Past Workshops

Anomaly detection with machine and deep learning

Downtown / Midtown
Houston, TX

Anomaly detection(in R)

Join Pablo, our expert in building multivariate survival analysis, random forest, time series, and deep learning models to turn data into business insight

Today, it's an arms race between companies and fraudsters. As companies tighten against known fraud, the villains turn to new approaches. In finance, companies have turned to anomaly detection to stay ahead of the fraudsters.

Do you want to learn how to identify anomalies?  This hands-on, immersive course will teach you how to build and optimize several different approaches for anomaly detection.  Join Pablo, as he teaches you how to use one-class SVM, KNN, Isolation Forest, and autoencoders using neural networks -- all to detect anomalies in your data. You will learn how to handle incomplete data, impute observations, and understand the strengths and limitations of these orthogonal approaches. By the end of the course, you will have built and deployed your own unsupervised models for anomaly detection.

Cloud-Based Machine Learning

Downtown / Midtown
Houston, TX

Using cloud-based tools for machine learning (in Spark, R)

Join Mike, our expert in high-volume, cloud-based machine and deep learning using geospatial and financial data.

Until recently, cloud computing was out of reach for most businesses. Today, however, cloud computing resources only cost pennies or dollars an hour, providing opportunities for forward-thinking companies to gain value from their "big data"

Do you have large datasets or need quicker results?  This hands-on, immersive course will teach you how to use Spark to process data and quickly develop machine learning techniques on truly big data. Join Mike, as he teaches you how to access cloud resources to load, 'munge', and model data faster than you ever thought possible. This course will rely primarily on R and SparkR (but will be highly accessible to Python, R and SAS users). By the end of the course, you will have built effective regression, classification and unsupervised learning models and will have learned how to handle datasets that exceed your local memory. 

Deep Learning Using Convolutional Neural Networks

Downtown / Midtown
Houston, TX

Deep Learning with Convolutional Neural Networks (in Keras, python)

Join Ziad, our expert in cloud-based machine and deep learning operations

Today's highest performing machine learning algorithms often rely on deep neural networks.  These state-of-the-art algorithms solve today's problems, from image classification, to self driving cars, to classic game of Go, to optimizing data center power usage.  

Do you want to learn how to use deep learning algorithms?  Join us for a practical overview of neural network theory, tips on how to build and train them using the popular Keras framework for Tensor Flow, and to gain an understanding of the problems best suited for deep learning.  By the end of the course, you will have applied deep learning to supervised classification and regression problems as well as simple image analysis.

Survival Analysis Modeling

Downtown / Midtown
Houston, TX

Survival Analysis (in R)

Join Pablo, our expert in building multivariate survival analysis, random forest, and time series models to turn data into business insight

The medical industry has developed cutting-edge algorithms to predict survival. Today, data-driven companies use these methods to build the best predictions for time until an event - like equipment failure, illness, employee turnover, or bankruptcy.

Do you want to learn how to predict future events like failure or turnover?  This hands-on, immersive course will teach you how to build and optimize survival analysis models.  Join Pablo, as he teaches you how to make full use of your data. You will learn how to handle censored data, build survival curves, and model time-to-event using free R packages. By the end of the course, you will have built Kaplan-Meier survival curves, a Cox proportional hazards model, and be able to explain your results to a business decision maker.  

Forecasting Time Series

Downtown / Midtown
Houston, TX

Forecasting Time Series by Measuring Seasonality (in R)

Join Pablo, our expert in building multivariate survival analysis, random forest, and time series models to turn data into business insight

Data that is collected over time - like stock prices, sales volume, and sensor measurements - typically has strong seasonal trends. Time series techniques let companies identify these trends and build predictions to more effectively leverage internal resources. 

Do you want to learn how to measure seasonality? Or how to build effective forecasts?  This hands-on, immersive course will teach you how to build and optimize time series forecasting models that solve business needs. Join Pablo, as he teaches you how to build valid ARIMA and STL models for business forecasting using R. He will teach you the white noise test, model comparison techniques, as well as the strengths and limitations of predictive time series models. By the end of the course, you will have quantified seasonality, built a time series forecast, measured the efficacy, and learned how to communicate your results to a business decision maker.

Decision Trees and Random Forest

Downtown / Midtown
Houston, TX

Building and Understanding Decision Trees and Random Forest (in R)

Join Pablo, our expert in building multivariate survival analysis, random forest, and time series models to turn data into business insight

Random forest is a rapid machine learning algorithm that can develop predictions quickly and effectively. This powerful ensemble learner requires minimal data manipulation and returns variable importance, making it a strong first step for company data projects.

Have you ever wanted to use random forest but worried about using a tool you did not understand?  This hands-on, immersive course will teach you the ins and outs of the algorithm and its components:  decision trees. Join Pablo, as he teaches you how to deploy, optimize, and explain random forest algorithms.  You will learn the practical theory, the strengths, and the limitations of using this algorithm within your machine learning pipeline. By the end of the course, you will have built an effective supervised model, algorithmically optimized the parameters, and learned how to effectively explain and use the results of your random forest.  

Data science from zero to hero (python)

Downtown / Midtown
Houston, TX

Data science from zero to hero (in python)

Join Ziad, our expert in cloud-based machine and deep learning operations, and Neeraj, our expert in cloud infrastructure and introductory instruction

Today's highest performing machine learning algorithms often rely on deep neural networks.  These state-of-the-art algorithms solve today's problems, from image classification, to self driving cars, to classic game of Go, to optimizing data center power usage.  

Do you want to learn how to conduct true data science using code and state-of-the-art techniques? Join us for a hands-on course to take you from zero to hero in data science! This immersive course will teach you how to use Python, starting at zero with basic set-up. We will teach you the Python data science stack, traditional modeling, and cutting-edge techniques like decision trees and Random Forest. You will learn how to set up Python, load data sets, explore datasets graphically, fit sophisticated models, and deploy learning algorithms to the cloud. By the end of the course, you will be a Python hero who can not only load, transform, and model data but will also know how to explain your findings to a business or project leader.

Machine Learning Pipelines

Downtown / Midtown
Houston, TX

Building and deploying machine learning pipelines (in Python)

Join Ziad, our expert in anomaly detection, internet of things, and deploying machine learning pipelines to production.

The rapid growth of machine learning applications has created a demand for methods that can be used easily and without expert knowledge. Automating these methods and deploying them to production is a critical need for business across the country.

Do you want to learn how to conduct effective data science for business?  This hands-on, immersive course will teach you how to build and deploy a proper machine learning pipeline.  Join Ziad, as he teaches you how to deploy regression and classification systems.  He will teach you the best practices for feature selection, hyperparameter tuning, model selection, and machine learning pipeline algorithms.  By the end of the course, you will have built an effective unsupervised model for anomaly detection and will deploy the algorithm to production through a python web app.