Machine Learning

Machine learning (ML) algorithms, a subset of artificial intelligence, use statistics to find natural patterns in large amounts of data, and adapt automatically through experience, to develop insight and make better business decisions.  Deep learning is a specialized form of ML that uses techniques that gives machines an enhanced ability to find—and amplify—even the smallest patterns.

The following courses are offered. All courses can be customized and delivered at your location.

All courses are taught online by live Instructors

  • Fundamentals of Machine Learning (Python and R)
  • Machine Learning with R
  • Deep Learning with Keras and Tensorflow in R

"Online is always tougher, but having a live instructor vs. a course that is just a series of videos/tutorials makes a huge difference.“ (training course survey)

Fundamentals of Machine Learning

This workshop is designed for beginners with little experience in Machine Learning (ML). Most commonly used tools and methods in ML will be discussed during the workshop with an applied focus. Attendees could expand their knowledge in various topics from conventional techniques to newer modern methods in Artificial Intelligence and ML. Getting familiar with the fundamentals of ML, allows attendees to pick the right tool after they recognize the application domain, and apply the tools and methods they learned to real world problems. By understanding the basics, attendees also have the chance to learn further more about those topics in detail based on their individual interest.

Taking a hands-on approach using R and Python platforms, the following topics will be covered:

  • Data preprocessing
  • Supervised learning
  • Clustering
  • Unsupervised learning
  • Predictive modeling
  • ML landscape post deep learning revolution
Intended Audience
This course is intended for those who wish to learn about machine learning tasks as well as a guide to applying them. Readers should have knowledge of basic statistical concepts


  • Basic knowledge of linear algebra such as Matrix summation and multiplication is sufficient.
  • Familiarity with the concept of probability density function. Knowing Normal density function should be sufficient.
  • Basic understanding of computer programming. However, the workshop will be presented as though the attendees have no background in R and Python.
                                       Fundamentals of Machine Learning Course Outline
Day One Day Two  

Data Handling

Linear Regression

·       Regression

·       Classification

Decision Tree -> Random Forest

·       Regression

·       Classification

NN/Deep Learning

·       Overview

·       Essentials


·       Dimension Reduction

·       Unsupervised: K-means

Cross Validation

AI Adoption


4 online sessions

Online Course Fees: TBD  

These trainings can also be customized and delivered at your location.


Fundamentals of Machine Learning Instructors

Hamed Namavari, Data Scientist, Recovery Decision Science: Hamed Namavari has been with the Analytics Team at Recovery Decision Science (RDS) as Data Scientist since 2012. He recently acquired his PhD in Economics from the University of Cincinnati where he is also a graduate in MA in Applied Economics and MS in Business Analytics. His research interests are focused primarily on Bayesian inference and hypothesis testing in spatiotemporal econometrics. A few of his works have been published in Expert Systems with Applications.

Kaveh Bastani, Data Scientist, Recovery Decision Science Kaveh Bastani is a data scientist in Recovery Decision Science, OH. He received his PhD in Industrial and Systems Engineering from Virginia Tech in February 2016. His research interests primarily focus on consumer analytics using deep learning in Fintech industry. His research works include a wide range of applications ranging from consumer complaint analysis, consumer behavior prediction to consumer worthiness scoring. His research has appeared in high-quality journals including IISE Transactions, Decision Support Systems, Expert Systems with Applications, IEEE Transactions, and ASME Transactions.

Machine Learning Operations (ML OPS)

Learn how to free-up Data Science time for more model building and less model support.

This course is for individuals and teams looking to scale their Data Science functions while increasing the number and performance of models in production. MLOps is the best practices and standards to deploy, monitor, and improve A.I. models. Students learn an overview of MLOps and how to apply best practices for your team.

This Course Addresses:

  • Scaling Data Science - How do you shift data science teams to building more models?
  • Increasing R.O.I. - How do you deploy more models in production to increase R.O.I. and adoption?
  • Performance - How do teams quantify and improve model performance?
Learning Outcomes:
  • Understand and utilize the terminology, best practices, and critical concepts for deploying, monitoring, and governing A.I. models.
  • Automate the training, deployment, and monitoring of a model.
  • Measure and monitor the performance of A.I. models.
  • Govern A.I. models to increase R.O.I. and trust.
Learning Path:


Learn the terminology and concepts of enterprise MLOps, including process, roles, and best practices.

A.I. Architecture
Learn to design, architect, and deploy deep learning applications.

A.I. Pipelines
Build automated model pipelines for continuous training, integration, and deployment.

A.I. Monitoring
Measure, monitor, and improve model performance to maximize impact and R.O.I. throughout the model’s life cycle.  

This course does not require prerequisite knowledge.

  • 4 online sessions with live instructor
  • Time commitment 12-16 hours     
  • Certificate of Completion.

Online Course Fee: See registration page

This training can also be customized and delivered at your location.

Machine Learning with R

Learn the fundamentals and application of modern machine learning tasks. This course will cover unsupervised techniques to discover the hidden structure of datasets along with supervised techniques for predicting categorical and numeric responses via classification and regression.

Learn how to process data for modeling, how to train your models, how to visualize your models and assess their performance, and how to tune their parameters for better performance. The course emphasizes intuitive explanations of the techniques while focusing on problem-solving with real data across a wide variety of applications.  

Intended Audience

This course is intended for academics and data science practitioners who wish to learn about machine learning tasks as well as a guide to applying them. Readers should have knowledge of basic statistical ideas, such as correlation and linear regression analysis, along with intermediate R programming skills.

                                    Machine Learning with R Topic Outline
Topics Topics  
  • Unsupervised
  • Principal components analysis
  • Clustering
  • Supervised regression techniques
  • Model validation
  • Linear regression and its cousins
  • Nonlinear regression
  • Regression trees
  • Supervised classification techniques
  • Model validation
  • Linear classification models
  • Nonlinear classification models
  • Classification trees

Deep Learning with Keras and Tensorflow in R

This two-day workshop introduces the essential concepts of building deep learning models with TensorFlow and Keras via R. First, we’ll establish a mental model of where deep learning fits in the spectrum of machine learning, highlight its benefits and limitations, and discuss how the TensorFlow - Keras - R toolchain work together. We'll then build an understanding of deep learning through first principles and practical applications covering a variety of tasks such as computer vision, natural language processing, anomaly detection, and more. Throughout the workshop you will gain an intuitive understanding of the architectures and engines that make up deep learning models, apply a variety of deep learning algorithms (i.e. MLPs, CNNs, RNNs, LSTMs, autoencoders), understand when and how to tune the various hyperparameters, and be able to interpret model results. Leaving this workshop, you should have a firm grasp of deep learning and be able to implement a systematic approach for producing high quality modeling results.

Intended Audience

Is this workshop for you? If you answer "yes" to these three questions, then this workshop is likely a good fit:

  • Are you relatively new to the field of deep learning and neural networks but eager to learn? Or maybe you have applied a basic feedforward neural network but aren't familiar with the other deep learning frameworks?
  • Are you an experienced R user comfortable with the tidyverse, creating functions, and applying control (i.e. if, ifelse) and iteration (i.e. for, while) statements?
  • Are you familiar with the machine learning process such as data splitting, feature engineering, resampling procedures (i.e. k-fold cross validation), hyperparameter tuning, and model validation? This workshop will provide some review of these topics but coming in with some exposure will help you stay focused on the deep learning details rather than the general modeling procedure details.
                         Deep Learning with Keras and Tensorflow in R Course Outline
Day One Day Two  


Deep learning ingredients

Deep learning recipe

·       Training your model

·       Mini-project: Predicting Ames, IA home sales prices

Computer vision & CNNs

·       MNIST revisited

·       Cats vs. dogs

·       Transfer learning

Project: Classifying natural images

Word embeddings

·       The original IMDB

·       Pre-trained embeddings

·       Mini project - Amazon reviews

Collaborative filtering


·       IMDB revisted

·       Mini-project: Non-IMDB reviews

Wrap up

·       Project: Detecting Duplicate Quora

·       Final words of wisdom


Additional Topics if time permits

  • Improving generalization with k-fold cross validation
  • Performing a grid search
  • Linear regression with stochastic gradient descent
  • Diagnosing model performance with learning curves
  • Save your models for later with serialization
  • Visualizing what CNNs learn

Bradley Boehmke headshot

Machine Learning & Deep Learning with R Instructor

Brad Boehmke, PhD, is the Director of Data Science at 84.51°, a professor at three universities, author of Data Wrangling with R, and creator of multiple R open source packages and data science short courses. He focuses on developing algorithmic processes, solutions, and tools that enable 84.51° and its analysts to efficiently extract insights from data and provide solution alternatives to decision-makers. He has a wide analytic skill set covering descriptive, predictive, and prescriptive analytic capabilities applied across multiple domains including retail, healthcare, cyber intelligence, finance, the Department of Defense, and aerospace. A summary of his works is available online.

Course Fee: TBD

Room TBD
Lindner College of Business
2906 Woodside Drive
Cincinnati, OH 45221

This training can also be delivered at your location


For more information about these classes, or for custom training classes, please contact
Headshot of Larry Porter

Larry Porter

Training , Marketing and Sponsorship Manager