Aspiring data scientist and writer. BS in Communications. I hope to use my multiple talents and skillsets to teach others about the transformative power of computer programming and data science.
Ensemble/Voting Classification in Python with Scikit-Learn
Ensemble classification models can be powerful machine learning tools capable of achieving excellent performance and generalizing well to new, unseen datasets. The value of an ensemble classifier is that, in joining together the predictions of multiple classifiers, it can correct for errors made by any individual classifier, leading to better...
Dimensionality Reduction in Python with Scikit-Learn
In machine learning, the performance of a model only benefits from more features up until a certain point. The more features are fed into a model, the more the dimensionality of the data increases. As the dimensionality increases, overfitting becomes more likely. There are multiple techniques that can be used...
Analyzing API Data with MongoDB, Seaborn, and Matplotlib
A commonly requested skill for software development positions is experience with NoSQL databases, including MongoDB. This tutorial will explore collecting data using an API, storing it in a MongoDB database, and doing some analysis of the data. However, before jumping into the code let's take a moment to go over...
Image Classification with Transfer Learning and PyTorch
Transfer learning is a powerful technique for training deep neural networks that allows one to take knowledge learned about one deep learning problem and apply it to a different, yet similar learning problem. Using transfer learning can dramatically speed up the rate of deployment for an app you are designing,...
Gradient Boosting Classifiers in Python with Scikit-Learn
Gradient boosting classifiers are a group of machine learning algorithms that combine many weak learning models together to create a strong predictive model. Decision trees are usually used when doing gradient boosting. Gradient boosting models are becoming popular because of their effectiveness at classifying complex datasets, and have recently been...
Multiple Linear Regression with Python
Linear regression is one of the most commonly used algorithms in machine learning. You'll want to get familiar with linear regression because you'll need to use it if you're trying to measure the relationship between two or more continuous values. A deep dive into the theory and implementation of linear...
Image Recognition and Classification in Python with TensorFlow and Keras
TensorFlow is a well-established Deep Learning framework, and Keras is its official high-level API that simplifies the creation of models. Image recognition/classification is a common task, and thankfully, it's fairly straightforward and simple with Keras. In this guide, we'll take a look at how to classify/recognize images in...
Overview of Classification Methods in Python with Scikit-Learn
Are you a Python programmer looking to get into machine learning? An excellent place to start your journey is by getting acquainted with Scikit-Learn. Doing some classification with Scikit-Learn is a straightforward and simple way to start applying what you've learned, to make machine learning concepts concrete by implementing them...