The purpose of this project is to provide a comprehensive and yet simple course in Machine Learning using Python. Machine Learning, as a tool for Artificial Intelligence, is one of the most widely adopted scientific fields. A considerable amount of literature has been published on Machine Learning. The purpose of this project is to provide the most important aspects of Machine Learning by presenting a series of simple and yet comprehensive tutorials using Python. In this project, we built our tutorials using many different well-known Machine Learning frameworks such as Scikit-learn.

Create your own chatbots using the world's most popular chatbot language. This course is designed for people with absolutely no knowledge of Artificial Intelligence Markup Language (AIML). It guides you step by step and teaches you how to create a chatbot using the world's most popular chatbot language. From the very beginning to more advanced features, take it at your own pace, practice and learn from Steve Worswick, the 5 times holder of the Loebner Prize.

Who is this course for? It is highly recommended that you also have a solid foundation and undertsanding of how Python works. We will be using Python 3.8 for all the examples. It is seen as a part of artificial intelligence. Machine learning algorithms build a model based on sample data, known as "training data", in order to make predictions or decisions without being explicitly programmed to do so.

What is Python and why is it so popular? This is a commonly Googled question today, even as more people turn to software programming / software development as a career option. There are many coding languages available today. But Python leads the pack. What is the reason behind the increasing demand for programmers proficient in Python?

In this article, we've listed all Machine Learning and Deep Learning courses by Andrew Ng, an excellent teacher from Standford University, and a tech-entrepreneur. The Machine Learning and Deep Learning courses given below are all available on Coursera in case you are interested in enrolling in any one of them. The Machine Learning course by Stanford and popularized by Andrew Ng's teaching is the best certification course in Machine Learning you can go for. The course is 11 weeks long and covers almost everything that you need to know about Machine Learning with great examples and assignments. The course has a 4.9/5 average rating from over 160,000 student ratings.

Data Science, which is also known as the sexiest job of the century, has become a dream job for many of us. But for some, it looks like a challenging maze and they don't know where to start. If you are one of them, then continue reading. In this post, I'll discuss how you can start your journey of Data Science from scratch. I'll explain the following steps in detail.

You're looking for a complete Support Vector Machines course that teaches you everything you need to create a SVM model in R, right? You've found the right Support Vector Machines techniques course! How this course will help you? A Verifiable Certificate of Completion is presented to all students who undertake this Machine learning advanced course. If you are a business manager or an executive, or a student who wants to learn and apply machine learning in Real world problems of business, this course will give you a solid base for that by teaching you some of the advanced technique of machine learning, which are Support Vector Machines.

Association Rules is one of the very important concepts of machine learning being used in market basket analysis. This course covers the working Principle of Association Mining and its various concepts like Support, Confidence, and Life in a very simplified manner. All of these algorithms has been explained by taking working examples. Parteek Bhatia is Professor in the Department of Computer Science and Engineering and Former Associate Dean of Student Affairs at Thapar Institute of Engineering and Technology, Patiala. At present he is on sabbatical at Tel Aviv University, Israel and acting as Visiting Professor at LAMBDA Lab, TAU.

In the new era of technology Artificial Intelligence (AI) is the next big thing. Keeping this in mind, at least 53 schools in Madhya Pradesh will teach students of classes 8 and 9 Artificial Intelligence (AI) from this academic session (2021-22). The Madhya Pradesh State Board of Open School Education is set to launch the study of emerging technology artificial intelligence (AI) as separate subjects in the school curriculum. For this, global tech giant Microsoft has been hired to teach the students and also train state teachers to enhance their understanding of Artificial Intelligence (AI). Around 1,500 teachers and over 40,000 students will be benefitted from this project.

With the development of deep learning, medical image classification has been significantly improved. However, deep learning requires massive data with labels. While labeling the samples by human experts is expensive and time-consuming, collecting labels from crowd-sourcing suffers from the noises which may degenerate the accuracy of classifiers. Therefore, approaches that can effectively handle label noises are highly desired. Unfortunately, recent progress on handling label noise in deep learning has gone largely unnoticed by the medical image. To fill the gap, this paper proposes a noise-tolerant medical image classification framework named Co-Correcting, which significantly improves classification accuracy and obtains more accurate labels through dual-network mutual learning, label probability estimation, and curriculum label correcting. On two representative medical image datasets and the MNIST dataset, we test six latest Learning-with-Noisy-Labels methods and conduct comparative studies. The experiments show that Co-Correcting achieves the best accuracy and generalization under different noise ratios in various tasks. Our project can be found at: https://github.com/JiarunLiu/Co-Correcting.