In this article, you will learn the basics of doing Machine Learning on sound data. Sound data (often called audio data) is a data type that is not very intuitive to work with. The big difficulty when starting with sound data is that, unlike tabular data or images, sound data is not easy to represent in a tabular format. As you may or may not know, images can be easily represented as matrices, because they are based on pixels. Each of the pixels has a value that indicates the intensity of the black and white (or for color images you have an intensity of red, green, and blue separately).

Then this course is for you! This course has been designed by two professional Data Scientists so that we can share our knowledge and help you learn complex theory, algorithms and coding libraries in a simple way. We will walk you step-by-step into the World of Data Science. With every tutorial you will develop new skills and improve your understanding of this challenging yet lucrative sub-field of Data Science. Moreover, the course is packed with practical exercises which are based on real-life examples.

Master how Machine Learning and Deep Learning algorithms and libraries work under the hood with practical examples. Together we are going to master in depth concepts in machine learning and python programming, then apply our knowledge in building our own neural network from scratch without using any library. What you'll learn in this course will not only lay a solid foundation in your Deep Learning career, but also permit you to understand how deep learning libraries work. If you've gotten to this point, it means you are interested in mastering how neural networks work and using your skills to solve practical problems. You may already have some knowledge on Machine learning and python programming, or you may be coming in contact with these for the very first time.

In this section, we will introduce the deep learning framework we'll be using through this course, which is PyTorch. We will show you how to install it, how it works and why it's special, and then we will code some PyTorch tensors and show you some operations on tensors, as well as show you Autograd in code!

Are you ready to master Machine Learning techniques and Kick-off your career as a Data Scientist?! You came to the right place! Machine Learning skill is one of the top skills to acquire in 2019 with an average salary of over $114,000 in the United States according to PayScale! The total number of ML jobs over the past two years has grown around 600 percent and expected to grow even more by 2020. In this course, we are going to provide students with knowledge of key aspects of state-of-the-art classification techniques.

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.