Top 5 Machine Learning Algorithms Every Student Should Know Chad Readey
For data science students like Chad Readey, understanding machine learning is more than just an academic requirement—it’s a gateway to solving real-world problems.
For data science students like Chad Readey, understanding machine learning is more than just an academic requirement—it’s a gateway to solving real-world problems. Whether it’s predicting MLB pitches or building business insights from raw data, mastering a few key algorithms can unlock powerful opportunities. Here are the top five machine learning algorithms every aspiring data scientist should know, based on practical experience and academic insight.
1. Logistic Regression
Despite its name, logistic regression is primarily used for classification problems, not regression. For Chad Readey, this algorithm played a pivotal role in his MLB pitch prediction project. It allows students to predict binary outcomes—such as “yes” or “no,” “hit” or “miss”—based on input variables.
Logistic regression is particularly useful because it introduces the concept of probability and decision thresholds. It’s also a great stepping stone to more complex classification models. By applying logistic regression, students learn how to evaluate model performance using metrics like accuracy, precision, recall, and AUC-ROC curves.
2. Decision Trees
Decision trees are highly intuitive models that mirror human decision-making processes. They divide data into branches based on conditions until they reach a conclusion. Chad appreciates decision trees for their visual clarity and ease of interpretation—qualities that make them ideal for both academic presentations and stakeholder discussions.
These models help students understand how data splits affect outcomes, and how overfitting can become a concern. They also pave the way for learning ensemble methods like Random Forests and Gradient Boosting Machines.
3. k-Nearest Neighbors (k-NN)
The k-Nearest Neighbors algorithm is one of the simplest yet surprisingly powerful techniques in supervised learning. It classifies a new data point based on the majority class among its “k” closest data points in the training set.
Chad sees k-NN as a hands-on way to grasp distance metrics, feature scaling, and the importance of data distribution. It also reinforces the value of model evaluation and tuning—as the choice of “k” significantly influences accuracy. While not ideal for very large datasets, k-NN remains a great starting point for understanding instance-based learning.
4. Support Vector Machines (SVM)
Support Vector Machines are a bit more mathematically complex but offer excellent performance in classification problems, especially when data is not linearly separable. SVM works by finding the hyperplane that best separates different classes in a dataset.
For students like Chad, learning SVM introduces the power of kernel functions, margins, and optimization techniques. Though computationally expensive, SVM models can achieve high accuracy and are especially useful in text classification and image recognition problems.
5. Linear Regression
Linear regression is often the first algorithm students encounter, and for good reason. It models the relationship between a dependent variable and one or more independent variables using a straight line. It’s a foundational concept that sets the stage for other forms of regression and optimization.
Chad Readey’s journey in data science illustrates how even foundational algorithms can lead to impactful projects when understood well. From predicting sports outcomes to interpreting complex datasets, these five machine learning algorithms are essential tools for every student stepping into the world of data.
For any aspiring data scientist, building a strong foundation in these algorithms is more than academic—it's a strategic investment in real-world readiness.