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The Story of How Nikolaos Got a Data Science Job in Just 3 Months

March 5, 2023

Miguel González-Fierro

As a mentor of Nikolaos, my primary goal was to guide him toward securing a job in the field of data science. We developed a plan of action that involved improving his resume, LinkedIn, and GitHub profiles, theoretical and practical knowledge, and training on how to approach an interview. Nikolaos was able to get a Data Science position in less than 3 months. In this post, I share his story.

Machine Learning Mastery for Career Growth at Top-Tech Corporations

Feb. 14, 2023

Aishwarya Naresh Reganti and Miguel González-Fierro

In this interview hosted by The LevelUp Org I provide my perspective on having an exciting career if you are interested in machine learning. Many of the ideas come from what I have observed in academia, the startup world, and big tech. I hope this is useful to people starting their careers and looking for direction.

Start with Transformers

Feb. 2, 2023

Neil Leiser and Miguel González-Fierro

In this conversation with Neil Leiser, we first dig into my beginning in robotics and the connection between robotics and AI. Then I share a few stories on how to learn AI faster, how to switch fields, taking initiatives, the importance of diversity, and the power of practical knowledge. We finally talk about Data Science at Microsoft where we talk about my career path and the learnings I got from some of the best in the field.

My Most Popular LinkedIn Posts Of 2022

Jan. 2, 2023

Miguel González-Fierro

In 2022 around 11 million people saw my posts on LinkedIn. I've been posting to help people understand and apply AI. In particular, I focused on how people can switch their careers to AI because that's the journey I followed. Here are some of the most popular posts.

Building Recommender Systems

Nov. 26, 2022

Seth Juarez and Miguel González-Fierro

Recommendation systems are one of the most exciting AI solutions available today. They are information filters that learn users' behavior based on their historical interactions with items, and then predict their preferences for a given item. In this post, we make an overview of Recommenders, the open-source repository that helps Data Scientists build recommendation systems.