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
- Unsupervised learning
- Predictive modeling
- ML landscape post deep learning revolution
- 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.
|Day One||Day Two|
Decision Tree -> Random Forest
· Dimension Reduction
· Unsupervised: K-means
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 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.
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.
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.
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.
|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
· The original IMDB
· Pre-trained embeddings
· Mini project - Amazon reviews
RNNs & LSTMs
· IMDB revisted
· Mini-project: Non-IMDB reviews
· 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
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. Summary of his works is available online.
Course Fee: TBD
Lindner College of Business
2906 Woodside Drive
Cincinnati, OH 45221
This training can also be delivered at your location