Machine Learning Fundamentals, Building models and applying them to real data
Machine Learning Fundamentals, Building models and applying them to real data


In this session, we will give a fun, conceptual, and hands-on overview of Machine Learning. We will focus on two aspects: (1) The core fundamentals of machine learning, and (2) a hands on approach to applying it. We will focus on several algorithms, including Neural Networks, Support Vector Machines, Decision Trees, and Naive Bayes. Then we will study the testing framework in machine learning, the metrics to evaluate a model’s performance, and several techniques to improve these models. We will show how to develop these techniques in Python, more specifically, Pandas and Scikit-learn. At then end, you'll have the chance to apply the knowledge you've learned on two possible projects: One that builds a spam detector using the Naive Bayes classifier, and another one that analyzes census data using several different machine learning algorithms


Arpan likes to find computing solutions to everyday problems. He obtained his PhD from North Carolina State University, focusing on biologically-inspired computer vision, and applying it to research areas ranging from robotics to cognitive science. ​At Udacity, he works with partners from both academia and industry to build practical artificial intelligence and machine learning courses. Arpan enjoys exploring the outdoors through hiking and backpacking.

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