24th September , 2018
Machine Learning is a first-class ticket to the most exciting careers in data analysis today. As data sources proliferate along with the computing power to process them, going straight to the data is one of the most straightforward ways to quickly gain insights and make predictions.
Machine learning brings together computer science and statistics to harness that predictive power. It’s a must-have skill for all aspiring data analysts and data scientists or anyone else who wants to wrestle all that raw data into refined trends and predictions.
Machine Learning – Learn & Develop New Skills | Terralogic
This is a class that will teach you the end-to-end process of investigating data through a machine learning lens. It will teach you how to extract and identify useful features that best represent your data, a few of the most important machine learning algorithms, and how to evaluate the performance of your machine learning algorithms.
AI agents with their core ML aim at interacting with humans in a variety of ways, including providing estimates on phenomena, making recommendations for decisions, and being instructed and corrected.
In our Machine Learning Department, we study and research the theoretical foundations of the field of machine learning, as well as on the contributions to the general intelligence goal of the field of artificial intelligence. In addition to their theoretical education, all our students, advised by faculty, get hands-on experience with complex real datasets.
Machine Learning can impact many applications relying on all sorts of data, basically any data that is recorded in computers, such as health data, scientific data, financial data, location data, weather data, energy data, etc. As our society increasingly relies on digital data, machine learning is crucial for most of our current and future applications.
Machine learning techniques to intelligently handle large and complex amounts of information build upon foundations in many disciplines, including statistics, knowledge representation, planning and control, databases, Casual inference, computer systems, machine vision, and natural language processing.