You will find here nine interesting topics that you won't learn in college classes. Most have interesting applications in business and elsewhere. They are not especially difficult, and I explain them in simple English. Yet they are not part of the traditional statistical curriculum, and even many experienced data scientists with a PhD degree have not heard about some of these concepts. This is a well known model, used as a base stochastic process to model the logarithm of stock prices, yet it has interesting properties (depending on dimension) that few people know about.

It explores main concepts from basic to expert level which can help you achieve better grades, develop your academic career, apply your knowledge at work or do your business forecasting research. Learning deep learning regression is indispensable for data mining applications in areas such as consumer analytics, finance, banking, health care, science, e-commerce and social media. It is also essential for academic careers in data mining, applied statistical learning or artificial intelligence. But as learning curve can become steep as complexity grows, this course helps by leading you step by step using S&P 500 Index ETF prices historical data for algorithm learning to achieve greater effectiveness. This practical course contains 33 lectures and 4 hours of content.

Prerequisites: No prerequisites, knowledge of some undergraduate level mathematics would help but is not mandatory. Working knowledge of Python would be helpful if you want to run the source code that is provided. Taught by a Stanford-educated, ex-Googler and an IIT, IIM - educated ex-Flipkart lead analyst. This team has decades of practical experience in quant trading, analytics and e-commerce. This course is a down-to-earth, shy but confident take on machine learning techniques that you can put to work today Let's parse that.

This course is designed to get students on board with data science and make them ready to solve industry problems. This course is a perfect blend of foundations of data science, industry standards, broader understanding of machine learning and practical applications. Special emphasis is given to regression analysis. Linear and logistic regression is still the workhorse of data science. These two topics are the most basic machine learning techniques that everyone should understand very well.

Stepwise regression analysis, a technique that helps you select the best subset of predictors for a regression analysis, when you have a big number of predictors. This way you can create regression models that are both parsimonious and effective. After finishing this course, you will be able to fit any nonlinear regression model using SPSS. K nearest neighbor, a very popular predictive technique used mostly for classification purposes. So you will learn how to predict the values of a categorical variable with this method.

I have a model that's trained to detect various concepts on biomedical papers. I'm using the last dense layer before the output to use as vector embeddings for these medical papers. I then use k-means on the embedding vectors to cluster the papers together. I prefer this over a pure unsupervised approach as these embeddings will be forced to focus on biological similarity over biological & semantic. I have about 10M papers and each cluster needs to have 50 elements.

This course prepares participants to begin running data analysis on databases. Both univariate and multivariate analysis are covered with a particular focus on regression analysis. Regression analysis is done in Excel, SAS, and Stata to give viewers a sense of familiarity with a variety of different software package structures. The focus in this course is on financial data though the techniques are also applicable to more general forms of data like that used in marketing or management analyses. If you would like Continuing Education Credit (e.g.

We propose Rademacher complexity bounds for multi-class classifiers trained with a two-step semi-supervised model. In the first step, the algorithm partitions the partially labeled data and then identifies dense clusters containing κ predominant classes using the labeled training examples such that the proportion of their non-predominant classes is below a fixed threshold stands for clustering consistency. In the second step, a classifier is trained by minimizing a margin empirical loss over the labeled training set and a penalization term measuring the disability of the learner to predict the κ predominant classes of the identified clusters. The resulting data-dependent generalization error bound involves the margin distribution of the classifier, the stability of the clustering technique used in the first step and Rademacher complexity terms corresponding to partially labeled training data. Our theoretical result exhibit convergence rates extending those proposed in the literature for the binary case, and experimental results on different multi-class classification problems show empirical evidence that supports the theory.

We propose a modification of linear discriminant analysis, referred to as compressive regularized discriminant analysis (CRDA), for analysis of high-dimensional datasets. CRDA is specially designed for feature elimination purpose and can be used as gene selection method in microarray studies. CRDA lends ideas from $\ell_{q,1}$ norm minimization algorithms in the multiple measurement vectors (MMV) model and utilizes joint-sparsity promoting hard thresholding for feature elimination. A regularization of the sample covariance matrix is also needed as we consider the challenging scenario where the number of features (variables) is comparable or exceeding the sample size of the training dataset. A simulation study and four examples of real-life microarray datasets evaluate the performances of CRDA based classifiers. Overall, the proposed method gives fewer misclassification errors than its competitors, while at the same time achieving accurate feature selection.

Anomaly Detection has several important applications. In this paper, our focus is on detecting anomalies in seller-reviewer data using tensor decomposition. While tensor-decomposition is mostly unsupervised, we formulate Bayesian semi-supervised tensor decomposition to take advantage of sparse labeled data. In addition, we use Polya-Gamma data augmentation for the semi-supervised Bayesian tensor decomposition. Finally, we show that the Polya-Gamma formulation simplifies calculation of the Fisher information matrix for partial natural gradient learning. Our experimental results show that our semi-supervised approach outperforms state of the art unsupervised baselines. And that the partial natural gradient learning outperforms stochastic gradient learning and Online-EM with sufficient statistics.