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MACHINE LEARNING.pptx
- 3. DECISION TREES ○ Decision Tree is a Supervised learning technique that can be used for both classification and Regression problems, but mostly it is preferred for solving Classification problems. It is a tree-structured classifier, where internal nodes represent the features of a dataset, branches represent the decision rules and each leaf node represents the outcome. ○ In a Decision tree, there are two nodes, which are the Decision Node and Leaf Node. Decision nodes are used to make any decision and have multiple branches, whereas Leaf nodes are the output of those decisions and do not contain any further branches. ○ The decisions or the test are performed on the basis of features of the given dataset. MACHINELEARNINGALGORITHM
- 5. EXAMPLE:
- 7. NEURALNETWORKS ❏ Artificial neural networks (ANNs) or simulated neural networks (SNNs), are a subset of machine learning and are at the heart of deep learning algorithms. ❏ Inspired by the structure and function of the human brain. ❏ Human brain is arguably the most powerful computational engine known today. ❏ Deep learning was conceptualized by Geoffrey Hinton . ❏ He created the concept of a "neural network". ❏ A deep learning algorithm structured similar to the organization of neurons in the brain.
- 8. WHATIS NEURONINBIOLOGY? ❏ Neuron by itself is useless. ❏ Instead, you require networks of neurons to generate any meaningful functionality. ❏ Neurons function by receiving and sending signals. ❏ The neuron’s dendrites receive signals and pass along those signals through the axon. ❏ The dendrites of one neuron are connected to the axon of another neuron. ❏ These connections are called synapses.
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- 11. CONTD… So how does it compute prediction? Each connection between neurons is associated with a weight. This weight dictates the importance of the input value. Each neuron has an Activation Function. Once a set of input data has passed through all the layers of the neural network, it returns the output data through the output layer.