Useful Tools for Profiling Machine Learning Tasks — Profiling is a software engineering task in which software bottlenecks are analyzed programmatically. This process includes analyzing memory usage, the number of function calls and the runtime of those calls. Such analysis is important because it provides a rigorous way to detect parts of a software program that may be…

A guide to outlier detection methods with examples in Python — Outlier detection, which is the process of identifying extreme values in data, has many applications across a wide variety of industries including finance, insurance, cybersecurity and healthcare. In finance, for example, it can detect malicious events like credit card fraud. In insurance, it can identify forged or fabricated documents. In…

Introduction to Pandas Methods in Python — Pandas is a popular python library used for statistical analysis and data transformations. It allows you to easily perform tasks such as reading in data, displaying data, selecting columns, filtering data and much more. In this post we will walk through some of the basic methods for displaying and manipulating…

Methods for Optimizing Machine Learning Model Outputs — Mathematical optimization is the process of finding the best set of inputs that maximizes (or minimizes) the output of a function. In the field of optimization, the function being optimized is called the objective function. A wide range of out-of-the-box tools exist for solving optimization problems that only work with…

Loss Functions for Multi-class and Multi-label Classification — Deep learning models are a mathematical representation of the network of neurons in the human brain. These models have a wide range of applications in healthcare, robotics, streaming services and much more. For example, deep learning can solve problems in healthcare like predicting patient readmission. Further, Tesla’s self-driving cars employ…

Using Regularization to Prevent Overfitting in Deep Learning Models — Overfitting is a common problem data scientists face when building models with high complexity. It occurs when a model fits very well to the training data then subsequently performs poorly when tested on new data. This issue most often arises when building deep neural network models, which is a statistical…

Using Random Forests and PCA for Dimensionality Reduction — Dimensionality reduction is the process of transforming high-dimensional data into a lower dimensional format while preserving the most important properties. This technique has applications in many industries including quantitative finance, healthcare, and drug discovery. …

Three Time Series Forecasting Methods in Python — Time series forecasting is the task of predicting future values based on historical data. Examples across industries include forecasting of weather, sales numbers and stock prices. More recently, it has been applied to predicting price trends for cryptocurrencies such as Bitcoin and Ethereum. …

Three Methods for Portfolio Optimization in Python — In investing, portfolio optimization is the task of selecting assets such that the return on investment is maximized while the risk is minimized. For example, an investor may be interested in selecting five stocks from a list of 20 to ensure they make the most money possible. Portfolio optimization methods…