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ML 1.9 DECISION TREE MODEL OF LEARNING

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Decision Tree of Learning A Decision Tree is a supervised learning model used for classification and regression . A decision tree learns by splitting data into smaller and purer groups using simple rules , until it can confidently make a prediction. It makes decisions by splitting data into subsets based on feature values, forming a tree-like structure of rules. It learns by asking a sequence of simple questions about the data and splitting it step by step, like a flowchart, until it reaches a decision. Key Components of a Decision Tree: ·         Root Node : The topmost node representing the entire dataset, which is then split into subsets. ·         Decision Nodes : Intermediate nodes that represent decisions based on specific features, leading to further splits. ·         Leaf Nodes (Terminal Nodes) : Nodes that represent the final output or decision, containing no furthe...
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ML 1.8 Association Apriori algorithm

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ML 1.7 Some canonical learning problems

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  v   Some canonical learning problems: - Ø In machine learning, canonical learning problems are standard tasks that serve for developing and evaluating algorithms. Ø These canonical problems provide a framework for understanding and applying machine learning techniques across various domains. Ø These problems help in understanding the strengths and limitations of various approaches. Some of the most common canonical learning problems include:   1.      Classification : Assigning inputs to predefined categories. For example, determining whether an email is spam or not.   Binary Classification: trying to predict a simple yes/no response. For instance, predict whether a user review of the newest Apple product is positive or negative about the product.   Multiclass Classification: trying to put an example into one of a number of classes. For instance, predict whether a news story is about entertainment, sports, politics, re...
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ML 1.6 Applications of Machine Learning

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  v   Applications of Machine Learning: - 1.       Image Recognition 2.       Speech Recognition 3.       Traffic prediction 4.       Product recommendations 5.       Self-driving cars 6.       Email Spam and Malware Filtering 7.       Virtual Personal Assistant 8.       Online Fraud Detection 9.       Stock Market trading 10.   Medical Diagnosis 11.   Automatic Language Translation   1)   Image Recognition: - Ø Image recognition is one of the most common applications of machine learning. It is used to identify objects, persons, places, digital images, etc. The popular use case of image recognition and face detection is,  Automatic friend tagging suggestion .   Ø Facebook provides us a feature...
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