identifies fast‑moving products, slow‑moving items, and product bundles, informing inventory decisions and loyalty initiatives.

References: https://www.analyticsvidhya.com/blog/2015/06/machine-learning-basics/