Edtech company Simplilearn has announced the findings of Career Impact Survey 2018, whose findings reveal that artificial intelligence (AI) and machine learning (ML) are the most widely chosen (25% respondents) domains for reskilling, followed by Big Data and data science (20%). Other new-age categories like digital marketing, cloud computing, cybersecurity, DevOps and agile & scrum together saw 55% uptake in reskilling among professionals. Certification courses helped (31%) professionals to enhance their performance, gain appreciation (29%), and 40% respondents who have taken certification courses admitted to feeling more confident at work. "Going digital is indispensable for a company's survival; it's crucial for professionals to upgrade skills to meet industry requirements", said Krishna Kumar, founder & CEO, Simplilearn.

I have been asked by quite a few people on how to start Machine Learning and Deep Learning. Here, I have curated a list of resources which I used and the path I took when I first learnt Machine Learning. I will keep on updating this article as I find more helpful resources. This will teach you the ropes of Machine Learning and will brush up your Linear Algebra skill a little bit. Make sure you do all the assignments and after you have completed the course, you will get a hold of Machine Learning concepts such as; Linear Regression, Logistics Regression, SVM, Neural Networks and K-means clustering.

TensorFlow, the open source software library developed by the Google Brain team, is a framework for building deep learning neural networks. It is also considered one of the best ways to build deep learning models by machine learning practitioners across the globe. In deep learning models, which rely on a lot of data and computing resources, TensorFlow is used significantly. Given its flexible architecture for easy deployment on various platforms such as CPUs, GPUs and TPUs, TensorFlow remains one of the favourite libraries to get into ML. Its huge popularity also means that tech enthusiasts are on a constant lookout to learn more and work more with this library.

Artificial intelligence (AI) and machine learning (ML) are the most widely chosen domains for reskilling among working tech professionals in India, according to the findings of education technology company Simplilearn. The firm's'Career Impact Survey 2018' which was aimed at analyzing the impact of professional certifications and reskilling among working professionals revealed that AI and ML domains were chosen by 25% of respondents. This was followed by big data and data science domains chosen by 20% of the participants. Other new age categories such as'digital marketing, cloud computing, cybersecurity, DevOps and Agile and Scrum' together saw 55% uptake in reskilling among professionals. The certification courses helped 31% of professionals to enhance their performance, gain manager and peer appreciation, according to the survey.

Machine Learning training bootcamp is a 3-day specialized training course that covers the essentials of machine learning, a shape and utilization of man-made reasoning (AI). Machine learning computerizes the information investigation process by empowering PCs, machines and IoT to learn and adjust through experience connected to particular undertakings without unequivocal programming. Learning Objectives: Learn about Artificial Intelligence and Machine Learning List similarities and differences between AI, Machine Learning and Data Mining Learn how Artificial Intelligence uses data to offer solutions to existing problems Explore how Machine Learning goes beyond AI to offer data necessary for a machine to learn, adapt and optimize / Clarify how Data Mining can serve as foundation for AI and machine learning to use existing information to highlight patterns List the various applications of machine learning and related algorithms Learn how to classify the types of learning such as supervised and unsupervised learning Implement supervised learning techniques such as linear and logistic regression Use unsupervised learning algorithms including deep learning, clustering and recommender systems (RS) used to help users find new items or services, such as books, music, transportation, people and jobs based on information about the user or the recommended item Learn about classification data and Machine Learning models Select the best algorithms applied to Machine Learning Make accurate predictions and analysis to effectively solve potential problems List Machine Learning concepts, principles, algorithms, tools and applications Learn the concepts and operation of support neural networks, vector machines, kernel SVM, naive bayes, decision tree classifier, random forest classifier, logistic regression, K-nearest neighbors, K-means and clustering Comprehend the theoretical concepts and how they relate to the practical aspects of machine learning / Be able to model a wide variety of robust machine learning algorithms including deep learning, clustering and recommendation systems Course Agenda and Topics: The Basics of Machine Learning Machine Learning Techniques, Tools and Algorithms Data and Data Science Review of Terminology and Principles Applied Artificial Intelligence (AI) and Machine Learning Popular Machine Learning Methods Learning Applied to Machine Learning Principal component Analysis Principles of Supervised Machine Learning Algorithms Principles of Unsupervised Machine Learning Regression Applied to Machines Learning Principles of Neural Networks Large Scale Machine Learning Introduction to Deep Learning Applying Machine Learning Overview of Algorithms Overview of Tools and Processes Request More Information .

The British University in Dubai has launched a bachelors degree in Artificial Intelligence, the first of its kind in the country. The programme has been developed in collaboration with the University of Edinburgh and is open to both Emiratis and expatriates. Emiratis on the course will receive scholarship from the ICT fund, while expatriates can apply for scholarships to British University in Dubai. The ICT Fund was launched by Telecommunications Regulatory Authority in 2007 to achieve rapid development within the information and communication technology sector in UAE. The four-year degree course will be starting at the end of September.

Massive open online courses (MOOCs) were supposed to bring a revolution in education. But they haven't lived up to expectations. We have been putting educators in front of cameras and shooting video -- just as the first TV shows did with radio stars, microphone in hand. This is not to say the millions of hours of online content are not valuable; the limits lie in the ability of the underlying technology to customize the material to the individual and to coach. That is about to change, though, through the use of virtual reality, artificial intelligence and sensors.

Today's tutorial is on saliency detection, the process of applying image processing and computer vision algorithms to automatically locate the most "salient" regions of an image. In essence, saliency is what "stands out" in a photo or scene, enabling your eye-brain connection to quickly (and essentially unconsciously) focus on the most important regions. For example -- consider the figure at the top of this blog post where you see a soccer field with players on it. When looking at the photo, your eyes automatically focus on the players themselves as they are the most important areas of the photo. This automatic process of locating the important parts of an image or scene is called saliency detection.

With the rapid advancements in cancer research, the information that is useful for characterizing disease, staging tumors, and creating treatment and survivorship plans has been changing at a pace that creates challenges when physicians try to remain current. One example involves increasing usage of biomarkers when characterizing the pathologic prognostic stage of a breast tumor. We present our semantic technology approach to support cancer characterization and demonstrate it in our end-to-end prototype system that collects the newest breast cancer staging criteria from authoritative oncology manuals to construct an ontology for breast cancer. Using a tool we developed that utilizes this ontology, physician-facing applications can be used to quickly stage a new patient to support identifying risks, treatment options, and monitoring plans based on authoritative and best practice guidelines. Physicians can also re-stage existing patients or patient populations, allowing them to find patients whose stage has changed in a given patient cohort. As new guidelines emerge, using our proposed mechanism, which is grounded by semantic technologies for ingesting new data from staging manuals, we have created an enriched cancer staging ontology that integrates relevant data from several sources with very little human intervention.

The internet is filled with tutorials to get started with Deep Learning. You can choose to get started with the superb Stanford courses CS221 or CS224, Fast AI courses or Deep Learning AI courses if you are an absolute beginner. All except Deep Learning AI are free and accessible from the comfort of your home. All you need is a good computer (preferably with a Nvidia GPU) and you are good to take your first steps into Deep Learning. This blog is however not addressing the absolute beginner.