Youtube has become a major source of information and an educational platform for many. It has an array of lessons and tutorials to learn any subject, including topics in computer science. Unlike subscription-based ed-tech models, most of the content on it is free.
Programmers, enthusiastic about teaching data science, artificial intelligence, machine learning, and deep learning are also very active on the platform and have well-established channels with videos right from the basics of programming to complex subjects of the field.
We enlisted the top programmers teaching these subjects on YouTube. Choosing the right one will depend on your experience in the field and the kind of projects you want to work on.
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This is a complete guide to start and improve your knowledge of machine learning (ML), artificial intelligence (AI) in 2021 without ANY background in the field and stay up-to-date with the latest news and state-of-the-art techniques!
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Recently, researchers from Google proposed the solution of a very fundamental question in the machine learning community — What is being transferred in Transfer Learning? They explained various tools and analyses to address the fundamental question.
The ability to transfer the domain knowledge of one machine in which it is trained on to another where the data is usually scarce is one of the desired capabilities for machines. Researchers around the globe have been using transfer learning in various deep learning applications, including object detection, image classification, medical imaging tasks, among others.
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The technology in the IT sector is rapidly growing with everything in the world moving online to make users life easy with it. This development in technology has allowed critical industries to also move online with technologies like blockchain, Artificial intelligence, Cloud Computing, Big Data Service, etc.
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Machine learning (ML) is an application of artificial intelligence (AI). Machine learning equips the systems with the ability to automatically learn and make improvements from experience without being explicitly programmed. The ML algorithms employ statistics to find patterns in massive patterns of data and use them to learn for themselves.
The goal of ML is to allow computers to learn automatically without any intervention or input, or assistance from humans. The data used for learning comprises numbers, images, words, etc. According to a recent study, 77% of the devices that we use today utilize ML facilities.
Since the time ML is becoming more widely adopted, a parallel trend with open access to models is also witnessing a rise in its popularity and development. The large companies developing ML are raising the bar for model performance in parallel as well. This is possible due to the large and comprehensive datasets that are available with them, which they use to train models by dedicated ML practitioners.
Hyper-automation supports the idea of almost anything inside a company can be automated. It has been gaining popularity for some time around the world now, but with the pandemic last year, its necessity and emphasis on it has increased even further. Intelligent process automation and digital process automation has experienced a boost.
In today’s times, producing a working ML model that makes fairly good predictions is not enough. The ML practitioners require model interpretability wherein they understand why predictions are being made before deciding whether the model should go into production. This is often important in the case of enterprises where the predictions are scrutinized for societal factors such as social justice, ethics and fairness.
ML can contribute towards business forecasting and help in making important, informed decisions related to business. The experts gather and screen a set of data over a fixed period of time, which is then utilized for making smart decisions. Once ML is trained with diverse data sets, it can provide conjectures with accuracy as high as approximately 95%.
Economic analyst Transforma Insights has forecasted that the IoT market will develop 24.1 billion devices in 2030, leading to $1.5 trillion in income throughout the world due to its rapid development.
It is predicted inference at the edge will grow substantially throughout 2021. Among the various factors contributing to this growth, the main two are the growth of IoT and a greater reliance on devices for doing remote work.
With all the learnt skills you can get active on other competitive platforms as well to test your skills and get even more hands-on. If you are interested to learn more about the course, check out the page of PG Diploma in Machine Learning & AI and talk to our career counsellor for more information.
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