Since the best-selling first edition was published, there have been several prominent developments in the field of machine learning, including the increasing work on the statistical interpretations of machine learning algorithms. Unfortunately, computer science students without a strong statistical background often find it hard to get started in this area. Remedying this deficiency, Machine Learning: An Algorithmic Perspective, Second Edition helps students understand the algorithms of machine learning. It puts them on a path toward mastering the relevant mathematics and statistics as well as the necessary programming and experimentation.

About this course: The course aims at helping students to be able to solve practical ML-amenable problems that they may encounter in real life that include: (1) understanding where the problem one faces lands on a general landscape of available ML methods, (2) understanding which particular ML approach(es) would be most appropriate for resolving the problem, and (3) ability to successfully implement a solution, and assess its performance. A learner with some or no previous knowledge of Machine Learning (ML) will get to know main algorithms of Supervised and Unsupervised Learning, and Reinforcement Learning, and will be able to use ML open source Python packages to design, test, and implement ML algorithms in Finance. Fundamentals of Machine Learning in Finance will provide more at-depth view of supervised, unsupervised, and reinforcement learning, and end up in a project on using unsupervised learning for implementing a simple portfolio trading strategy.

The practice of Business Analysis revolves around the formation, transformation and finalisation of requirements to recommend suitable solutions to support enterprise change programmes. Practitioners working in the field of business analysis apply a wide range of modelling tools to capture the various perspectives of the enterprise, for example, business process perspective, data flow perspective, functional perspective, static structure perspective, and more. These tools aid in decision support and are especially useful in the effort towards the transformation of a business into the "intelligent enterprise", in other words, one which is to some extent "self-describing" and able to adapt to organisational change. However, a fundamental piece remains missing from the puzzle. Achieving this capability requires us to think beyond the idea of simply using the current mainstream modelling tools.

A staggering amount of data is created everyday; analyzing and organizing this enormous amount of data can be quite a complex task. Haskell is a powerful and well-designed functional programming language that is designed to work with complex data. It is trending in the field of data science as it provides a powerful platform for robust data science practices. This course will introduce the basic concepts of Haskell and move on to discuss how Haskell can be used to solve the issues by using the real-world data. The course will guide you through the installation procedure, after you have all the tools that you require in place, you will explore the basic concepts of Haskell including the functions, and the data structures.

About this course: This course aims at providing an introductory and broad overview of the field of ML with the focus on applications on Finance. Supervised Machine Learning methods are used in the capstone project to predict bank closures. Simultaneously, while this course can be taken as a separate course, it serves as a preview of topics that are covered in more details in subsequent modules of the specialization Machine Learning and Reinforcement Learning in Finance. The goal of Guided Tour of Machine Learning in Finance is to get a sense of what Machine Learning is, what it is for and in how many different financial problems it can be applied to.

In this tutorial, you will learn how to use the video analysis features in Amazon Rekognition Video using the AWS Console. Amazon Rekognition Video is a deep learning powered video analysis service that detects activities and recognizes objects, celebrities, and inappropriate content. As a developer, analyzing video is a challenge you will face if you are developing a video cataloging system or creating an application to provide sentiment analysis. This challenge can be solved by building your own machine learning model, however this option is time-intensive, expensive, and requires machine learning expertise. Amazon Rekognition Video provides an easy-to-use API that offers real-time analysis of streaming video and facial analysis.

A prediction interval is calculated as some combination of the estimated variance of the model and the variance of the outcome variable. Prediction intervals are easy to describe, but difficult to calculate in practice. In simple cases like linear regression, we can estimate the confidence interval directly. In the cases of nonlinear regression algorithms, such as artificial neural networks, it is a lot more challenging and requires the choice and implementation of specialized techniques. General techniques such as the bootstrap resampling method can be used, but are computationally expensive to calculate. The paper "A Comprehensive Review of Neural Network-based Prediction Intervals and New Advances" provides a reasonably recent study of prediction intervals for nonlinear models in the context of neural networks.

This course helps to understand what AI is, how it works, and how to use it to build smart apps. You can learn how to build simple machine learning models and implement conversational bots. To learn about machine learning, enter this course to get both theoretical and practical knowledge. You will understand various concepts such as inductive bias, the PAC and Mistake-bound learning frameworks, minimum description length principle, and Ockham's Razor.

A Modern Approach, 3e offers the most comprehensive, up-to-date introduction to the theory and practice of artificial intelligence. Number one in its field, this textbook is ideal for one or two-semester, undergraduate or graduate-level courses in Artificial Intelligence. In this mind-expanding book, scientific pioneer Marvin Minsky continues his groundbreaking research, offering a fascinating new model for how our minds work. He argues persuasively that emotions, intuitions, and feelings are not distinct things, but different ways of thinking. Introduction to Artificial Intelligence presents an introduction to the science of reasoning processes in computers, and the research approaches and results of the past two decades.

The field of computer science is exceptionally vast and ever-expanding. It will take a lifetime to just fathom its depth, forget mastering all the diversified fields. However, it's'programming' which is ubiquitous in the various branches of computer science. Programming offers a plethora of opportunities to kick-start your professional career. Now if you dabble in the art of coding (the other term for'programming'), yet the multitude of options confuses you, pore over the following article to find your niche in computer science.