Introduction_to_Machine_Learning_Models_Presentation.pptx
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The document provides an overview of machine learning models, defining them as algorithms that learn from data and make predictions, categorized into supervised, unsupervised, and reinforcement learning. It highlights various examples of each model type, such as linear regression and decision trees for supervised learning, and k-means clustering for unsupervised learning. Additionally, it outlines applications in fields like healthcare for disease prediction, finance for fraud detection, and retail for customer segmentation.
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![Introduction to Machine
Learning Models
An Overview of Popular ML Models
Presented by: [Your Name]
Institution: [Your Institution]
Date: [Today's Date]](https://image.slidesharecdn.com/introductiontomachinelearningmodelspresentation-241111212638-b829503d/85/Introduction_to_Machine_Learning_Models_Presentation-pptx-1-320.jpg)
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Introduction_to_Machine_Learning_Models_Presentation.pptx
- 1. Introduction to Machine Learning Models An Overview of Popular ML Models Presented by: [Your Name] Institution: [Your Institution] Date: [Today's Date]
- 2. What are Machine Learning Models? • - Definition: Algorithms that enable systems to learn and make predictions from data. • - Categories: Supervised, Unsupervised, and Reinforcement Learning. • - Importance: Automates decision-making and improves accuracy with data.
- 3. Types of Machine Learning Models • - **Supervised Learning Models**: e.g., Linear Regression, Decision Trees. • - **Unsupervised Learning Models**: e.g., K- Means Clustering, PCA. • - **Reinforcement Learning Models**: e.g., Q- Learning, Deep Q Networks.
- 4. Applications of Machine Learning Models • - **Healthcare**: Predicting diseases, drug discovery. • - **Finance**: Fraud detection, algorithmic trading. • - **Retail**: Customer segmentation, recommendation systems.