As higher education costs continue to rise, students bear the ultimate burden of choosing the right school, major, and delivery format to maximize post-graduation success. Unlike previous generations, millennials and adult learners are searching for alternatives to full-time, on-campus programs, and universities are eager to offer non-traditional routes to a degree. Distance learning programs have existed since the 1980s, but technological innovation, content scalability, and widespread mobile adoption have enabled the online degree program to be a competitive option for aspiring students. Long gone are the days of aggressive marketing tactics and empty promises made by degree mills and unaccredited for-profit universities. Today, a learner can enroll in competitive bachelor's and master's programs at U Penn, Columbia, Johns Hopkins, NYU, and more.

Are you interested in learning how to integrate machine learning in your apps? Machine Learning is the future of digital transformation. And now you can learn it from the comfort of your home.. in your own time.. without having to attend class. My name is Mohammad Azam and I am the creator of many popular online courses including Mastering MapKit in iOS Using Swift and Creating Stickers and iMessages Applications in iOS 10 Using Swift 3, Mastering Micro Services Using JPA, Mastering Server Side Swift Using Vapor, Mastering ARKit for iOS and more. I have created over 2 dozens apps and some of my apps were even featured by Apple on the App Store.

This course will introduce you to the core capabilities of Artificial Intelligence (AI) and empower you to contribute to this exciting and transformational new era in global technological development. Developed by the pioneering Data Science team at the University of Southampton – ranked among the top 100 universities globally – this course will equip you with the specialist knowledge and skills you need to understand and effectively apply AI technologies within your organisation. Taught by industry-leading experts in AI and data science using a practical approach to learning, this 6-week flexible online course will see you exploring a range of AI capabilities in the context of real-life business case studies – teaching you how you can effectively harness the power of AI to transform your business.

Have a go at developing a cloud-native microservice on a fully open source and open standards stack. Develop a microservice using the Eclipse MicroProfile programming model and deploy it to the Open Liberty server. Finally, build and run the application in a Docker container ready for deployment to your favourite cloud. This lab will guide you in deploying the IBM IoT Platform starter service. You will learn how to define a sensor within the platform and simulate some sensor data which will then be used to create a dashboard for device monitoring.

When you begin to study machine learning, you'll soon realize the importance of KDnuggets. This tutorial is an invaluable resource for delving into data science statistics, as it covers core topics like sampling distribution and the central limit theorem.

Building Deep Learning models without callbacks is like driving a car with no functioning brakes -- you have little to no control over the whole process that is very likely to result in a disaster. In this article, you will learn how to monitor and improve your Deep Learning models using Keras callbacks like ModelCheckpoint and EarlyStopping. A callback is a set of functions to be applied at given stages of the training procedure. You can use callbacks to get a view on internal states and statistics of the model during training. You define and use a callback when you want to automate some tasks after every training/epoch that help you have controls over the training process.

Google's TensorFlow framework is the current leading software for implementing and experimenting with the algorithms that power AI and machine learning. We will embark on this journey by quickly wrapping up some important fundamental concepts, followed by a focus on TensorFlow to complete tasks in computer vision and natural language processing. You will be introduced to some important tips and tricks necessary for enhancing the efficiency of our models. We will highlight how TensorFlow is used in an advanced environment and brush through some of the unique concepts at the cutting edge of practical AI. If you want to develop a solid foundation on using TensorFlow and continue your journey into advancing the state of the art in AI to create your own smart machine learning solutions, this course is for you.

The world of technology is rapidly changing, and one must adapt to it quickly to survive in the race of'survival of the fittest'. The Department of EEE has a team of postgraduate students from the Indian Institute of Technology (IIT) Guwahati, along with their faculty members are developing an Artificial Intelligence-enabled Chatbot named "ALBELA" to teach and support the first-year students of Electrical & Electronics Engineering (EEE). ALBELA is capable of addressing the queries and doubts that each of the approximately 850 students pursuing EEE at the Indian Institute of Technology (IIT) may have and can even scan and convert documents, PDFs or Word and provide results relevant to the query asked. "We have been working on its development since the last 7 months with a team of dedicated 7 research scholars of the department. Earlier we did the trial runs of the Chatbot and started using from this academic session onwards. The response from the students has been overwhelming and we hope that this will become the new normal shortly. Prof. Rohit Sinha, Head EEE Department, and the team IBM have extended their continuous support for this activity."

Bayes Theorem provides a principled way for calculating a conditional probability. It is a deceptively simple calculation, although it can be used to easily calculate the conditional probability of events where intuition often fails. Bayes Theorem also provides a way for thinking about the evaluation and selection of different models for a given dataset in applied machine learning. Maximizing the probability of a model fitting a dataset is more generally referred to as maximum a posteriori, or MAP for short, and provides a probabilistic framework for predictive modeling. In this post, you will discover Bayes Theorem for calculating conditional probabilities.

Automatic analysis of teacher and student interactions could be very important to improve the quality of teaching and student engagement. However, despite some recent progress in utilizing multimodal data for teaching and learning analytics, a thorough analysis of a rich multimodal dataset coming for a complex real learning environment has yet to be done. To bridge this gap, we present a large-scale MUlti-modal Teaching and Learning Analytics (MUTLA) dataset. This dataset includes time-synchronized multimodal data records of students (learning logs, videos, EEG brainwaves) as they work in various subjects from Squirrel AI Learning System (SAIL) to solve problems of varying difficulty levels. The dataset resources include user records from the learner records store of SAIL, brainwave data collected by EEG headset devices, and video data captured by web cameras while students worked in the SAIL products. Our hope is that by analyzing real-world student learning activities, facial expressions, and brainwave patterns, researchers can better predict engagement, which can then be used to improve adaptive learning selection and student learning outcomes. An additional goal is to provide a dataset gathered from the real-world educational activities versus those from controlled lab environments to benefit educational learning community.