Learn basics to advanced concepts in machine learning and its implementation in financial markets. Includes deep learning, tensor flows, installation guides, downloadable strategy codes along with real-market data.

Logistic regression is a popular method since the last century. It establishes the relationship between a categorical variable and one or more independent variables. This relationship is used in machine learning to predict the outcome of a categorical variable. It is widely used in many different fields such as the medical field, trading and business, technology, and many more. This article explains the process of developing a binary classification algorithm and implements it on a medical dataset.

The empirical practice of using factor models to adjust for shared, unobserved confounders, $\mathbf{Z}$, in observational settings with multiple treatments, $\mathbf{A}$, is widespread in fields including genetics, networks, medicine, and politics. Wang and Blei (2019, WB) formalizes these procedures and develops the "deconfounder," a causal inference method using factor models of $\mathbf{A}$ to estimate "substitute confounders," $\hat{\mathbf{Z}}$, then estimating treatment effects by regressing the outcome, $\mathbf{Y}$, on part of $\mathbf{A}$ while adjusting for $\hat{\mathbf{Z}}$. WB claim the deconfounder is unbiased when there are no single-cause confounders and $\hat{\mathbf{Z}}$ is "pinpointed." We clarify pinpointing requires each confounder to affect infinitely many treatments. We prove under these assumptions, a na\"ive semiparametric regression of $\mathbf{Y}$ on $\mathbf{A}$ is asymptotically unbiased. Deconfounder variants nesting this regression are therefore also asymptotically unbiased, but variants using $\hat{\mathbf{Z}}$ and subsets of causes require further untestable assumptions. We replicate every deconfounder analysis with available data and find it fails to consistently outperform na\"ive regression. In practice, the deconfounder produces implausible estimates in WB's case study to movie earnings: estimates suggest comic author Stan Lee's cameo appearances causally contributed \$15.5 billion, most of Marvel movie revenue. We conclude neither approach is a viable substitute for careful research design in real-world applications.

We are familiar with most of the supervised learning methods, for example, linear regression, logistic regression, decision trees, SVM so on… where for an input we have an associated output/label. When we have a problem in which we have input but no associated output/label such kind of learning is known as unsupervised learning. One mechanism that we may use in this context is cluster analysis or clustering. Definition 1: Cluster analysis is a multivariate statistical technique. It group's observations on the basis some of their features or variables they are described by.! Definition 2: Cluster analysis observations in a data set can be divided into different groups and is very useful.

In the formula above, f(xi) represents the predicted output value for ith example from the input, and b0 and b1 are regression coefficients that represent the y-intercept and slope of the regression line. We want that value to be as close as possible to the real value – y. Thus model needs to learn the values regression coefficients b0 and b1, based on which model will be able to predict the correct output. In order to make these estimates, the algorithm needs to know how bad are his current estimations of these coefficients. At the beginning of the training process, we feed samples into the algorithm which calculates output f(xi) of the current sample, based on initial values of regression coefficients.

I recently participated in a Machine Learning workshop at Rootstrap, where my coworkers and I learned about the basics of data science, did some research, and created interesting experiments. We had the opportunity to choose among the studied Machine Learning algorithms and work with them. So, I decided to do an experiment where a mathematical model predicts the life expectancy of a country. That is, given some data of a given country, we can make a prediction of its life expectancy in a determined year. The experiment was made in a jupyter notebook, using the python programming language, and the scikit learn library. In this blog, I'm going to talk about my experience and explain a little bit about the work I did and the new things that I have learned.

Artificial Intelligence (AI) revolution is here! The technology is progressing at a massive scale and is being widely adopted in the Healthcare, defense, banking, gaming, transportation and robotics industries. Machine Learning is a subfield of Artificial Intelligence that enables machines to improve at a given task with experience. Machine Learning is an extremely hot topic; the demand for experienced machine learning engineers and data scientists has been steadily growing in the past 5 years. According to a report released by Research and Markets, the global AI and machine learning technology sectors are expected to grow from $1.4B to $8.8B by 2022 and it is predicted that AI tech sector will create around 2.3 million jobs by 2020.

When modeling, it is important to clean the data sample to ensure that the observations best represent the problem. Sometimes a dataset can contain extreme values that are outside the range of what is expected and unlike the other data. These are called outliers and often machine learning modeling and model skill in general can be improved by understanding and even removing these outlier values. In this tutorial, you will discover outliers and how to identify and remove them from your machine learning dataset. Discover data cleaning, feature selection, data transforms, dimensionality reduction and much more in my new book, with 30 step-by-step tutorials and full Python source code. How to Use Statistics to Identify Outliers in Data Photo by Jeff Richardson, some rights reserved.

Udemy Coupon - Deep Learning Prerequisites: Logistic Regression in Python, Data science techniques for professionals and students - learn the theory behind logistic regression and code in Python BESTSELLER 4.6 (2,529 ratings) Created by Lazy Programmer Inc.  English [Auto-generated], Portuguese [Auto-generated], 1 more Preview this Course - GET COUPON CODE 100% Off Udemy Coupon . Free Udemy Courses . Online Classes

In data science, there is a long history of using synthetic data for method development, feature selection and feature engineering. Our current interest in synthetic data comes from recent work in explainability. Today's datasets are typically larger and more complex - requiring less interpretable models. In the setting of \textit{post hoc} explainability, there is no ground truth for explanations. Inspired by recent work in explaining image classifiers that does provide ground truth, we propose a similar solution for tabular data. Using copulas, a concise specification of the desired statistical properties of a dataset, users can build intuition around explainability using controlled data sets and experimentation. The current capabilities are demonstrated on three use cases: one dimensional logistic regression, impact of correlation from informative features, impact of correlation from redundant variables.