You might have optimal machine learning algorithm to solve your problem. But once you apply it in real world soon you will realize that you need to train it on more data. Due to lack of large dataset you will try to further optimize the algorithm, tune hyper-parameters or look for some low tech approach. Most state of the art machine learning models are trained on large datasets. Real world performance of machine learning solutions drastically improves with more data.

Then this course is for you! This course has been designed by Code Warriors the ML Enthusiasts so that we can share our knowledge and help you learn complex theories, algorithms, and coding libraries in a simple way. We will walk you step-by-step into the World of Machine Learning. With every tutorial, you will develop new skills and improve your understanding of this challenging yet lucrative sub-field of Data Science. This course is fun and exciting, but at the same time, we dive deep into Machine Learning.

Two years ago, the Google News Initiative partnered with the London School of Economics and Political Science to launch JournalismAI, a global effort to foster media literacy in newsrooms through research, training and experimentation. Since then, more than 62 thousand journalists have taken Introduction to Machine Learning, an online course provided in 17 languages in partnership with Belgian broadcaster VRT. More than 4,000 people have downloaded the JournalismAI report, which argued that "robots are not going to take over journalism" and that media organizations are keen to collaborate with one another and with technology companies. And over 20 media organizations including La Nación, Reuters, the South China Morning Post and The Washington Post have joined Collab, a global partnership to experiment with AI. To mark this anniversary, together with the London School of Economics, we are hosting a week-long online event to bring together international academics, publishers and practitioners.

A new review published in the Journal of Research in Science Teaching highlights the potential of machine learning--a subset of artificial intelligence--in science education. Although the authors initiated their review before the COVID-19 outbreak, the pandemic highlights the need to examine cutting-edge digital technologies as we re-think the future of teaching and learning. Based on a review of 47 studies, investigators developed a framework to conceptualize machine learning applications in science assessment. The article aims to examine how machine learning has revolutionized the capacity of science assessment in terms of tapping into complex constructs, improving assessment functionality, and facilitating scoring automaticity. Based on their investigation, the researchers identified various ways in which machine learning has transformed traditional science assessment, as well as anticipated impacts that it will likely have in the future (such as providing personalized science learning and changing the process of educational decision-making).

With the global pandemic in place, almost every college and university has moved towards e-learning platforms. With the introduction of the learning management system in different parts of the world, it has become easier for schools, colleges, and universities to reach out to students. E-learning has had its share of success. Stats show that the retention rate for students taking classes is more when compared to traditional classroom learning. The learning management system has proved to be an added advantage.

This is very common to switch to data science. Most data scientists I know out there do not have a degree in data science. They switched from another area. I also know many people who are trying to switch from another major. I meet many people being confused if it is the right career track for them.

Amazon Web Services (AWS) yesterday announced five new machine learning services aimed at helping companies in the industrial and manufacturing sectors embed intelligence in their production processes. The new services – Amazon Monitron, Amazon Lookout for Equipment, the AWS Panorama Appliance, the AWS Panorama SDK, and Amazon Lookout for Vision – are designed to help these companies to improve operational efficiency, quality control, security, and workplace safety. The services combine sophisticated machine learning, sensor analysis, and computer vision capabilities to address common technical challenges faced by industrial customers, and represent the most comprehensive suite of cloud-to-edge industrial machine learning services available. "Industrial and manufacturing customers are constantly under pressure from their shareholders, customers, governments, and competitors to reduce costs, improve quality, and maintain compliance. These organizations would like to use the cloud and machine learning to help them automate processes and augment human capabilities across their operations, but building these systems can be error prone, complex, time consuming, and expensive," said Swami Sivasubramanian, vice president of Amazon Machine Learning for AWS.

Existing studies on question answering on knowledge bases (KBQA) mainly operate with the standard i.i.d assumption, i.e., training distribution over questions is the same as the test distribution. However, i.i.d may be neither reasonably achievable nor desirable on large-scale KBs because 1) true user distribution is hard to capture and 2) randomly sample training examples from the enormous space would be highly data-inefficient. Instead, we suggest that KBQA models should have three levels of built-in generalization: i.i.d, compositional, and zero-shot. To facilitate the development of KBQA models with stronger generalization, we construct and release a new large-scale, high-quality dataset with 64,331 questions, GrailQA, and provide evaluation settings for all three levels of generalization. In addition, we propose a novel BERT-based KBQA model. The combination of our dataset and model enables us to thoroughly examine and demonstrate, for the first time, the key role of pre-trained contextual embeddings like BERT in the generalization of KBQA.

In recent years, speech emotion recognition technology is of great significance in industrial applications such as call centers, social robots and health care. The combination of speech recognition and speech emotion recognition can improve the feedback efficiency and the quality of service. Thus, the speech emotion recognition has been attracted much attention in both industry and academic. Since emotions existing in an entire utterance may have varied probabilities, speech emotion is likely to be ambiguous, which poses great challenges to recognition tasks. However, previous studies commonly assigned a single-label or multi-label to each utterance in certain. Therefore, their algorithms result in low accuracies because of the inappropriate representation. Inspired by the optimally interacting theory, we address the ambiguous speech emotions by proposing a novel multi-classifier interactive learning (MCIL) method. In MCIL, multiple different classifiers first mimic several individuals, who have inconsistent cognitions of ambiguous emotions, and construct new ambiguous labels (the emotion probability distribution). Then, they are retrained with the new labels to interact with their cognitions. This procedure enables each classifier to learn better representations of ambiguous data from others, and further improves the recognition ability. The experiments on three benchmark corpora (MAS, IEMOCAP, and FAU-AIBO) demonstrate that MCIL does not only improve each classifier's performance, but also raises their recognition consistency from moderate to substantial.

Almost all novice data scientists and machine learning developers are being confused about picking a programming language. They always ask which programming language will be best for their machine learning and data science project. Either we will go for python, R, or MatLab. Well, the choice of a programming language depends on developers' preference and system requirements. Among other programming languages, R is one of the most potential and splendid programming languages that have several R machine learning packages for both ML, AI, and data science projects.