AI Algorithms: Practical Business Applications of Machine Learning and Deep Learning (Part 1)

In this edition, we'll explore detailed use cases of AI technologies, focusing on the business applications of machine learning and deep learning algorithms. First, we’ll provide a non-technical overview of these algorithms and then illustrate their practical utility with industry examples.

• Unsupervised machine learning processes data without predefined output variables, aiming to group similar data into clusters based on inherent patterns. Techniques like “k-means clustering” and “Gaussian mixture models (GMM)”, are used in segmenting customer behavior patterns. The approach also mitigates human bias in segmentation decisions and adapts to dynamic market changes, making it scalable and beneficial for uncovering hidden data patterns. If you want to learn more about “k-means clustering" refer to this video, and if you want to know more about “Gaussian mixture models (GMM), please refer to this video.
• Supervised learning methods utilize input data paired with explicit output variables for making predictions. It emphasizes the use of labeled data to train algorithms, enabling them to learn relationships between inputs and outputs. Common algorithms are linear regression, logistic regression, and tree-based methods (such as decision trees, Random Forest, and Gradient Boosting). Let's talk about these in detail:

1. A “Linear algorithm” is a common approach in supervised learning where the relationship between input variables and the target variable is modeled as a linear combination of the input features. Examples include Linear Regression for continuous outcomes and Linear SVM (Support Vector Machine) for classification tasks. These methods are simple and easy to understand, making them useful in many situations where the data shows clear linear patterns. These algorithms are pivotal in the finance industry for tasks such as forecasting asset prices, risk management through portfolio optimization, and analyzing economic trends based on historical data.
2. The "Logistic regression” algorithm is a popular method in supervised learning for deciding between two outcomes. It calculates the likelihood of one outcome using a special function that changes the results to be between 0 and 1. Estimating coefficients shows how input factors connect to the likelihood of an outcome, especially when the data is separated into groups. Logistic regression finds extensive application in healthcare for predicting patient outcomes, such as disease diagnosis based on medical tests or determining the likelihood of a patient responding positively to a treatment regimen. The main difference is that logistic regression predicts probabilities for binary classification, while linear regression models the relationship between variables for continuous outcomes.
3. “Tree-based algorithms” are like following a flowchart to make decisions. They break down the data into smaller and smaller groups based on key features, aiming to predict outcomes accurately. Examples include Decision Trees (single trees), Random Forests (many trees working together), and Gradient Boosting (trees that learn from previous mistakes). They're good at handling complex relationships in data and are easy to understand, showing which features are most important for making predictions.

Tree-based algorithms find extensive application in industries like retail and e-commerce. For instance, they are used to predict customer behavior, segment markets for targeted marketing campaigns, and optimize inventory management by forecasting demand patterns based on historical sales data and other relevant features. This helps businesses enhance operational efficiency and tailor their strategies to maximize profitability.

In the next edition, we will discuss reinforcement learning algorithms and deep learning.