If you sell your expertise for a living, you will discover significant benefits in transferring your knowledge to the world of Artificial Intelligence (AI). It is now possible to create an online chatbot with near human characteristics to deliver your intellectual property to the world. From your website your clients will be able to interact with the AI system to receive a personalised experience of the way you deliver your specialist skills. The Chatbot will be able to track the client's progress and adapt the learning experience to suit their individual mood, personality and abilities. Your unique talents will be instantly made available to a global audience 24 / 7. Up until August 2016 the software to build a chatbot has been in prototype with major corporations but now it's going mainstream.

If you are practitioner interested in deep learning for NLP, you may have different goals and requirements from the material. For example, you may want to focus on the methods and applications rather than the foundational theory. The course is comprised of 13 lectures, although the first and second lectures are both split into two parts. The complete lecture breakdown is provided below. The GitHub repository for the course provides links to slides, flash videos and reading for each lecture. I would recommend watching the videos via this unofficial YouTube playlist. Below is a course overview slide taken from the first lecture.

When we build these models, we always use a set of historical data to help our machine learning algorithms learn what is the relationship between a set of input features to a predicted output. We'll show how you can evaluate these issues by assessing metrics of bias vs. variance and precision vs. recall, and present some solutions that can help when you encounter such scenarios. If we were to train a machine learning model and it learned to always predict an email as not spam (negative class), then it would be accurate 99% of the time despite never catching the positive class. Similarly, increasing the number of training examples can help in cases of high variance, helping the machine learning algorithm build a more generalizable model.

By Anish Srikrishna When you buy a product from an online marketplace, have you ever wondered how your order gets processed among millions of others? Does it baffle you how some banks use chatbots to solve customer queries based on transaction history, or that financial and portfolio management institutions deploy sentiment analysis and algorithms to consistently generate handsome returns for customers. In the age of decision sciences, all this and more can be effortlessly achieved. Thanks to the quantum advances in technology, today decision sciences is the cornerstone of success in the data-driven world. Decision sciences is helping corporations in BFSI (banking, financial services and insurance), ecommerce, telecom, transportation and many such industries process massive amount of data to leverage their true business potential.

So, you want to become a data scientist or may be you are already one and want to expand your tool repository. You have landed at the right place. The aim of this page is to provide a comprehensive learning path to people new to python for data analysis. This path provides a comprehensive overview of steps you need to learn to use Python for data analysis. If you already have some background, or don't need all the components, feel free to adapt your own paths and let us know how you made changes in the path.

Learn from expert, Ankesh Kumar, CEO of Personic, how to build the use case and business case for launching your chatbot. How do we budget the costs for development, discovery, launch, promotion, and support. What is the Chatbot Lifecycle and how can we optimize our efforts. Limited registration, get your free seat below. Get a free Template Business Case for attending.

In an increasingly polarized country, we all share one thing in common: everyone has taken a standardized test. Whether it was the SAT, ACT, GMAT, LSAT, or some other exam, we have all sat at a desk and fretted about the impact our performance might have on our future. Well, today's kids are about to become a degree further removed from our shared childhood experiences, because they likely will not have to prepare for these tests with oversized test prep books. But where we criticize today's youth for not playing outside, we can only envy them for the educational resources coming their way. Before we dive into how cool that technology could be, let's zoom out and look at education technology (edtech) as an industry, because it is booming.

You're in good company – a recent article by Laurence Bradford in Forbes calls data science'the century's hottest career'. But how can you get your foot in the door? Many resources out there may lead you to believe that becoming a data scientist requires comprehensive mastery of a number of fields, such as software development, data munging, databases, statistics, machine learning and data visualization. You don't need to learn a lifetime's worth of data-related information and skills as quickly as possible. Instead, learn to read data science job descriptions closely.

In the syntax below, we are asking Python to import numpy and pandas package. The'as' is used to alias package name.

AI is the new hotness. Separating hype from fact is sometimes difficult. In this webcast, we will define what AI is, the differences between AI, data science, and machine learning, and how it applies to organizations of all shapes and sizes. We'll also cover how Microsoft is democratizing AI. As a fun demo, we'll rebuild the "Not hotdog" custom algorithm live during this presentation.