alejandro-robles7

plug-and-play-pipeline

AIScratch

Scratch scripts

alejandro-robles7

Config files for my GitHub profile.

alejandro-robles7.github.io

:triangular_ruler: Jekyll theme for personal sites, blogs, and portfolios. Two-columns and extremely flexible.

AWS-Deployment-Docker_ECR

bigdata-pyspark

Big Data with PySpark track

CatsAndDogs

CreditCardApprovals

Predicting Credit Card Approvals

DecisionTree

implement decision tree

discrete-event-simulation

alejandro-robles7/discrete-event-simulation

alejandro-robles7/plug-and-play-pipeline

alejandro-robles7/AWS-Deployment-Docker_ECR

alejandro-robles7/dockerize-bert

ML Classification Webapp Deployment

alejandro-robles7/fbai-mlops-assignments

Code repository for FourthBrain's MLOps and Systems Program Assignments

alejandro-robles7/Sentiment-Analysis-ML-Flask-App

A machine learning end to end flask web app for sentiment analysis model created using Scikit-learn & VADER Sentiment.

alejandro-robles7/MLOps-Face-Detection

alejandro-robles7/training-data-analyst

Labs and demos for courses for GCP Training (http://cloud.google.com/training).

alejandro-robles7/unit-testing

alejandro-robles7/alejandro-robles7

Config files for my GitHub profile.

alejandro-robles7/smlbook

Serverless Machine Learning in Action

alejandro-robles7/Writing-Functions-in-Python

You've done your analysis, built your report, and trained a model. What's next? Well, if you want to deploy your model into production, your code will need to be more reliable than exploratory scripts in a Jupyter notebook. Writing Functions in Python will give you a strong foundation in writing complex and beautiful functions so that you can contribute research and engineering skills to your team. You'll learn useful tricks, like how to write context managers and decorators. You'll also learn best practices around how to write maintainable reusable functions with good documentation. They say that people who can do good research and write high-quality code are unicorns. Take this course and discover the magic! 1

alejandro-robles7/Introduction-to-Airflow-in-Python

Are you responsible for delivering data on a schedule? You may have written scripts, added complex cron tasks, and tried various ways to meet an ever-changing set of requirements. It's even trickier to work with your teammates on managing multiple sets of requirements. Airflow can do this while adding scheduling, error handling, and reporting. Introduction to Airflow in Python will guide you in the basic concepts of Airflow and help you implement data engineering workflows in production. You'll implement many different data engineering tasks in a sane, repeatable fashion, while losing as little sanity as possible. 1

alejandro-robles7/Fraud-Detection-in-Python

A typical organization loses an estimated 5% of its yearly revenue to fraud. In this course, you will learn how to fight fraud by using data. For example, you'll learn how to apply supervised learning algorithms to detect fraudulent behavior similar to past ones, as well as unsupervised learning methods to discover new types of fraud activities. Moreover, in fraud analytics you often deal with highly imbalanced datasets when classifying fraud versus non-fraud, and during this course you will pick up some techniques on how to deal with that. The course provides a mix of technical and theoretical insights and shows you hands-on how to practically implement fraud detection models. In addition, you will get tips and advice from real-life experience to help you prevent making common mistakes in fraud analytics.

alejandro-robles7/Feature-Engineering-for-Machine-Learning-in-Python

Every day you read about the amazing breakthroughs in how the newest applications of machine learning are changing the world. Often this reporting glosses over the fact that a huge amount of data munging and feature engineering must be done before any of these fancy models can be used. In this course, you will learn how to do just that. You will work with Stack Overflow Developers survey, and historic US presidential inauguration addresses, to understand how best to preprocess and engineer features from categorical, continuous, and unstructured data. This course will give you hands-on experience on how to prepare any data for your own machine learning models. 1

alejandro-robles7/bigdata-pyspark

Big Data with PySpark track

alejandro-robles7/statistical-simulation

Statistical Simulation in Python

alejandro-robles7/predicting-ctr

alejandro-robles7/unit-testing-for-data-science-in-python

Example data science project used in Datacamp's Unit Testing for Data Science in Python course

alejandro-robles7/msthesis

thesis

alejandro-robles7/Thesis---Alejandro-Robles

Latex Thesis

alejandro-robles7/Thesis

code for thesis

alejandro-robles7/ml-workflows

alejandro-robles7/CreditCardApprovals

Predicting Credit Card Approvals

alejandro-robles7/Matlab

alejandro-robles7/CatsAndDogs

alejandro-robles7/DogsCats456

Create an algorithm to distinguish dogs from cats

alejandro-robles7/KerasTraining

Train NN and CNN using Keras

alejandro-robles7/DecisionTree

implement decision tree

alejandro-robles7/AIScratch

Scratch scripts