10 Compelling Machine Learning Dissertations from Ph.D. Students
As a data scientist, an integral part of my work in the field revolves around keeping current with research coming out of academia. I frequently scour arXiv.org for late-breaking papers that show trends and fertile areas of research. Other sources of valuable research developments are in the form of... Read more
Using Auto-sklearn for More Efficient Model Training
Applying a machine learning algorithm to any number of data-related tasks can be an enormous time saver, but the variable factors associated with creating an algorithm can be daunting. One must consider a variety of design-related decisions, and the risks surrounding the creation of an accurate architecture can make... Read more
Strategies for Addressing Class Imbalance
Class imbalance is common in real-world datasets. For example, a dataset with examples of credit card fraud will often have exponentially more records of non-fraudulent activity than those of fraudulent cases. In many applications, training your model on imbalanced classes can inhibit model functionality if predictive accuracy for minority... Read more
Building Your First Bayesian Model in R
Bayesian models offer a method for making probabilistic predictions about the state of the world. Key advantages over a frequentist framework include the ability to incorporate prior information into the analysis, estimate missing values along with parameter values, and make statements about the probability of a certain hypothesis. The... Read more
The Best Machine Learning Research of 2019 So Far
The uses of machine learning are expanding rapidly. Already in 2019, significant research has been done in exploring new vistas for the use of this technology. Gathered below is a list of some of the most exciting research that has been undertaken in the realm of machine learning so... Read more
Watch: Challenges and Opportunities in Applying Machine Learning
There are many opportunities in applying machine learning, whether as an individual developer or in a business. But how do you get started? This talk provides an overview that separates fact from fiction and proposes processes to find opportunities for applying ML. This includes understanding where ML can have... Read more
Watch: Introduction to Machine Learning with Scikit-Learn
Machine learning has become an indispensable tool across many areas of research and commercial applications. From text-to-speech for your phone to detecting the Higgs-Boson particle, machine learning excels at extracting knowledge from large amounts of data. This talk gives a general introduction to machine learning and introduces practical tools... Read more
Watch: Deploying Investments in AI and Machine Learning
Over the next 18 months, companies will be completing the R&D phase of their AI/ML investments and will be deploying their models and algorithms to production. The proper execution of deploying your AI/ML models will separate the organizations who see an ROI on AI from those who don’t. This... Read more
Designing Better Recommendation Systems with Machine Learning
Recommendation systems are among the most familiar applications of machine learning and artificial intelligence. Not only are these systems valuable to consumers who may be looking for anything from new shows to watch or a better options for airfare, but they are also important to the producers and advertisers... Read more
The Complete Guide to Decision Trees (part 2)
(See part 1 here.) Now you may ask yourself: how do DTs know which features to select and how to split the data? To understand that, we need to get into some details. All DTs perform basically the same task: they examine all the attributes of the dataset to... Read more