Deep learning is a subset of machine learning which in turn is a subset of artificial intelligence. Artificial intelligence is a technique that enables a machine to mimic human behaviour. Machine learning is a technique to achieve AI through algorithms trained with data and finally, deep learning is a type of machine learning inspired by the structure of the human brain in terms of deep learning this structure is called an artificial neural network let's understand deep learning better and how it's different from machine learning. We create a machine that could differentiate between tomatoes and cherries if done using machine learning we'd have to tell the Machine the features based on which the two can be differentiated these features could be the size and the type of stem on them with deep learning, on the other hand, the features are picked out by the neural network without human intervention, of course, that kind of independence comes at the cost of having a much higher volume of data to train our machine.
Ensemble Learning
Using multiple learning algorithms together for the same task for better predictions than individual learning model.
Neural Networks
Neural networks are a set of algorithms, modelled loosely after the human brain, that is designed to recognize patterns. They interpret sensory data through a kind of machine perception, labelling or clustering raw input. The patterns they recognize are numerical, contained in vectors, into which all real-world data, be it images, sound, text or time series, must be translated.
Neural networks help us cluster and classify. You can think of them as a clustering and classification layer on top of the data you store and manage. They help to group unlabeled data according to similarities among the example inputs, and they classify data when they have a labelled dataset to train on. (Neural networks can also extract features that are fed to other algorithms for clustering and classification; so you can think of deep neural networks as components of larger machine-learning applications involving algorithms for reinforcement learning, classification and regression.)
Regularization
Rule System
Regression
Regression algorithms fall under the family of Supervised Machine Learning algorithms which is a subset of machine learning algorithms. One of the main features of supervised learning algorithms is that they model dependencies and relationships between the target output and input features to predict the value for new data. Regression algorithms predict the output values based on input features from the data fed in the system. The go-to methodology is the algorithm builds a model on the features of training data and using the model to predict the value for new data.
Naive Bayes
The Naive Bayes Classifier technique is based on the so-called Bayesian theorem and is particularly suited when the dimensionality of the inputs is high. Despite its simplicity, Naive Bayes can often outperform more sophisticated classification methods.
Decision Tree
A decision tree is a flowchart-like tree structure where an internal node represents feature(or attribute), the branch represents a decision rule, and each leaf node represents the outcome. The topmost node in a decision tree is known as the root node. It learns to partition on the basis of the attribute value. It partitions the tree in recursively manner called recursive partitioning. This flowchart-like structure helps you in decision making. It's visualization like a flowchart diagram which easily mimics the human-level thinking. That is why decision trees are easy to understand and interpret.
Dimensionality Reduction
Instance-Based
Clustering
Clustering is the task of dividing the population or data points into a number of groups such that data points in the same groups are more similar to other data points in the same group than those in other groups. In simple words, the aim is to segregate groups with similar traits and assign them into clusters.
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