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1) Give a broad overview of Machine Learning, Artificial Intelligence, and Data Science
2) Provide hands-on exposure to solving different machine learning problems, including classification, regression, computer vision, natural language processing.
3) Introduce popular machine learning tools and frameworks like Tensorflow.
Ideal participant profile: Min 2 years experienced programmers. Engineers/technology professionals good at programming.
In the past ten years, there has been great progress in the area of machine learning and artificial intelligence. Machine Learning, which was restricted to academia and only a few industries, is now finding applications in almost all aspects and industries of the business world and human life. Machine Learning algorithms can now translate one human language to others. They can detect cancer. They can identify and track objects in a video. They can predict fraud before it happens. They can recognize speech and generate translated speech in another language. They can predict machine failure in factories. They can solve question papers.
How is that one technology can do so many things? The answer is in their ability to learn from data. Just like humans do. This ability to learn from data gives them an enormous advantage over traditional programs. Machine learning algorithms can handle imperfect and noisy data and still perform well. Their real power is the ability to extract their own logic from the data and apply it to solve the problem.
The course starts with an introduction to the world of Artificial intelligence, machine learning, and data science. Explores different breakthroughs and discusses some state of the art developments. This workshop will include a practical introduction to classification and regression algorithms with tools like Scikit-learn and TensorFlow. It will also discuss computer vision problems, with convolutional neural networks (CNN), and natural language processing problems with recurrent neural networks (RNN).
The importance is given for a mathematical understanding of the concepts and algorithms along with hands-on exposure to practical problems.
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