prabath03dot / zero-to-hero-artificial-intelligance

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Artificial Intelligance

Please read the Guidelines for contributors

Tables of content

• Machine Learning
  • Supervised Machine Learning
    • Regression
      • Linear Regression
      • Poisson Regression
      • Support Vector Regression
    • Classificatin
      • Logistic Regression
      • Neural Network
      • Decision Tree
      • Naive Bayes Classifier
  • Unsupervised Machine Learning
    • Clustering
    • High Dimension Data
    • Generative Models
  • Reinforcement Machine Learning
  • Semi-supervised Machine Learning
• Deep Learning
  • Feed Forward Neural Network
  • Radial Basis Function Neural Network
  • Multi-layer Perceptron
  • Convolutinal Neural Network
  • Recurrent Neural Network
  • Modular Neural Network
  • Sequence to Sequence Model
• Neural Networks
  • Feed Forward Neural Network
  • Radial Basis Function Neural Network
  • Multi-layer Perceptron
  • Convolutinal Neural Network
  • Recurrent Neural Network
  • Modular Neural Network

Machine Learning

Topic	Description	Example / Tutorial (link)
Supervised Machine Learning	It is known as supervised learning when we train the algorithm by explicitly supplying the labels. The supplied dataset is used to train the model in this sort of method. The model is shaped like this. Y=f(X) where x is the input variable, y is the output variable, and f(X) is the hypothesis.	Tutorials Point
Regression	The output variable in regression is numerical(continuous), which means we train the hypothesis(f(x)) to produce continuous output(y) given the input data (x). The regression approach is employed in the prediction of quantities, sizes, values, and other things since the output is influenced by the real number.	projecctpro
Linear Regression	The most basic type of regression method. In this case, we have two variables: one independent, which is the predicted output, and one dependent, which is the feature. The relationship between these two variables is believed to be linear, which means that a straight line may be used to divide them. The goal of this function is to find the line that divides these two variables with as little error as feasible. The error is calculated as the total of the Euclidean distances between the points and the line.When there is just one independent variable, it is referred to as simple linear regression and is denoted by:Y = b0+b1x1+c Multiple linear regression is used when there is more than one independent variable. and is provided by: Y =bo+b1x1+b2x2+b3x3... In all equations above, y is the dependent variable and x is the independent variable.	Try out this tutorial
Poisson Regression	It is based on the Poisson distribution, in which the dependent variable(y) has a value of a tiny, non-negative integer such as 0,1,2,3,4, and so on.Assuming that a big count will not occur on a regular basis. Poisson regression is similar to logistic regression in this respect, except the dependent variable is not restricted to a single value.	Try out this tutorial
Support Vector Regression	As the name implies, Support Vector Regression is a regression algorithm that supports both linear and non-linear regressions. This approach is based on the Support Vector Machine idea. SVR varies from SVM in that SVM is a classifier that predicts discrete categorical labels, whereas SVR is a regressor that predicts continuous ordered variables.The goal of basic regression is to decrease the error rate, however the goal of SVR is to fit the error within a particular threshold, which means that the task of SVR is to approximate the best value within a given margin termed ε- tube.	Youtube Tutorial for Support Vector Machine
Classificatin	The output variable in classification is discrete. To put it another way, we train the hypothesis(f(x)) to produce discrete output(y) for the input data (x). A class can also be used to describe the output. Using the previous example of home pricing, instead of finding the precise amount, we can use classification to forecast whether the house price will be above or below. As a result, we have two classes: one for when the price is above and one for when the price is below. Classification is used in speech recognition, image classification, NLP, etc.	Calssification Tuutorial in IBM deveoper site
Logistic Regression	It is a categorization algorithm of some sort. It is used to calculate the discrete value given the independent variables. It aids in determining the likelihood of occurrence of a function by employing a logit function. The hypothesis of these approaches' output(y) ranges from 0 to 1.A logistic regression function is given by:p=1/(1+e^-y) where y is the equation on line. The value is scaled between 0 and 1 as a result of this function. A sigmoid function is another name for this function.	YouTube tutorial for Logistic Regression

Deep Learning

Topic	Description	Example / Tutorial (link)

Neural Networks

Topic	Description	Example / Tutorial (link)
Radial Basis Function Neural Network	Radial basis function (RBF) networks are a commonly used type of artificial neural network for function approximation problems. Radial basis function networks are distinguished from other neural networks due to their universal approximation and faster learning speed. An RBF network is a type of feed forward neural network composed of three layers, namely the input layer, the hidden layer and the output layer.	Introduction to Radial Basis Function Neural Network
Feed Forward Neural Network	MLPs, or multilayer perceptrons, are the quintessential deep learning models. A feedforward network's purpose is to approximate f*. So, a classifier's output is y = f*(x). A feedforward network learns the parameters that result in the best function approximation.	Introduction for Feed Forward Neural Network