There is a shift happening in the way we as a species communicate with machines. With the advent of Amazon Alexa, Google Assistant, Apple Siri, and Microsoft Cortana, the focus on Voice User Interfaces or Voice Activated Conversational Interfaces is rapidly increasing. This ever changing world presents a threat to the way we operate, especially when we do not understand it. A more AI aware world might be years away, but if we learn how to talk to and control the machines then we grow collectively. Yes, Amazon Alexa and similar voice activated interfaces look and sound pretty cool.

Problem-solving in Manufacturing is usually perceived as a slow and boring activity especially when many possible factors involved. At the same time it's often common that problems going on and on unobserved which is very costly. Is it possible to apply Artificial Intelligence to help human to identify the problem? Is it possible to dedicate this boring problem solving activity to computer? This course will help you to combine popular problem-solving technique called "is/is not" with Artificial Intelligence in order to quickly identify the problem.

Machine learning and the world of artificial intelligence (AI) are no longer science fiction. Get started with the new breed of software that is able to learn without being explicitly programmed, machine learning can access, analyze, and find patterns in Big Data in a way that is beyond human capabilities. The business advantages are huge, and the market is expected to be worth $47 billion and more by 2020. In this course, you will implement your own custom algorithm on top of SAP's HANA Database, which is an In-Memory database capable of Performing huge calculation over a large set of Data. We are going to use Native SQL to write the algorithm of Naive Bayes.

We from Mammoth Interactive are here to tell you that your Android and iOS apps can become smarter, stronger and more convenient thanks to machine learning. Better yet, we'll show you how to build your very own intelligent software that grows with you. Machine learning is changing the world around us. ML began on computers, but the next big wave is machine learning for mobile. Have you ever thought: why can't my mobile device do more?

The random forest algorithm (RF) has several hyperparameters that have to be set by the user, e.g., the number of observations drawn randomly for each tree and whether they are drawn with or without replacement, the number of variables drawn randomly for each split, the splitting rule, the minimum number of samples that a node must contain and the number of trees. In this paper, we first provide a literature review on the parameters' influence on the prediction performance and on variable importance measures, also considering interactions between hyperparameters. It is well known that in most cases RF works reasonably well with the default values of the hyperparameters specified in software packages. Nevertheless, tuning the hyperparameters can improve the performance of RF. In the second part of this paper, after a brief overview of tuning strategies we demonstrate the application of one of the most established tuning strategies, model-based optimization (MBO). To make it easier to use, we provide the tuneRanger R package that tunes RF with MBO automatically. In a benchmark study on several datasets, we compare the prediction performance and runtime of tuneRanger with other tuning implementations in R and RF with default hyperparameters.

This paper investigates to what extent do cognitive biases affect human understanding of interpretable machine learning models, in particular of rules discovered from data. Twenty cognitive biases (illusions, effects) are covered, as are possibly effective debiasing techniques that can be adopted by designers of machine learning algorithms and software. While there seems no universal approach for eliminating all the identified cognitive biases, it follows from our analysis that the effect of most biases can be ameliorated by making rule-based models more concise. Due to lack of previous research, our review transfers general results obtained in cognitive psychology to the domain of machine learning. It needs to be succeeded by empirical studies specifically aimed at the machine learning domain.

This course was funded by a wildly successful Kickstarter. Do you want to learn how to use Artificial Intelligence (AI) for automation? In this course, we cover coding in Python, working with TensorFlow, and analyzing credit card fraud. We interweave theory with practical examples so that you learn by doing. AI is code that mimics certain tasks.

Learn to code and build apps! Learn how to use TensorFlow 1.4.1 to build, train, and test machine learning models. If you want to build sophisticated and intelligent mobile apps or simply want to know more about how machine learning works in a mobile environment, this course is for you. There are next to no courses on big platforms that focus on mobile machine learning in particular. All of them focus specifically on machine learning for a desktop or laptop environment.

In this course, you will learn the basics of Machine Learning and Data Mining; almost everything you need to get started. You will understand what Big Data is and what Data Science and Data Analytics is. You will learn algorithms such as Linear Regression, Logistic Regression, Support Vector Machine, K-Nearest Neighbor, Decision Trees, and Neural Networks. You'll also understand how to combine algorithms into ensembles. Preprocessing data will be taught and you will understand how to clean your data, transform it, how to handle categorical features, and how to handle unbalanced data.

This course is a comprehensive, practical guide to using MongoDB and Spark in Python, learning how to store and make sense of huge data sets, and performing basic machine learning tasks to make predictions. MongoDB is one of the most powerful non-relational database systems available offering robust scalability and expressive operations that, when combined with Python data analysis libraries and distributed computing, represent a valuable set of tools for the modern data scientist. NoSQL databases require a new way of thinking about data and scalable queries. Once Mongo queries have been mastered, it is necessary to understand how we can leverage this API in Python's rich analysis and visualization ecosystem. This course will cover how to use MongoDB, particularly if you are used to SQL databases, with a focus on scalability to large datasets.