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Introduction to Machine Learning

"Introduction to Machine Learning" provides a foundational overview of the field, exploring the principles and applications of machine learning. This theme introduces participants to the core concepts, algorithms, and techniques used in machine learning, emphasizing its role in enabling computers to learn from data and make informed predictions or decisions. Topics include supervised and unsupervised learning, classification, regression, clustering, and the evaluation of machine learning models. The theme also touches on real-world applications, ethical considerations, and the evolving landscape of machine learning.

Webinar
27 June 2020
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Introduction to Machine Learning
Introduction to Machine Learning

"Introduction to Machine Learning" offers a comprehensive entry point into the dynamic realm of machine learning. Participants will gain a solid understanding of the fundamental concepts driving this field, including how algorithms learn patterns from data to make predictions or decisions. The theme covers key topics such as supervised learning for labeled data, unsupervised learning for unlabeled data, classification methods, regression analysis, clustering techniques, and the essential process of evaluating the performance of machine learning models. Additionally, the theme explores real-world applications, ethical considerations surrounding machine learning, and the ongoing advancements shaping the landscape of this rapidly evolving field. This introduction serves as a stepping stone for those looking to delve into the exciting and impactful world of machine learning.

Theme: Introduction to Machine Learning
Schedule: Sunday, 28 Juni 2020, 10:00 WIB
Registration: bit.ly/nusantechgaluh

Speaker:
Galuh Sahid - Data Scientist at GoJek and Being the First Google Developer Expert in Machine Learning from Indonesia

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"Becoming a Machine Learning Engineer" is a theme that explores the pathway to establishing a career in the field of machine learning. This theme covers the foundational knowledge, skills, and steps required to become proficient in machine learning, including programming languages, statistical concepts, and algorithmic understanding. It delves into practical applications of machine learning across various industries, highlighting the significance of continuous learning and staying abreast of technological advancements in this rapidly evolving field. Participants will gain insights into the role of a machine learning engineer, the challenges they may encounter, and the rewarding aspects of contributing to cutting-edge technology and data-driven solutions.

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