Description Artificial Intelligence usage is exploding across the global and jobs are booming for this profession. There is a severe shortage of AI professionals globally as everyone from startups to tech giants to governments is jumping onboard the AI revolution. If you answered YES then this course is for you! This course is specifically designed to take away the complexity and mystery surrounding AI and Machine Learning and make it accessible for average IT guy who does not know advanced programming or data science. It will teach you the core concepts of AI / Machine Learning and then make you actually implement them using freely available services so that you get actual practical experience!

Blog - New features of Python 3.8 Sat, 30 Nov 2019 In this blog, I show how to use new features of Python 3.8. Video Tutorial - Upcasting and Downcasting in Java Wed, 27 Nov 2019 In this video, I explain upcasting and downcasting in Java Video Tutorial - How to use Lamdba Expressions in Java Sun, 24 Nov 2019 In this video, I demonstrate how to use Lambda Expression, Lambda Blocks, and Method Reference in Java 8 and above. Video Tutorial - Top-N Analysis in Oracle Database Tue, 19 Nov 2019 In this video, I show how to perform Top-N Analysis in Oracle Database 11g and 18c. Video Tutorial - Why to override equals(), hashCode() and toString() methods of Object class in Java. Sat, 16 Nov 2019 In this video, I explain why to overriding equals(), hashCode() and toString() methods of Object class in Java.

Recently, Andrew Ng took to the professional networking platform to announce that the Specialization on Machine Learning Engineering for Production (MLOps) by DeepLearning.AI is now available on Coursera. The Machine Learning Engineering for Production (MLOps) Specialization covers how to conceptualize, build, and maintain integrated systems that continuously operate in production. NG posted, "I'm thrilled DeepLearning.AI's Machine Learning Engineering for Production (MLOps) specialization is now available on Coursera!" "Being able to train ML models is essential. And, to build an effective AI career, you need production engineering skills as well, to build and deploy ML systems. With this specialization, you can grow your knowledge of ML into production-ready skills" the AI expert added.

Mixture of experts is an ensemble learning technique developed in the field of neural networks. It involves decomposing predictive modeling tasks into sub-tasks, training an expert model on each, developing a gating model that learns which expert to trust based on the input to be predicted, and combines the predictions. Although the technique was initially described using neural network experts and gating models, it can be generalized to use models of any type. As such, it shows a strong similarity to stacked generalization and belongs to the class of ensemble learning methods referred to as meta-learning. In this tutorial, you will discover the mixture of experts approach to ensemble learning.

Data scientists spend only 20 percent of their time on building machine learning algorithms and 80 percent of their time finding, cleaning, and reorganizing huge amounts of data. That mostly happen because many use graphical tools such as Excel to process their data. However, if you use a programming language such as Python you can drastically reduce the time it takes for processing your data and make them ready for use in your project. This course will show how Python can be used to manage, clean, and organize huge amounts of data. Data scientist is one of the hottest skill of 21st century and many organization are switching their project from Excel to Pandas the advanced Data analysis tool .

Python is one of the most widely used programming languages in the data science field. Python has many packages and libraries that are specifically tailored for certain functions, including pandas, NumPy, scikit-learn, Matplotlib, and SciPy. So if you are looking for the Best Books on Data Science with Python, then you should check these books. In this article, you will find 8 Best Books on Data Science with Python. These books will give you in-depth knowledge starting from basics to advanced level.

Have you ever asked yourself where you currently are on your Machine Learning journey? And what’s there that you can still learn about? This checklist helps you answer such questions. It provides an…

Although Natural Language Processing (NLP) is at the core of many tools young people use in their everyday life, high school curricula (in Italy) do not include any computational linguistics education. This lack of exposure makes the use of such tools less responsible than it could be and makes choosing computational linguistics as a university degree unlikely. To raise awareness, curiosity, and longer-term interest in young people, we have developed an interactive workshop designed to illustrate the basic principles of NLP and computational linguistics to high school Italian students aged between 13 and 18 years. The workshop takes the form of a game in which participants play the role of machines needing to solve some of the most common problems a computer faces in understanding language: from voice recognition to Markov chains to syntactic parsing. Participants are guided through the workshop with the help of instructors, who present the activities and explain core concepts from computational linguistics. The workshop was presented at numerous outlets in Italy between 2019 and 2021, both face-to-face and online.

Recently, maximizing mutual information has emerged as a powerful method for unsupervised graph representation learning. The existing methods are typically effective to capture information from the topology view but ignore the feature view. To circumvent this issue, we propose a novel approach by exploiting mutual information maximization across feature and topology views. Specifically, we first utilize a multi-view representation learning module to better capture both local and global information content across feature and topology views on graphs. To model the information shared by the feature and topology spaces, we then develop a common representation learning module using mutual information maximization and reconstruction loss minimization. To explicitly encourage diversity between graph representations from the same view, we also introduce a disagreement regularization to enlarge the distance between representations from the same view. Experiments on synthetic and real-world datasets demonstrate the effectiveness of integrating feature and topology views. In particular, compared with the previous supervised methods, our proposed method can achieve comparable or even better performance under the unsupervised representation and linear evaluation protocol.

The increasingly sophisticated field of artificial intelligence (AI) has grown and spawned several disciplines that deserve their own focused consideration, namely machine learning (ML) and the ML subset "deep learning." As it sounds, deep learning is the process of leveraging data analytics and the latest gains in computing power to enable computers to observe, learn, and respond to relatively complex situations faster than humans can. Given this rapid evolution in AI and its offshoots, there are now several good deep learning books available for those aspiring to master the technology. Although there may be concerns about AI taking peoples' jobs (Skynet, anyone?), the truth is that advances in AI--and by extension, deep learning--have generated a huge demand for talent. Whenever there is demand, job security and good wages tend to follow.