Lasse Rouhiainen is a best-selling author and international expert on artificial intelligence, disruptive technologies and digital marketing. Finnish in origin but based in Spain, Lasse focuses his work on investigating how companies and society in general can better adapt to, and benefit from, artificial intelligence. Lasse has given keynote presentations, seminars and workshops in more than 16 countries around the world and holds frequent conferences at several universities internationally. He has also provided training to thousands of students and businesses through online e-learning courses. Lasse has been a speaker at renowned seminars such as Mobile World Capital and TEDx, and has worked with top brands and institutions such as Michelin, Össur and the European Union Intellectual Property Office.

New Delhi: Two startups incubated at the Indian Institute of Technology, Madras, have joined hands with a mission to create 1,00,000 experts in artificial intelligence (AI) and deep learning by 2020. GUVI, which offers a platform for students in Tier 2,3 cities, to learn in vernacular languages, is now collaborating with One Fourth Labs, a startup founded by a IIT Madras faculty, which offers advanced AI courses at nominal fees, starting at ₹1,000. AI is one of the dominant technologies of this generation, which has helped machines reach human-level performance on specific tasks such as identifying faces, classifying images, playing complex strategy games, detecting anomalies in medical images and so on. There is a huge demand for AI talent in India, but the supply is limited due to a shortage of affordable courses, which take students from basics to advanced topics. GUVI will be the platform partner and One Fourth Labs will be the content partner for the joint initiative.

The social enterprise is on the rise--a signal of a broader shift in consumer culture and a growing expectation that private companies will make a positive impact on the world. But, how do we measure impact? For years, business leaders have clamored for actionable, often automated, data to measure the return on investment (ROI) of their initiatives. As a result, the business world has become infused with the buzzword, "data-driven." Measuring the effectiveness of solutions on outcomes is important for all businesses, but it is particularly critical for companies whose outcomes are intended to improve our society.

Forums play an important role in providing a platform for community interaction. The introduction of irrelevant content or spam by individuals for commercial and social gains tends to degrade the professional experience presented to the forum users. Automated moderation of the relevancy of posted content is desired. Machine learning is used for text classification and finds applications in spam email detection, fraudulent transaction detection etc. The balance of classes in training data is essential in the case of classification algorithms to make the learning efficient and accurate. However, in the case of forums, the spam content is sparse compared to the relevant content giving rise to a bias towards the latter while training. A model trained on such biased data will fail to classify a spam sample. An approach based on Synthetic Minority Over-sampling Technique(SMOTE) is presented in this paper to tackle imbalanced training data. It involves synthetically creating new minority class samples from the existing ones until balance in data is achieved. The enhanced data is then passed through various classifiers for which the performance is recorded. The results were analyzed on the data of forums of Spoken Tutorial, IIT Bombay over standard performance metrics and revealed that models trained after Synthetic Minority oversampling outperform the ones trained on imbalanced data by substantial margins. An empirical comparison of the results obtained by both SMOTE and without SMOTE for various supervised classification algorithms have been presented in this paper. Synthetic oversampling proves to be a critical technique for achieving uniform class distribution which in turn yields commendable results in text classification. The presented approach can be further extended to content categorization on educational websites thus helping to improve the overall digital learning experience.

John C. Mauro is Professor of Materials Science and Engineering at the Pennsylvania State University. John earned a B.S. in Glass Engineering Science (2001), B.A. in Computer Science (2001), and Ph.D. in Glass Science (2006), all from Alfred University. He joined Corning Incorporated in 1999 and served in multiple roles there, including Senior Research Manager of the Glass Research department. Mauro is the inventor or co-inventor of several new glass compositions for Corning, including Corning Gorilla Glass products. Mauro joined the faculty at Penn State in 2017 and is currently a world-recognized expert in fundamental and applied glass science, statistical mechanics, computational and condensed matter physics, thermodynamics, and the topology of disordered networks.

As if manufacturers didn't already have enough on their hands trying to find suitable applicants for their shop floors and R&D departments, the world of artificial intelligence is about to explode onto the scene. And when it does, the scramble for talent will only grow maddeningly tougher. This may sound like trouble, but there's a tremendous upside. According to a newly released study by the MAPI Foundation and the Information Technology and Innovation Foundation (ITIF), not only will AI enable machines to do a lot more--but it will also empower humans to do a lot more as well. That means an upsurge of new kinds of jobs related to developing new AI solutions, leading new AI business strategies and supervising AI implementations.

Machine Learning is a field of computer science concerned with developing systems that can learn from data. Like statistics and linear algebra, probability is another foundational field that supports machine learning. Probability is a field of mathematics concerned with quantifying uncertainty. Many aspects of machine learning are uncertain, including, most critically, observations from the problem domain and the relationships learned by models from that data. As such, some understanding of probability and tools and methods used in the field are required by a machine learning practitioner to be effective.

The following is a guest post by Pete McCain, a technology startup enthusiast associated with App Velocity. If you would like to submit a guest post, please contact us. There was a time when we all were highly skeptical about online education because we couldn't fathom a computer screen replacing our classrooms and the education ideals that come with them. But now examining the impact of online education, we can clearly see how eagerly we've embraced the idea of e-learning. It has levelled up education in the developed parts of the world and democratized education where schools and teachers couldn't reach.

Who are the primary implementers of machine learning and data science today? A new market research report shows that large enterprises and smaller businesses are the first movers. That's because big companies have the money to invest and smaller ones are unencumbered by long chains of command. Mid-sized enterprises are having a harder time. Without the resources of the bigger players or the agility of the little players, they are slower to implement data science and machine learning. But if they take a smart approach to their efforts, they can get significant value out of where they do invest.

Our Placement Center assures all candidates who complete our courses a placement in the Industry. If you are in search of a kickstart for your career in Artificial Intelligence, Machine Learning or Data Science then you are in the right place.