This book introduces probability, statistics and stochastic processes to students. It can be used by both students and practitioners in engineering, various sciences, finance, and other related fields. It provides a clear and intuitive approach to these topics while maintaining mathematical accuracy. You can also find courses and videos online.

Description HERE IS WHY YOU SHOULD TAKE THIS COURSE: This course your complete guide to both supervised & unsupervised learning using Python. This means, this course covers all the main aspects of practical data science and if you take this course, you can do away with taking other courses or buying books on Python based data science. In this age of big data, companies across the globe use Python to sift through the avalanche of information at their disposal.. By becoming proficient in unsupervised & supervised learning in Python, you can give your company a competitive edge and boost your career to the next level. LEARN FROM AN EXPERT DATA SCIENTIST WITH 5 YEARS OF EXPERIENCE: My name is Minerva Singh and I am an Oxford University MPhil (Geography and Environment) graduate.

Hi, Welcome to The Python Programming A-Z Definitive Diploma in 2021. Almost Python is currently used for everything in Software Engineering. Although it is very easy to learn, but, it is very useful and powerful. Its fields are so many, and by learning python, it will be very easy for you to get higher Jobs in the largest companies such as Google, Dropbox, Spotify and many more. Simply you can do multi scale tasks with python, because it is multipurpose professionally and quickly with fewer lines of code.

This lecture attempts to demystify conversational AI by covering its counterparts that include, but not limited to: Automatic Speech Recognition, Natural Language Processing & Understanding, Text-to-Speech Synthesis, Intention Extraction and Identification, etc.. We use NVIDIA's Jarvis, an application framework for multimodal conversational AI services that delivers real-time performance on GPUs, to perform sophisticated conversational AI tasks. By the end of the lecture, we present a Question/Answering Demo powered by NVIDIA's Jarvis. About "True conversational AI is a voice assistant that can engage in human-like dialogue, capturing context and providing intelligent responses. Such AI models must be massive and highly complex," Sid Sharma from'What Is Conversational AI?'.

This tutorial covers how to use TinyML with ESP32-CAM. It describes how to classify images using ESP32-CAM with a machine learning model running directly on the device. To do it, it is necessary to create a machine learning model using Tensorflow lite and shrink the model. There are several ways to do it, this tutorial uses Edge Impulse that simplifies all the steps. We will explore the power of TinyML with ESP32-CAM to recognize and classify images.

To enable mobile users to leverage IBM Watson services through a messenger app, complete this developer code pattern and build a framework that can act as an intermediator in connecting Watson services to WhatsApp Messenger. There are currently 2.4 billion users on WhatsApp, and the number keeps climbing. For medium and large businesses, WhatsApp introduced WhatsApp Business, which powers communication with customers all over the world so they can connect with businesses on WhatsApp in a simple, secure, and reliable way. To make the conversations smarter, Watson AI can be infused as the back end to deliver advanced AI capabilities to customers. In this code pattern, you will learn to build a framework to connect Watson Machine Learning, deploy a simple house price-prediction model, and access it from your WhatsApp Messenger.

Automatic multiple-choice question generation (MCQG) is a useful yet challenging task in Natural Language Processing (NLP). It is the task of automatic generation of correct and relevant questions from textual data. Despite its usefulness, manually creating sizeable, meaningful and relevant questions is a time-consuming and challenging task for teachers. In this paper, we present an NLP-based system for automatic MCQG for Computer-Based Testing Examination (CBTE).We used NLP technique to extract keywords that are important words in a given lesson material. To validate that the system is not perverse, five lesson materials were used to check the effectiveness and efficiency of the system. The manually extracted keywords by the teacher were compared to the auto-generated keywords and the result shows that the system was capable of extracting keywords from lesson materials in setting examinable questions. This outcome is presented in a user-friendly interface for easy accessibility.

Science and technology curricula in UK schools are'not fit for purpose' and need to be updated to help pupils'change the world for the better', teachers have warned. Polls taken amid COVID-19 on behalf of the Amazon Longitude Explorer Prize found 47 per cent of teachers think the technology curriculum, specifically, is out of date. More than half of teachers said they had not the support to ensure lessons kept pace with advances and 59 per cent said resource shortages were limiting lesson plans. And 59 per cent said the STEM (Science, Technology, Engineering and Mathematics) curricula constrain their ability to help students reach their potential. The findings also flagged issues that have arisen specifically as a consequence of the COVID-19 pandemic -- including the impact such has had on practical teaching.

Description Would you like to be able to develop and prepare the data you need to pose, explore, and answer the most pressing and complex questions in your field of research? This course concerns itself with one of the most demanding and least covered parts of developing a predictive model for precision agriculture, or just about anything: sampling. When studying machine learning through video tutorials you normally access somebody's dataset and learn how to apply algorithms. But how were those neat datasets created? This course details how to use and adapt to your unique needs some tools I developed to sample just about any spatially explicit variable through the Google Earth Engine Platform.

The original version of this article appeared in Toward Data Science. When I started teaching data science and artificial intelligence in Duke University's Pratt School of Engineering, I was frustrated by how little insight I actually felt I had into how effective my teaching was, until the end-of-semester final exam grades and student assessments came in. Being new to teaching, I spent time reading up on pedagogical best practices and how methods like mastery learning and one-on-one personalized guidance could drastically improve student outcomes. Yet even with my relatively small class sizes I did not feel I had enough insight into each individual student's learning to provide useful personalized guidance to them. In the middle of the semester, if you had asked me to tell you exactly what a specific student had mastered from the class to date and where he or she was struggling, I would not have been able to give you a very good answer.