Machine Learning Algorithms Powering the Future of AI Innovation

Fusion Software Institute

Technology

Machine Learning Algorithms

what is Machine Learning

Definition—Machine learning is a branch of artificial intelligence where systems learn from data and improve their performance without being explicitly programmed. How It Works – It uses algorithms to identify patterns in data and make predictions or decisions based on those patterns. Applications – Used in spam detection, product recommendations, speech recognition, fraud detection, self‑driving cars, and more.

What is Algorithm

An algorithm is a step-bystep set of instructions or rules designed to solve a problem or perform a specific task.

Why Algorithms Matter The selected algorithms form the backbone of many real‑world AI systems. They address tasks such as prediction, classification, clustering, and pattern recognition. Understanding strengths, weaknesses, and use cases helps in choosing the right method.

Supervised Learning Algorithms (Part 1)

Linear Regression – predicts continuous outcomes (e.g., sales forecasting, property values) . Logistic Regression – binary classification using sigmoid output (e.g., spam detection, fraud detection) . Decision Trees – intuitive tree-structured models for both regression and classification, but prone to overfitting. Random Forest – ensemble of decision trees for better accuracy & generalization

Supervised Learning Algorithms (Part 2)

Support Vector Machines (SVM) – separates classes via optimal hyperplane; strong performance in high‑dimensional data (e.g. image/text) . K‑Nearest Neighbors (KNN) – lazy, non‑parametric; classifies based on nearest neighbors; simple yet effective for small datasets. Naive Bayes – probabilistic model assuming feature independence; effective for text classification like spam or sentiment analysis .

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