The methodology described here has broad applications, leading to new statistical tests, new type of ANOVA (analysis of variance), improved design of experiments, interesting fractional factorial designs, a better understanding of irrational numbers leading to cryptography, gaming and Fintech applications, and high quality random number generators (and when you really need them). It also features exact arithmetic / high performance computing and distributed algorithms to compute millions of binary digits for an infinite family of real numbers, including detection of auto- and cross-correlations (or lack of) in the digit distributions. The data processed in my experiment, consisting of raw irrational numbers (described by a new class of elementary recurrences) led to the discovery of unexpected apparent patterns in their digit distribution: in particular, the fact that a few of these numbers, contrarily to popular belief, do not have 50% of their binary digits equal to 1. It turned out that perfectly random digits simulated in large numbers, with a good enough pseudo-random generator, also exhibit the same strange behavior, pointing to the fact that pure randomness may not be as random as we imagine it is. Ironically, failure to exhibit these patterns would be an indicator that there really is a departure from pure randomness in the digits in question. In addition to new statistical / mathematical methods and discoveries and interesting applications, you will learn in my article how to avoid this type of statistical traps that lead to erroneous conclusions, when performing a large number of statistical tests, and how to not be misled by false appearances. I call them statistical hallucinations and false outliers. This article has two main sections: section 1, with deep research in number theory, and section 2, with deep research in statistics, with applications. You may skip one of the two sections depending on your interests and how much time you have. Both sections, despite state-of-the-art in their respective fields, are written in simple English. It is my wish that with this article, I can get data scientists to be interested in math, and the other way around: the topics in both cases have been chosen to be exciting and modern.

Welcome to a new series of short articles I am presenting about Artificial Intelligence specifically in the Azure AI stack. The objective is that you will learn about an Azure based AI service in no more than one minute and thus quickly get familiar with the entire stack over a short period of time. These are going short, easily digestible articles so let's get started!

This event features 25 expert talks, 3 technical tracks and is absolutely no-cost to you. Deploy AI is the preeminent virtual conference for data science experts where you will learn about Models & Ops, AI Infrastructure, & Open Source Software.

Whether you are counting cars on a road or people who are stranded on rooftops in a natural disaster, there are plenty of use cases for object detection. Often times, pre-trained object detection models do not suit your needs & you need to create your own custom models. How can you use machine learning to train your own custom model without substantive computing power & time? How can you use your custom-trained model to detect objects, in real time, with complete user privacy, all on a device with limited computing power? In this workshop, you will learn how to build an app that lets you use your own custom-trained models to detect objects.

The bots are coming and having borne witness to at least Six Terminator movies, you are understandably terrified. Not only are marketers ensuring you that these learned machines are going to automate your training so effectively that you may no longer need employees, but they have the gall to hide it all behind acronyms like AI/ML. Never fear, Dr. Allen Partridge, Head of Evangelism for Adobe Digital Learning Products is here to gently walk you through the future tech forest, carefully sorting out the helpful modern miracles from the dystopic fantasies. In this session, you will learn: What can you actually do with AI/ML today? What kind of problems are best suited for AI/ML?

I had been looking for a good book to recommend to my "Introduction to Data Science" classes at UCLA as a text to use once my class completes … sort of the next step after learning the basics. That's why I was looking forward to reviewing the new 3rd edition of the widely acclaimed title "Python Machine Learning" by Sebastian Raschka, Vahid Mirjalili. The book is a comprehensive guide to machine learning and deep learning with Python. It acts as both a step-by-step tutorial, and a useful resource you'll keep coming back to as you fill up your data science toolbox. I knew I was going to like it the minute I started thumbing through the pages and saw some mathematics.

Multiclass classification problems are those where a label must be predicted, but there are more than two labels that may be predicted. These are challenging predictive modeling problems because a sufficiently representative number of examples of each class is required for a model to learn the problem. It is made challenging when the number of examples in each class is imbalanced, or skewed toward one or a few of the classes with very few examples of other classes. Problems of this type are referred to as imbalanced multiclass classification problems and they require both the careful design of an evaluation metric and test harness and choice of machine learning models. The glass identification dataset is a standard dataset for exploring the challenge of imbalanced multiclass classification.

This tutorial is a free preview from The course titled Practical Deep Learning with Keras and Python, and it is available in The Complete Data Science Course Bundle on my website. In this course you will learn how to apply machine learning techniques to real world problems, including how to build a complete pipeline using Keras and Python. If you enjoy this video and would like to continue learning with us, feel free to check out this 6 course bundle which will take you from beginner to advanced in data science, machine learning and neural networks. I will include timestamps and links to additional resources in the description of this video. Without further ado, let's get started!

This Python tutorial for Data Science and Machine Learning will kick-start your learning of Python concepts needed for data science, as well as programming in general. Understand how to use the Jupyter Notebook, Understanding of Python from the beginning, Learn to use Object Oriented Programming with classes, Learn how to use NumPy, Pandas, Seaborn, Matplotlib, Plotly, Scikit-Learn, Machine Learning, Tensorflow, and more! This course will teach you from Python basics to advanced concepts in a practical manner, with Hands on exercises covered as well. This Python tutorial for data science will kick-start your learning of Python concepts needed for data science, as well as programming in general. Python is required for data science because, Python programming is a versatile language commonly preferred by data scientists and big tech giant companies around the world, from startups to behemoths.

In this Python tutorial, you'll learn how to facial recognition with Python and the face_recognition library Welcome to a tutorial for implementing the face recognition package for Python. The purpose of this package is to make facial recognition (identifying a face) fairly simple. Whether it's for security, smart homes, or something else entirely, the area of application for facial recognition is quite large, so let's learn how we can use this technology. To begin, we need to install everything. Installation instruction splits between Windows and Linux for some dependencies, then there is a common part for them.