Artificial Intelligence (AI) algorithms are increasingly providing decision making and operational support across multiple domains. AI includes a wide library of algorithms for different problems. One important notion for the adoption of AI algorithms into operational decision process is the concept of assurance. The literature on assurance, unfortunately, conceals its outcomes within a tangled landscape of conflicting approaches, driven by contradicting motivations, assumptions, and intuitions. Accordingly, albeit a rising and novel area, this manuscript provides a systematic review of research works that are relevant to AI assurance, between years 1985 - 2021, and aims to provide a structured alternative to the landscape. A new AI assurance definition is adopted and presented and assurance methods are contrasted and tabulated. Additionally, a ten-metric scoring system is developed and introduced to evaluate and compare existing methods. Lastly, in this manuscript, we provide foundational insights, discussions, future directions, a roadmap, and applicable recommendations for the development and deployment of AI assurance.

As we evolve from pandemic to next normal world, 36% of manufacturers are already engaged on AI projects and 23% more have plans to use AI in coming months to unlock the anticipated trillions of dollars in value in industrial sectors. Based on the lessons learned during the pandemic they are all seeking the best way to achieve new levels of productivity, safety and agility by unlocking the AI driven insights from petabytes of data they harvest from their connected factories. To answer these and other questions, many manufactures are turning to AI to speed time to value. Are you a manufacturer looking at how AI can benefit your business? If so, join this webinar with two leaders in AI as they discuss where and how manufacturers are seeing value from AI in areas such as inspection, predictive maintenance, quality control, video analytics and digital twins.

This course is being covered by a Semiconductor Industry Professional with about 4 years of industrial experience with Masters Degree in Embedded Systems and will have elements of practical application throughout the course for better understanding. Digital Electronics course is essential for many graduate courses and forms the basis for many other courses. It's important to get these basics right to build strong foundation. You can quickly go through introductory lectures which are available for preview and signup for the rest of the course! Check out the initial few lectures for dis counts on the course!

We all, whether beginners or more experienced, need perfect and engaging courses to learn better. So I wrote this post for those who are really confused about which course is the best and free on the web. In this article, I will talk about the "7 best free AI AI courses in 2022" available. The list is a bit long but very interesting as all the courses listed here are from the most famous international education websites like Coursera, Udacity and Udemy. This is a beginner class for anyone who wants to understand what AI does, how it affects, and what it can be used for, without involving basic math or statistics. .

This course is about the fundamental concepts of machine learning, focusing on regression, SVM, decision trees and neural networks. These topics are getting very hot nowadays because these learning algorithms can be used in several fields from software engineering to investment banking. Learning algorithms can recognize patterns which can help detect cancer for example or we may construct algorithms that can have a very good guess about stock prices movement in the market. In each section we will talk about the theoretical background for all of these algorithms then we are going to implement these problems together. We will use Python with SkLearn, Keras and TensorFlow.

Welcome to KGP Talkie's Natural Language Processing (NLP) course. It is designed to give you a complete understanding of Text Processing and Mining with the use of State-of-the-Art NLP algorithms in Python. We will learn Spacy in detail and we will also explore the uses of NLP in real life. This course covers the basics of NLP to advance topics like word2vec, GloVe, Deep Learning for NLP like CNN, ANN, and LSTM. I will also show you how you can optimize your ML code by using various tools of sklean in python.

Machine Learning and artificial intelligence (AI) is everywhere; if you want to know how companies like Google, Amazon, and even Udemy extract meaning and insights from massive data sets, this data science course will give you the fundamentals you need. Data Scientists enjoy one of the top-paying jobs, with an average salary of $120,000 according to Glassdoor and Indeed. If you've got some programming or scripting experience, this course will teach you the techniques used by real data scientists and machine learning practitioners in the tech industry - and prepare you for a move into this hot career path. Each concept is introduced in plain English, avoiding confusing mathematical notation and jargon. It's then demonstrated using Python code you can experiment with and build upon, along with notes you can keep for future reference.

Machine Learning and artificial intelligence (AI) is everywhere; if you want to know how companies like Google, Amazon, and even Udemy extract meaning and insights from massive data sets, this data science course will give you the fundamentals you need. Data Scientists enjoy one of the top-paying jobs, with an average salary of $120,000 according to Glassdoor and Indeed. If you've got some programming or scripting experience, this course will teach you the techniques used by real data scientists and machine learning practitioners in the tech industry - and prepare you for a move into this hot career path. Each concept is introduced in plain English, avoiding confusing mathematical notation and jargon. It's then demonstrated using Python code you can experiment with and build upon, along with notes you can keep for future reference.

This is the third course from my Computer Vision series. Image Recognition, Object Detection, Object Recognition and also Optical Character Recognition are among the most used applications of Computer Vision. Using these techniques, the computer will be able to recognize and classify either the whole image, or multiple objects inside a single image predicting the class of the objects with the percentage accuracy score. Using OCR, it can also recognize and convert text in the images to machine readable format like text or a document. Object Detection and Object Recognition is widely used in many simple applications and also complex ones like self driving cars.

Every machine learning algorithm has three components: Representation: how to represent knowledge. Examples include decision trees, sets of rules, instances, graphical models, neural networks, support vector machines, model ensembles and others. Artificial intelligence is the simulation of human intelligence processes by machines, especially computer systems. Specific applications of AI include expert systems, natural language processing, speech recognition and machine vision. As the hype around AI has accelerated, vendors have been scrambling to promote how their products and services use AI.