Should we be afraid of AI? People are scared of the advancements made in the field of Articial Intelligence. technology is booming very rapidly and various innovations are being made in the field of artificial intelligence. With all these advancements taking us through everyday life, there is a common question by various people around the globe – SHOULD WE BE AFRAID OF AI ? A hundred years from now – we could see astonishing things happening in this world with respect to this field. But no matter what, we should always know that human beings cannot be replaced or replicated by any technology and AI cannot be the downfall of humanity.
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Simplilearn’s Artificial Intelligence course provides training in the skills required for a career in AI. You will master TensorFlow, Machine Learning and other AI concepts, plus the programming languages needed to design intelligent agents, deep learning algorithms & advanced artificial neural networks that use predictive analytics to solve real-time decision-making problems without explicit programming.
Why learn Artificial Intelligence?
The current and future demand for AI engineers is staggering. The New York Times reports a candidate shortage for certified AI Engineers, with fewer than 10,000 qualified people in the world to fill these jobs, which according to Paysa earn an average salary of $172,000 per year in the U.S. (or Rs.17 lakhs to Rs. 25 lakhs in India) for engineers with the required skills.
You can gain in-depth knowledge of Artificial Intelligence by taking our Artificial Intelligence certification training course. Those who complete the course will be able to:
1. Master the concepts of supervised and unsupervised learning
2. Gain practical mastery over principles, algorithms, and applications of machine learning through a hands-on approach which includes working on 28 projects and one capstone project.
3. Acquire thorough knowledge of the mathematical and heuristic aspects of machine learning.
4. Understand the concepts and operation of support vector machines, kernel SVM, naive bayes, decision tree classifier, random forest classifier, logistic regression, K-nearest neighbors, K-means clustering and more.
5. Comprehend the theoretical concepts and how they relate to the practical aspects of machine learning.
6. Be able to model a wide variety of robust machine learning algorithms including deep learning, clustering, and recommendation systems
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