Classification accuracy is the total number of correct predictions divided by the total number of predictions made for a dataset. As a performance measure, accuracy is inappropriate for imbalanced classification problems. The main reason is that the overwhelming number of examples from the majority class (or classes) will overwhelm the number of examples in the minority class, meaning that even unskillful models can achieve accuracy scores of 90 percent, or 99 percent, depending on how severe the class imbalance happens to be. An alternative to using classification accuracy is to use precision and recall metrics. In this tutorial, you will discover how to calculate and develop an intuition for precision and recall for imbalanced classification.

Emerging technologies are changing the way finance functions operate, opening up new opportunities for treasury and enabling teams to deliver increased value to the organization. As a new decade emerges, executive are looking to artificial intelligence and machine learning as a means for enhancing overall operations. Download part two of AFP's Executive Guide to Emerging Technologies, underwritten by Kyriba, to learn how artificial intelligence and machine learning are being applied in today's treasury processes and how they will affect the future of treasury.

So you know the theory of Machine Learning and know how to create your first algorithms. There are tons of courses out there about the underlying theory of Machine Learning which don't go any deeper – into the applications. This course is not one of them. Are you ready to apply all of the theory and knowledge to real life Machine Learning challenges? We gathered best industry professionals with tons of completed projects behind.

In this introductory lecture set of lectures I will give a very quick overview of the different kinds of machine learning paradigms and therefore I call this lectures machine learning.

More and more businesses are realising the power of AI and how much it can impact the success of their business. But something that many may not have considered is how it can be used to impact the experience that employees have whilst at work, and how it can measure company culture. The capabilities of AI are advancing every day and crossing into every aspect of business, so it makes sense to get your company on board and harnessing its power before your competitors do! Alex Tebbs, co-founder of unified communications company VIA, shares his insights on a few ways that businesses can use AI to improve the employee experience. Utilising AI-powered assistants in your hiring process can significantly improve the experience that potential job candidates have in the application process.

Machine Learning is broadly utilized in information investigation as a strategy to create algorithms for making forecasts on information. This is the reason machine learning is identified with direct polynomial math, insights and likelihood. Machine Learning is comprehensively arranged into 3 sorts: Reinforcement learning – Computer program connects with a powerful situation, and it must play out a specific objective without guide or educator. Unsupervised learning -- Computer is given contributions without wanted yields, the objective is to discover structure in inputs. Supervised learning -- Computer is given data sources and their ideal yields. The objective is to gain proficiency with a general principle to delineate to the yield.

This Udacity Nanodegree Program that will help you gain the must-have skills for all aspiring data analysts and data scientists. Explore the end to end process of investigating data through a machine learning lens. Learn to extract and identify useful features that can be used to represent your data in the best form. In addition to this, you will also go over some of the most important ML algorithms and evaluate their performance.

A great technological shift is on the verge of occurring very soon. Disruptive Artificial Intelligence technologies are going to change the world and human labour will be replaced by robot workers and the shift has in-fact started. This mind-blowing course introduces you to the concept of Artificial Intelligence usage in the workplace along with providing you practical examples of the different platforms that deploy the same for automation. You will learn about the numerous Human Resources tools and usage of these in Artificial Intelligence, along with sales-based AI tools that can help you close the deal. You will be also introduced to Virtual chatbots that look like human and do all the automation and support work for you in any industry you are in. We will also look at a particular case study of a company leveraging human robots as receptionists to free up tasks for the real employees.

If you don't know, Keras is a both powerful and easy-to-use Python library for developing and evaluating deep learning models. It wraps the efficient numerical computation libraries like Theano and TensorFlow and allows you to define and train neural network models in a few short lines of code, which is just awesome. In this course, you will learn how to build an end-to-end Python machine learning project using Keras and tune a deep learning model and neural network. The best part of this course is that n the course, we will walk through every line of code so you'll be able to understand the model and the process.