bhavuk0909/Building_CNN

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Updated almost 5 years ago

bhavuk0909/Building_CNN

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KNN_Classification

K-Nearest Neighbors is one of the most basic yet essential classification algorithms in Machine Learning. It belongs to the supervised learning domain and finds intense application in pattern recognition, data mining and intrusion detection.It is widely disposable in real-life scenarios since it is non-parametric, meaning, it does not make any underlying assumptions about the distribution of data (as opposed to other algorithms such as GMM, which assume a Gaussian distribution of the given data).

Apriori

The Apriori algorithm is a categorization algorithm. Some algorithms are used to create binary appraisals of information or find a regression relationship. Others are used to predict trends and patterns that are originally identified. Apriori is a basic machine learning algorithm which is used to sort information into categories. Sorting information can be incredibly helpful with any data management process. It ensures that data users are appraised of new information and can figure out the data that they are working with.

Kernel_SVM_Classification

Implementing Kernel SVM with Scikit-Learn is similar to the simple SVM.To train the kernel SVM, we use the same SVC class of the Scikit-Learn's svm library. The difference lies in the value for the kernel parameter of the SVC class. In the case of the simple SVM we used "linear" as the value for the kernel parameter. However, for kernel SVM you can use Gaussian, polynomial, sigmoid, or computable kernel.

Logistic_Regression_as_a_Neural_Network

Smile_Detector_Using_OpenCV

Natural_Language_Processing

NLP is a field in machine learning with the ability of a computer to understand, analyze, manipulate, and potentially generate human language.

Building_ANN

Simple_Linear_Regression

Linear Regression is a machine learning algorithm based on supervised learning. It performs a regression task. Regression models a target prediction value based on independent variables. It is mostly used for finding out the relationship between variables and forecasting.

Naive_Bayes_Classification

Naive Bayes classifiers are a collection of classification algorithms based on Bayes’ Theorem. It is not a single algorithm but a family of algorithms where all of them share a common principle, i.e. every pair of features being classified is independent of each other.

Classifying-Non-cat-vs-cat-using-deep-NN

Hierarchical_Clustering

Hierarchical clustering is one of the popular and easy to understand clustering technique. This clustering technique is divided into two types: Agglomerative & Divisive.

Decision_Tree_Regression

Decision Tree is a decision-making tool that uses a flowchart-like tree structure or is a model of decisions and all of their possible results, including outcomes, input costs and utility.Decision-tree algorithm falls under the category of supervised learning algorithms. It works for both continuous as well as categorical output variables.

Linear_Discriminant_Analysis

Linear Discriminant Analysis or Normal Discriminant Analysis or Discriminant Function Analysis is a dimensionality reduction technique which is commonly used for the supervised classification problems. It is used for modeling differences in groups i.e. separating two or more classes. It is used to project the features in higher dimension space into a lower dimension space.

Random_Forest_Classification

Random forest, like its name implies, consists of a large number of individual decision trees that operate as an ensemble. Each individual tree in the random forest spits out a class prediction and the class with the most votes becomes our model’s prediction.A large number of relatively uncorrelated models (trees) operating as a committee will outperform any of the individual constituent models.

Decision_Tree_Classification

Dividing efficiently based on maximum information gain is key to decision tree classifier. However, in real world with millions of data dividing into pure class in practically not feasible (it may take longer training time) and so we stop at points in nodes of tree when fulfilled with certain parameters (for example impurity percentage). We shall see this in coding exercise.

Kernel_PCA

PCA is a linear method. That is it can only be applied to datasets which are linearly separable. It does an excellent job for datasets, which are linearly separable. But, if we use it to non-linear datasets, we might get a result which may not be the optimal dimensionality reduction. Kernel PCA uses a kernel function to project dataset into a higher dimensional feature space, where it is linearly separable. It is similar to the idea of Support Vector Machines.

K-Means_Clustering

The k-means clustering algorithm assigns data points to categories, or clusters, by finding the mean distance between data points. It then iterates through this technique in order to perform more accurate classifications over time. Since you must first start by classifying your data into k categories, it is essential that you understand your data well enough to do this.

Random_Forest_Regression

A Random Forest is an ensemble technique capable of performing both regression and classification tasks with the use of multiple decision trees and a technique called Bootstrap Aggregation, commonly known as bagging. The basic idea behind this is to combine multiple decision trees in determining the final output rather than relying on individual decision trees.

Multiple_Linear_Regression

Multiple Linear Regression attempts to model the Relationship between two or more features and a response by fitting a linear equation to observed data. The steps to perform multiple linear Regression are almost similar to that of simple linear Regression. The Difference Lies in the Evalution. We can use it to find out which factor has the highest impact on the predicted output and now different variable relate to each other.

Principal_Component_Analysis

Principal Component Analysis (PCA) is a statistical procedure that uses an orthogonal transformation which converts a set of correlated variables to a set of uncorrelated variables. PCA is a most widely used tool in exploratory data analysis and in machine learning for predictive models. Moreover, PCA is an unsupervised statistical technique used to examine the interrelations among a set of variables. It is also known as a general factor analysis where regression determines a line of best fit.

Logistic_Regression

Logistic regression is basically a supervised classification algorithm. In a classification problem, the target variable(or output), y, can take only discrete values for given set of features(or inputs), X.Logistic regression becomes a classification technique only when a decision threshold is brought into the picture. The setting of the threshold value is a very important aspect of Logistic regression and is dependent on the classification problem itself.

Support_Vector_Machine_Classification

In machine learning, support vector machines (SVMs, also support vector networks) are supervised learning models with associated learning algorithms that analyze data used for classification and regression analysis. A Support Vector Machine (SVM) is a discriminative classifier formally defined by a separating hyperplane. In other words, given labeled training data (supervised learning), the algorithm outputs an optimal hyperplane which categorizes new examples.

Polynomial_Regression

Polynomial Regression is a form of linear regression in which the relationship between the independent variable x and dependent variable y is modeled as an nth degree polynomial. Polynomial regression fits a nonlinear relationship between the value of x and the corresponding conditional mean of y.

Spport_Vector_Regression

Support Vector Machine can be applied not only to classification problems but also to the case of regression. Still it contains all the main features that characterize maximum margin algorithm: a non-linear function is leaned by linear learning machine mapping into high dimensional kernel induced feature space. The capacity of the system is controlled by parameters that do not depend on the dimensionality of feature space.

Thompson_Sampling

Thompson Sampling (Posterior Sampling or Probability Matching) is an algorithm for choosing the actions that address the exploration-exploitation dilemma in multi-armed bandit problem. Actions are performed several times and are called exploration. It uses training information that evaluates the actions taken rather than instructs by giving correct actions. This is what creates the need for active exploration, for an explicit trial-and-error search for good behaviour. Based on the results of those actions, rewards (1) or penalties (0) are given for that action to the machine. Further actions are performed in order to maximize the reward that may improve future performance. Suppose a robot has to pick several cans and put in a container. Each time it puts the can to the container, it will memorize the steps followed and train itself to perform the task with better speed and precision (reward). If the Robot is not able to put the can in the container, it will not memorize that procedure (hence speed and performance will not improve) and will be considered as a penalty.

Upper_Confidence_Bound

The Upper Confidence Bound algorithm the kind of algorithm that helps us to perform exploitation and exploration together.At the start of the campaign, we don't know what is the best arm or ad. So we cannot distinguish or discriminate any arm or ad. So the UCB algorithm assumes they all have the same observed average value. Then the algorithm creates confidence bound for each arm or ad. So it randomly picks any of the arms or ads. Then two things can happen- the user clicks the ad or the arm gives reward or does not. Let's say the ad did not have a click or the arm was a failure. So the observed average of the ad or arm will go down. And the confidence bound will also go down. If it had a click, the observed average would go up and the confidence bound also goes up. By exploiting the best one we are decreasing the confidence bound. As we are adding more and more samples, the probability of other arms or ad doing well is also going up. This is the fundamental concept of UCB.