Surviving the Impending AI/ML/DS Arms Race
Overview: In this video you will learn how bring the benefits of machine learning and artificial intelligence to your business. Jeremy explains in 5 simple steps the route to become more AI/ML driven in your company. Learning outcomes: You will learn the difference between Artificial Intelligence, Deep Learning, Machine... Read more
Last batch of notebooks for Think Stats
Getting ready to teach Data Science in the spring, I am going back through Think Stats and updating the Jupyter notebooks.  Each chapter has a notebook that shows the examples from the book along with some small exercises, with more substantial exercises at the end. If you are reading the book, you... Read more
Tutorial: Visualizing Machine Learning Models
One of the big issues I’ve encountered in my teaching is explaining how to evaluate the performance of machine learning models.  Simply put, it is relatively trivial to generate the various performance metrics–accuracy, precision, recall, etc–if you wanted to visualize any of these metrics, there wasn’t really an easy... Read more
Machine Learning: An In-Depth Guide – Model Performance and Error Analysis
Articles Overview, goals, learning types, and algorithms Data selection, preparation, and modeling Model evaluation, validation, complexity, and improvement Model performance and error analysis Unsupervised learning, related fields, and machine learning in practice Introduction Welcome to the fourth article in a five-part series about machine learning. In this article, we... Read more
The AI Revolution for Business
Overview: In this video you will be introduced to many artificial intelligence applications in use today. You will understand why is important to use AI in your business. Learning outcomes: You will know several applications of artificial intelligence. You will know the similarities between the scientific method and data... Read more
Using GRAKN.AI to detect patterns in credit fraud data
The worlds of first order logic and machine learning don’t usually collide. But with increasing sizes of datasets around the web and, more importantly, complex relationships that need to be represented, analysts need ways of applying machine learning techniques to discover patterns in their datasets. Sitting right in the middle of... Read more
Recently, I have published an article on Journal of Chemical Physics, entitled Tree based machine learning framework for predicting ground state energies of molecules (link to article and preprint). The article discusses in detail, the application of machine learning algorithms to predict ground state energies of molecules. Current standard of computationally efficient electronic structure... Read more
WHO Tuberculosis Data & ggplot2
So it has been a while since my previous post on some data taken from the UNHCR database. This post we’ll bring it back to the topic of infectious diseases (check out my other posts on the SIR model and MRSA typing). For this post, as similar to previous ones, I give a guide through... Read more
Building Random Forest Classifier with Python scikit-learn
In the Introductory article about random forest algorithm, we addressed how the random forest algorithm works with real life examples. As continues to that, In this article we are going to build the random forest algorithm in python with the help of one of the best Python machine learning libraryScikit-Learn. To build the random... Read more
Deep Learning for NLP Best Practices
Table of contents: Introduction Best practices Word embeddings Depth Layer connections Dropout Multi-task learning Attention Optimization Ensembling Hyperparameter optimization LSTM tricks Task-specific best practices Classification Sequence labelling Natural language generation Neural machine translation Introduction This post is a collection of best practices for using neural networks in Natural Language... Read more