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👋 Hi, I am Mario Filho

I'm a self-taught machine learning geek who's turned a fascination with computer science into a pretty cool career.

My story kicked off with Andrew Ng's course, which got me hooked on machine learning.

Sure, I didn't know what a derivative was at first, and math and I weren't exactly best friends, but I dove headfirst into the challenge anyway.

Without a traditional background, I knew I needed to prove myself.

So, I decided to jump into the competitive world of Kaggle.

It was tough, but I loved every minute of it.

I ended up winning a few competitions, becoming a Kaggle Competitions Grandmaster, and even made it to the 12th spot in the global rankings out of over 180,000 data scientists.

This success opened up some amazing opportunities.

I landed the role of Lead Data Scientist at Upwork Inc., where I got to develop a pretty neat reranking system that improved how we qualified freelancers on the platform.

As a freelancer myself, I've worked on some exciting projects, like creating machine learning models for startups and Fortune 500 companies.

One of my favorites was developing an artwork pricing model for an art analysis startup.

But it's not all about work for me. I love sharing what I've learned with others.

I've been a content advisor at UC Berkeley's Data-X, authored a popular book called "Manual Prático de Data Science," and even created an online course where I taught more than 900 students how to build machine learning pipelines with Python.

On my YouTube channel I've been sharing free lessons on data science and machine learning in Portuguese, and we've built a community of over 23,000 subscribers.

This blog is my latest project in this educational journey.

I believe my unconventional learning journey has its perks. It's made me more open-minded and willing to experiment, always on the lookout for what works best in practice.

This approach has served me well so far and I'm excited to see where it'll take me next.

TL;DR

Mario Filho's Projects

clippy-adagrad icon clippy-adagrad

PyTorch Implementation of Improving Training Stability for Multitask Ranking Models in Recommender Systems

ec2spotprices icon ec2spotprices

Uses boto to retrieve current spot instance prices on Amazon EC2.

gpt-summarizer icon gpt-summarizer

A scrappy Jupyter notebook to summarize long podcasts, youtube videos, etc

kaggle_crowdflower icon kaggle_crowdflower

1st Place Solution for Search Results Relevance Competition on Kaggle (https://www.kaggle.com/c/crowdflower-search-relevance)

learningcandlesticks icon learningcandlesticks

http://mariofilho.com/can-machine-learning-model-predict-the-sp500-by-looking-at-candlesticks/

machine-learning-success icon machine-learning-success

How did you successfully apply machine learning in a company? Here we share the impact that deployed machine learning systems made on business related metrics.

meli2020 icon meli2020

9th (public) place solution to MeLi Data Challenge 2020

nolearn icon nolearn

scikit-learn compatible wrappers for neural net libraries, and other utilities.

numerapi icon numerapi

Python API and command line interface for the numer.ai machine learning competition

onlinesvmpegasos icon onlinesvmpegasos

Code and dataset for the article on implementation of Online SVM using Pegasos

robyn icon robyn

Robyn is an experimental, automated and open-sourced Marketing Mix Modeling (MMM) package from Facebook Marketing Science. It uses various machine learning techniques (Ridge regression, multi-objective evolutionary algorithm for hyperparameter optimisation, gradient-based optimisation for budget allocation etc.) to define media channel efficiency a

swing icon swing

Implementation of the Swing Algorithm for Substitute Product Recommendation in Python

tspbrasil icon tspbrasil

Material sobre o artigo "Usando Otimização Para Aproximar a Menor Rota Entre Mais de 5.500 Municípios Brasileiros" publicado no site MarioFilho.com

tutorialensemble icon tutorialensemble

Arquivos para o tutorial do artigo Tutorial: Aumentando o Poder Preditivo de Seus Modelos de Machine Learning com Stacking Ensembles

tw-bert icon tw-bert

Implementation of End-to-End Query Term Weighting (TW-BERT)

unified-embeddings icon unified-embeddings

Implementation of Unified Embedding: Battle-Tested Feature Representations for Web-Scale ML Systems

xgboost icon xgboost

Scalable, Portable and Distributed Gradient Boosting (GBDT, GBRT or GBM) Library, for Python, R, Java, Scala, C++ and more. Runs on single machine, Hadoop, Spark, Flink and DataFlow

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