1 / 7

Machine Learning Algorithms Powering the Future of AI Innovation

In today's data-rich world, machine learning (ML) serves as the driving force behind intelligent systems transforming industriesu2014from healthcare to finance to manufacturing. Fusion Institute recently highlighted the Top machine learning algorithms that are foundational to real-world AI applications and innovation.

SagarHeddurshetty
Download Presentation

Machine Learning Algorithms Powering the Future of AI Innovation

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Fusion Software Institute Technology Machine Learning Algorithms

  2. 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 recommendations, speech recognition, self‐driving cars, and more. spam detection, fraud product detection,

  3. What is Algorithm An algorithm is a step-by- step set of instructions or rules designed to solve a problem or perform a specific task.

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

  5. 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

  6. 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 independence; effective for text classification like spam or sentiment analysis . model assuming feature

  7. Kickstart Your Career in AI & ML! Learn from Fusion Software Institute – Pune’s Leading IT Training Institute Industry‐aligned curriculum Hands‐on projects with real‐world datasets 100% Placement Assistance Get placed in Top MNCs with packages starting at ₹4 LPA ? Contact Now: 7498992609 | 9503397273 ? www.fusion-institute.com

More Related