ML & Human Learning

Machine Learning with Python

Learn to describe and implement various machine learning algorithms in Python, including classification and regression techniques, and evaluate their performance using appropriate metrics.

Machine Learning Basics

Learn fundamental machine learning principles, including K nearest neighbor, linear regression, and model analysis, with prerequisites of Python programming and basic mathematics.

Advanced ML & Signal Processing

Gain foundational knowledge of Linear Algebra and Machine Learning models, explore the scalability of SparkML and Scikit-Learn, and gain practical experience by adjusting models and analyzing vibration sensor data in a real-world IoT example.

ML with Apache Spark

Gain comprehensive knowledge of ML pipelines, model persistence, Spark applications, data engineering, and hands-on experience with Spark SQL and SparkML for regression, classification, and clustering.

Managing ML Projects with Google Cloud

This course explores enterprise machine learning applications, assesses the viability of ML use cases, and addresses the prerequisites, data characteristics, and critical factors for developing and managing ML models.

ML con Spark (MLlib) en Databricks

Learn to use Databricks and MLlib for creating and advancing machine learning models with Spark.

Detecting COVID-19 with Chest X-Ray using PyTorch

Acquire the ability to create custom Datasets and DataLoaders in PyTorch and train a ResNet-18 model for image classification.

Production ML Systems

Acquire the ability to differentiate between static and dynamic training and inference, manage model dependencies, establish distributed training for defect tolerance and replication, and generate exportable models.

ML in Healthcare: Fundamentals & Applications

Explore healthcare data mining methods, theoretical foundations of key techniques, selection criteria, and practical applications with emphasis on data cleansing, transformation, and modeling for real-world problem solving.