We have covered each and every topic in detail and also learned to apply them to real-world problems. There are lots and lots of exercises for you to practice and also 2 bonus NLP Projects "Sentiment analyzer" and "Drugs Prescription using Reviews". In this Sentiment analyzer project, you will learn how to Extract and Scrap Data from Social Media Websites and Extract out Beneficial Information from these Data for Driving Huge Business Insights. In this Drugs Prescription using Reviews project, you will learn how to Deal with Data having Textual Features, you will also learn NLP Techniques to transform and Process the Data to find out Important Insights. You will make use of all the topics read in this course. You will also have access to all the resources used in this course. Enroll now and become a master in machine learning.
This is an intermediate-level free artificial intelligence course. This course will teach the basics of modern AI as well as some of the representative applications of AI including machine learning, probabilistic reasoning, robotics, computer vision, and natural language processing. To understand this course, you should have some previous understanding of probability theory and linear algebra.
The objective of personalized learning is to design an effective knowledge acquisition track that matches the learner's strengths and bypasses her weaknesses to ultimately meet her desired goal. This concept emerged several years ago and is being adopted by a rapidly-growing number of educational institutions around the globe. In recent years, the boost of artificial intelligence (AI) and machine learning (ML), together with the advances in big data analysis, has unfolded novel perspectives to enhance personalized education in numerous dimensions. By taking advantage of AI/ML methods, the educational platform precisely acquires the student's characteristics. This is done, in part, by observing the past experiences as well as analyzing the available big data through exploring the learners' features and similarities. It can, for example, recommend the most appropriate content among numerous accessible ones, advise a well-designed long-term curriculum, connect appropriate learners by suggestion, accurate performance evaluation, and the like. Still, several aspects of AI-based personalized education remain unexplored. These include, among others, compensating for the adverse effects of the absence of peers, creating and maintaining motivations for learning, increasing diversity, removing the biases induced by the data and algorithms, and the like. In this paper, while providing a brief review of state-of-the-art research, we investigate the challenges of AI/ML-based personalized education and discuss potential solutions.
In the Internet of Things (IoT) era, billions of sensors and devices collect and process data from the environment, transmit them to cloud centers, and receive feedback via the internet for connectivity and perception. However, transmitting massive amounts of heterogeneous data, perceiving complex environments from these data, and then making smart decisions in a timely manner are difficult. Artificial intelligence (AI), especially deep learning, is now a proven success in various areas including computer vision, speech recognition, and natural language processing. AI introduced into the IoT heralds the era of artificial intelligence of things (AIoT). This paper presents a comprehensive survey on AIoT to show how AI can empower the IoT to make it faster, smarter, greener, and safer. Specifically, we briefly present the AIoT architecture in the context of cloud computing, fog computing, and edge computing. Then, we present progress in AI research for IoT from four perspectives: perceiving, learning, reasoning, and behaving. Next, we summarize some promising applications of AIoT that are likely to profoundly reshape our world. Finally, we highlight the challenges facing AIoT and some potential research opportunities.
Online Courses Udemy - Complete Machine Learning with R Studio - ML for 2020, Linear & Logistic Regression, Decision Trees, XGBoost, SVM & other ML models in R programming language - R studio 4.1 (41 ratings), Created by Start-Tech Academy, English [Auto-generated] Preview this Udemy course -. GET COUPON CODE Description In this course we will learn and practice all the services of AWS Machine Learning which is being offered by AWS Cloud. There will be both theoretical and practical section of each AWS Machine Learning services.This course is for those who loves machine learning and would build application based on cognitive computing, AI and ML. You could integrate these services in your Web, Android, IoT, Desktop Applications like Face Detection, ChatBot, Voice Detection, Text to custom Speech (with pitch, emotions, etc), Speech to text, Sentimental Analysis on Social media or any textual data. Machine Learning Services like- Amazon Sagemaker to build, train, and deploy machine learning models at scale Amazon Comprehend for natural Language processing and text analytics Amazon Lex for conversational interfaces for your applications powered by the same deep learning technologies as Alexa Amazon Polly to turn text into lifelike speech using deep learning Object and scene detection,Image moderation,Facial analysis,Celebrity recognition,Face comparison,Text in image and many more Amazon Transcribe for automatic speech recognition Amazon Translate for natural and accurate language translation As Machine learning and cloud computing are trending topic and also have lot of job opportunities If you have interest in machine learning as well as cloud computing then this course for you.
Recent years have witnessed the rising popularity of Natural Language Processing (NLP) and related fields such as Artificial Intelligence (AI) and Machine Learning (ML). Many online courses and resources are available even for those without a strong background in the field. Often the student is curious about a specific topic but does not quite know where to begin studying. To answer the question of "what should one learn first," we apply an embedding-based method to learn prerequisite relations for course concepts in the domain of NLP. We introduce LectureBank, a dataset containing 1,352 English lecture files collected from university courses which are each classified according to an existing taxonomy as well as 208 manually-labeled prerequisite relation topics, which is publicly available. The dataset will be useful for educational purposes such as lecture preparation and organization as well as applications such as reading list generation. Additionally, we experiment with neural graph-based networks and non-neural classifiers to learn these prerequisite relations from our dataset.