Automating Supervised Machine Learning Pipeline Development | Machine Learning | Community Webinar

Data Science Dojo · Beginner ·🧠 Large Language Models ·4y ago
Is there such a set of methodologies for data science and machine learning? YES! This talk will walk you through a high-level coverage of these methods and learn how to automate a supervised machine-learning pipeline. About the presenters: Thom Ives founded Integrated Machine Learning & AI, which is a very large group of data scientists who seek to grow and learn MORE TOGETHER. He is a leading data scientist and has developed a wide range of analytical models using multi-physics, data, and experiments. While Thom loves predictive modeling, his real passion for the data science space is making sure that data is clean from collection to storage for achieving the greatest overall return on data for all from retrieval. Thom is married and has 9 kids = 4 bios + 5 internationally adopted. He also has an awesome son-in-law that he is close to, AND he also regularly adopts amazing people from around the world! He lives in Eagle, Idaho, USA. Ghaith Sankari has an AI bachelor's degree from the University of Aleppo, and he is the founder of AI-HUT (Dubai, UAE). He is also a mentor in AI4Medicine Specialisation. Table of Contents: 0:00 – Introduction 2:50 – The highest level 9:26 – Supervised machine learning essence 12:21 – Features - Missing values 13:45 – Features - Data cleaning perception Vs reality 17:36 – Features - Encoding 22:26 – Features - Normalize 32:13 – Correlation - Methods 39:29 – Engineering features 50:02 – Human oversight 50:28 – Complete pipeline For further tutorials on the fundamentals of machine learning, check out this exclusive playlist: https://youtube.com/playlist?list=PL8eNk_zTBST-RTog7CPYvRfs1pYRWkPHG -- At Data Science Dojo, we believe data science is for everyone. Our data science trainings have been attended by more than 10,000 employees from over 2,500 companies globally, including many leaders in tech like Microsoft, Google, and Facebook. For more information please visit: https://hubs.la/Q01Z-13k0 💼 Learn to build LLM-powered apps
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Chapters (11)

Introduction
2:50 The highest level
9:26 Supervised machine learning essence
12:21 Features - Missing values
13:45 Features - Data cleaning perception Vs reality
17:36 Features - Encoding
22:26 Features - Normalize
32:13 Correlation - Methods
39:29 Engineering features
50:02 Human oversight
50:28 Complete pipeline
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