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.
How to Plot Inline and With Qt - Matplotlib with IPython/Jupyter Notebooks
There are a number of different data visualization libraries for Python. Out of all of the libraries, however, Matplotlib is easily the most popular and widely used one. With Matplotlib you can create both simple and complex visualizations. Jupyter notebooks are one of the most popular methods of sharing data...
Statistical Hypothesis Analysis in Python with ANOVAs, Chi-Square, and Pearson Correlation
Python is an incredibly versatile language, useful for a wide variety of tasks in a wide range of disciplines. One such discipline is statistical analysis on datasets, and along with SPSS, Python is one of the most common tools for statistics. Python’s user-friendly and intuitive nature makes running statistical...
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...
Text Generation with Python and TensorFlow/Keras
Are you interested in using a neural network to generate text? TensorFlow and Keras can be used for some amazing applications of natural language processing techniques, including the generation of text. In this tutorial, we'll cover the theory behind text generation using a Recurrent Neural Networks, specifically a Long Short-Term...
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...