Suchitra is a professor by profession and learner by passion. She hold a PhD degree in Electronics and Communication Engineering with core competency in computer vision, pattern recognition, Artificial Intelligence,machine learning and deep learning. She is passionate about data science, Artificial Intelligence, natural language processing and firmly believes that future is Artificial Intelligence.
Deep learning is not like any other technology, but it is in many cases the only technology that can solve certain problems. We need to ensure that all people involved in the project have a common understanding of what is required, how the process works, and that we have a realistic view of what is possible with the tools at hand. In order to define AI, we must first define the concept of intelligence in general. Intelligence can be generally described as the ability to perceive information and retain it as knowledge to be applied towards adaptive behaviors within an environment or context. While there are many different definitions of intelligence, they all essentially involve learning, understanding, and the application of the knowledge learned to achieve one or more goals.
Udemy is one of the most popular MOOC-based e-learning platforms in the world. Udemy has a wide variety of Machine Learning courses. That's why in this article, I am going to share with you the 10 Best Udemy Courses for Machine Learning. So give your few minutes to this article and find out the Best Udemy Courses for Machine Learning. Now, without any further ado, let's get started- This is the Bestseller Course at Udemy.
Deep Learning Prerequisites: Linear Regression in Python, Data science: Learn linear regression from scratch and build your own working program in Python for data analysis. Created by Lazy Programmer Inc. Preview this Course - GET COUPON CODE 100% Off Udemy Coupon . Free Udemy Courses . Online Classes
If you are thinking of learning Data Science, Machine learning (ML), or Deep Learning (DL), you are not alone; more and more people are starting with these advanced skills worldwide. I have seen a lot of interest from software engineers in the ML and AI space. They are totally caught up with the craze of developing programs that can recognize numbers, alphabets, vehicles, and several other image scanning stuff. The craze is very similar to what the 1980's programmer has about video games, where moving a character on screen gives the joy you get when your program correctly identifies the number or letter you make from hand. From college graduates to junior programmers and from experienced programmers to software architects, all show interest in ML and AI to become part of the next technical revolution we may be witnessing.
Digitization is penetrating more and more areas of life. Tasks are increasingly being completed digitally, and are therefore not only fulfilled faster, more efficiently but also more purposefully and successfully. The rapid developments in the field of artificial intelligence in recent years have played a major role in this, as they brought up many helpful approaches to build on. At the same time, the eyes, their movements, and the meaning of these movements are being progressively researched. The combination of these developments has led to exciting approaches. In this dissertation, I present some of these approaches which I worked on during my Ph.D. First, I provide insight into the development of models that use artificial intelligence to connect eye movements with visual expertise. This is demonstrated for two domains or rather groups of people: athletes in decision-making actions and surgeons in arthroscopic procedures. The resulting models can be considered as digital diagnostic models for automatic expertise recognition. Furthermore, I show approaches that investigate the transferability of eye movement patterns to different expertise domains and subsequently, important aspects of techniques for generalization. Finally, I address the temporal detection of confusion based on eye movement data. The results suggest the use of the resulting model as a clock signal for possible digital assistance options in the training of young professionals. An interesting aspect of my research is that I was able to draw on very valuable data from DFB youth elite athletes as well as on long-standing experts in arthroscopy. In particular, the work with the DFB data attracted the interest of radio and print media, namely DeutschlandFunk Nova and SWR DasDing. All resulting articles presented here have been published in internationally renowned journals or at conferences.
This is Moein Ud Din, I am instructor of mathematics, a programmer of Python, Artificial intelligence, machine learning, deep learning, image processing, NLP and an animation geek. It's my passion to pass my knowledge what I know to the desired entities. Kinda quintessential person, always hounding to make things ideal. I have plan in future to prepare more mathematics and IT courses, would love to make things change in my own certain way. Critics are most welcome as long as it helps me and my students.
Familiarity with basic statistics and mathematical notation is helpful. An Introduction to Statistical Learning is one of the best introductory textbooks on classical machine learning techniques such as linear regression. It was the first machine learning book I've bought and has given me a great foundation. The explanations are held on a high level, so you don't need advanced math skills. Every chapter comes with code examples and labs in R. It is a great book to work through cover-to-cover. Get "An Introduction to Statistical Learning" on Amazon