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Statistical Learning


Machine Learning in Physics: Glass Identification Problem

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Move your ML skills from theory to practice in one of the most interesting fields " Physics"? In this course you are going to solve the glass identification problem where you are going to build and train several machine learning models in order to classify 7 types of glass( 1- Building windows float-processed glass / 2- Building windows non-float-processed glass / 3- Vehicle windows float-processed glass / 4- Vehicle windows non-float-processed-glass / 5- Containers glass / 6- Tableware glass / 7- Headlamps glass). After completing this course, you will gain a bunch of skillset that allows you to deal with any machine learning problem from the very first step to getting a fully trained performent model.


H2O.ai

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H2O is an Open Source, Distributed, Fast & Scalable Machine Learning Platform: Deep Learning, Gradient Boosting (GBM) & XGBoost, Random Forest, Generalized Linear Modeling (GLM with Elastic Net), K-Means, PCA, Generalized Additive Models (GAM), RuleFit, Support Vector Machine (SVM), Stacked Ensemble...


Implementing SVM From Scratch

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The support vector machine (SVM), developed by the computer science community in the 1990s, is a supervised learning algorithm commonly used and originally intended for a binary classification setting. It is often considered one of the best "out of the box" classifiers. The SVM is a generalization of the simple yet elegant algorithm called the maximal margin classifier. This classifier, however, cannot be applied in every situation since it relies heavily on the assumption that the dataset is linearly separable -- thus, several extensions exist. Note: In the following, we will only cover the maximal margin classifier, purposely avoiding the different extensions.


Sharpness-Aware Minimization

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This post deals with a recent optimizing method for training neural networks described in the paper Sharpness-Aware Minimization for Efficiently Improving Generalization by P. Foret et al. (December 2020). Honestly, the first time I read about the paper details, I really thought the procedure therein described (or something similar) had already been explored many years before by tons of people… I was even surprised to read that it worked in some contexts. Modern models train through optimization methods relying just on the training loss. These models can easily memorize the training data and are prone to overfitting. They have more parameters than needed and this large number of parameters provides no guarantee of proper generalization to the test set.


Different Types of Regression Models - Analytics Vidhya

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Predictive modelling techniques such as regression analysis may be used to determine the relationship between a dataset's dependent (goal) and independent variables. It is widely used when the dependent and independent variables are linked in a linear or non-linear fashion, and the target variable has a set of continuous values. Thus, regression analysis approaches help establish causal relationships between variables, modelling time series, and forecasting. Regression analysis, for example, is the best way to examine the relationship between sales and advertising expenditures for a corporation.



Analysis of a Synthetic Breast Cancer Dataset in R

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Originally published on Towards AI the World's Leading AI and Technology News and Media Company. If you are building an AI-related product or service, we invite you to consider becoming an AI sponsor. At Towards AI, we help scale AI and technology startups. Let us help you unleash your technology to the masses. This post is about me analyzing a synthetic dataset containing 60k records of patients with breast cancer.


Clustering Text with k-Means

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In the last post, we talked about Topic Modeling or a way to identify several topics from a corpus of documents. The method used there was Latent Dirichlet Allocation or LDA. In this article, we're going to perform a similar task but through the unsupervised machine learning method of clustering. While the method is different, the outcome is several groups (or topics) of words related to each other. For this example, we will use the Wine Reviews dataset from Kaggle.


Introduction to Logistic Regression: Predicting Diabetes

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Data can be broadly divided into continuous data, those that can take an infinite number of points within a given range such as distance or time, and categorical/discrete data, which contain a finite number of points or categories within a given group of data such as payment methods or customer complaints. We have already seen examples of applying regression to continuous prediction problems in the form of linear regression where we predicted sales, but in order to predict categorical outputs we can use logistic regression. While we are still using regression to predict outcomes, the main aim of logistic regression is to be able to predict which category and observation belongs to rather than an exact value. Examples of questions which this method can be used for include: "How likely is a person to suffer from a disease (outcome) given their age, sex, smoking status, etc (variables/features)?" "How likely is this email to be spam?" "Will a student pass a test given some predictors of performance?".


Learning Resources for Machine Learning - Programmathically

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Familiarity with basic statistics and mathematical notation is helpful. An Introduction to Statistical Learning is one of the best introductory textbooks on classical machine learning techniques such as linear regression. It was the first machine learning book I've bought and has given me a great foundation. The explanations are held on a high level, so you don't need advanced math skills. Every chapter comes with code examples and labs in R. It is a great book to work through cover-to-cover. Get "An Introduction to Statistical Learning" on Amazon