The metaphorical data explosion of the current age has changed how organizations function. Today, with the right amounts of effort and investment in technology and expertise, business heads can take operational and other major decisions with greater success as they're based on cold statistics and empirical evidence instead of--as was the case in the days of yore--inadequately supported gut feelings and instinct. The application of business intelligence is also much more widespread now, with organizations in several sectors using machine learning, computer vision, NLP, IoT and other data-oriented technologies to collect, process and analyze the data collected before putting it to use in a variety of ways. An often-overlooked aspect of business intelligence is the need to incorporate a long-term data-driven culture at the workplace in a way that complex analytical terms and working mechanisms become second nature for all stakeholders in any organization. Only organizations with high data fluency can foster such a culture for the long term.

The series is designed to help you get started on a Microsoft Azure AI learning journey. Not matter your previous knowledge of AI, this actionable set of sessions will take you through what is available in Azure AI, Computer Vision and Conversational AI and give you space to ask a set of experts questions about each topic. We will only scratch the surface of what is available on Microsoft Learn and will encourage you to continue your learning between weekly sessions in the series. This series partners with the Microsoft Certified: Azure AI Fundamentals certification – if you follow this learning path you will be well on your way to taking this exam. Join us for this live learning experience where you will be guided by subject matter experts through the Learn modules below in real time along with developers around the globe.

Do you have a question, "How to Learn Data Analytics for Free?"… If yes, this article is for you. In this article, you will get a step by step guidance along with the free resources to learn data analytics. So without any further ado, let's get started- Before I answer this question, let's have a look at mandatory skills for Data Analysts. So, these are must-have skills for a Data Analysts.

KARACHI: More than 25,000 students from different parts of Sindh participated in the Presidential Initiative for Artificial Intelligence and Computing (PIAIC) grand entrance test 2022 here at the National Stadium Karachi (NSK) on Sunday. The initiative is launched to empower youth through training and providing them financial support to become entrepreneurs. Organised by the Saylani Welfare International Trust (SWIT), the test was witnessed by President Dr Arif Alvi, who also addressed the participants and advised them to work hard and focus on information technology. President Alvi asks youth to avail PTI govt's loans scheme to become entrepreneur "Once you complete your education and training, you would avail thousands of opportunities in this sector across the world," he said in his address before the test. "The government is extending all kinds of support to the IT sector and laws have been made to facilitate the growth of this sector," he said.

You will find in this course a consice review of the theory with intuitive concepts of the algorithms, and you will be able to put in practice your knowledge with many practical examples using your own datasets.

Decision trees are a supervised learning method used to build a model that predicts the value of a target variable by learning simple decision rules from the data features. DTs are used for both classification and regression and are simple to understand and interpret. Below, we have listed down the top online courses, YouTube videos and guides for enthusiasts to master decision trees. The course by CodeAcademy focuses on teaching developers how to build and use decision trees and random forests. The course looks at two methods in detail: Gini impurity and Information Gain.

If you are thinking of learning Data Science, Machine learning (ML), or Deep Learning (DL), you are not alone; more and more people are starting with these advanced skills worldwide. I have seen a lot of interest from software engineers in the ML and AI space. They are totally caught up with the craze of developing programs that can recognize numbers, alphabets, vehicles, and several other image scanning stuff. The craze is very similar to what the 1980's programmer has about video games, where moving a character on screen gives the joy you get when your program correctly identifies the number or letter you make from hand. From college graduates to junior programmers and from experienced programmers to software architects, all show interest in ML and AI to become part of the next technical revolution we may be witnessing.

Artificial Intelligence (AI) and robots often seem like fun science fiction, but in fact already affect our daily lives. For example, services like Google and Amazon help us find what we want by using AI. Every aspect of how Facebook works is based on AI and Machine Learning (ML). The reason your phone is so useful is it is full of AI -- sensing, acting, and learning about you. All these tools not only make us smarter, their intelligence is based partly on what they learn both from us and about us when we use them.

Even staple figures in the field of science such as Stephen Hawking and Elon Musk have been vocal about technology's threat against humanity. The facts of the matter are that machines generally operate how they're programmed to and we are a long way from developing the ASI (artificial superintelligence) needed for this "takeover" to even be feasible. At present, most of the AI technology utilized by machines is considered "narrow" or "weak," meaning it can only apply its knowledge towards one or a few tasks. "Machine learning and AI systems are a long way from cracking the hard problem of consciousness and being able to generate their own goals contrary to their programming," George Montanez, a data scientist at Microsoft, wrote under the same Metafact thread. Some of these risks include overoptimization, weaponization, and ecological collapse, according to Ben Nye, the Director of Learning Sciences at the University of Southern California, Institute for Creative Technologies (USC-ICT).

I vividly remember my high school Chemistry teacher teaching us about covalent bonds using a 3D model of a water molecule. I also remember enjoying my time in the Chemistry lab trying to determine if a given salt is more acidic or alkaline by performing many tests. How would this setup change if we needed to replace the human performing these experiments with a machine? Recently, my curiosity about applying deep learning architectures in the life sciences resulted in an interesting learning opportunity. I stumbled onto some libraries like RDKit and DeepChem that help with training and developing deep learning data models for use in Drug Discovery.