Machine learning (ML) has received a lot of attention recently, and not without good reason. It has already revolutionized fields from image recognition to healthcare to transportation.

By Andrew Trask This tutorial teaches backpropagation via a very simple toy example, a short python implementation. Consider trying to predict the output column given the three input columns. We could solve this problem by simply measuring statistics between the input values and the output values. If we did so, we would see that the leftmost input column is perfectly correlated with the output. Let's jump right in and use it to do this.

What is a Time Series? Many data sets are cross-sectional and represent a single slice of time. However, we also have data collected over many periods - weekly sales data, for instance. This is an example of time series data. Time series analysis is a specialized branch of statistics used extensively in fields such as Econometrics and Operations Research.

The objective of this course is to give you a wholistic understanding of machine learning, covering theory, application, and inner workings of supervised, unsupervised, and deep learning algorithms. In this series, we'll be covering linear regression, K Nearest Neighbors, Support Vector Machines (SVM), flat clustering, hierarchical clustering, and neural networks. For each major algorithm that we cover, we will discuss the high level intuitions of the algorithms and how they are logically meant to work. Next, we'll apply the algorithms in code using real world data sets along with a module, such as with Scikit-Learn. Finally, we'll be diving into the inner workings of each of the algorithms by recreating them in code, from scratch, ourselves, including all of the math involved.

It easier than ever before to train a neural network. However, it is rarely the case that you can just take code from a tutorial and directly make it work for your application. Interestingly, many of the most important tweaks are barely discussed in the academic literature but at the same time critical to make your product work. Therefore I thought it would be helpful for other people who plan to use deep learning in their business to understand some of these tweaks and tricks. This post is based on my talk I gave on May 10 at the Berlin.AI meetup (the slides are here).

Includes key concepts of statistical analysis - Probability theory, Types of distribution, Central limit theorem, Hypothesis testing, Statsistical inference.

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What if machines could learn? This has been one of the most intriguing questions in science fiction and philosophy since the advent of machines. With modern technology such questions are no longer bound to creative conjecture, machine learning is all around us. From deciding which movie you might want to watch next on Netflix to predicting stock market trends, machine learning has a profound impact on how data is understood in the modern era. This tutorial aims to give an accessible introduction to how to use machine learning techniques for your own projects and datasets.

"Speaker: Christopher Fonnesbeck This intermediate-level tutorial will provide students with hands-on experience applying practical statistical modeling methods on real data. Unlike many introductory statistics courses, we will not be applying ""cookbook"" methods that are easy to teach, but often inapplicable; instead, we will learn some foundational statistical methods that can be applied generally to a wide variety of problems: maximum likelihood, bootstrapping, linear regression, and other modern techniques. The tutorial will start with a short introduction on data manipulation and cleaning using [pandas](http://pandas.pydata.org/), Slightly more advanced topics include bootstrapping (for estimating uncertainty around estimates) and flexible linear regression methods using Bayesian methods. By using and modifying hand-coded implementations of these techniques, students will gain an understanding of how each method works.