Machine Learning is experiencing a golden age of investment, democratization, and accessibility across all sectors and industries encompassing the life sciences, healthcare, financial technology (fintech), consumer marketing, e-commerce, manufacturing, and more. But what exactly is Machine Learning? How is it connected to Artificial Intelligence (AI)? And most importantly, how can data scientists, programmers, software engineers, researchers, policy-makers, and entire organizations ethically undertake their endeavors in Machine Learning?

This presentation seeks to answer these questions, and more, by covering the subdivisions, approaches, and objectives underlying Machine Learning. Attendees are given a roadmap to help them navigate supervised, semi-supervised, and unsupervised ML approaches in addition to Deep Learning methodologies. Discussions about data bias, training bias, algorithm assumptions, and model generalization are all addressed to investigate the ethical implications of Machine Learning in both industry and research. By removing the “hype” surrounding Machine Learning approaches, this presentation seeks to empower decision-makers and stakeholders with a better understanding of the complexities, limitations, and potential surrounding ML applications in today’s era of rapidly growing Big Data.