Imports, Data Cleansing, and EDA Cleaning and EDA are important for this challenge as this data set contains many ordinal / categorical features that may be important in categorization and will need to be converted to numerical values. As a baseline, I imported the following libraries to clean, explore and model … Read moreClosing the Sale: Predicting Home Prices via Linear Regression
Our faces and voices can be analyzed for emotion. As I mentioned, biomimicry, or imitating natural design in the things we create, is critical in recreating this human tendency in AI. Our end goal is artificial empathy, which (for now, at least) describes a machine’s ability to recognize and respond to … Read moreNobody UNDERSTANDS Me … But Soon, Artificial Intelligence Just Might
If you ever struggled with tuning Machine Learning (ML) models, you are reading the right piece. Hyper-parameter tuning refers to the problem of finding an optimal set of parameter values for a learning algorithm. Usually, the process of choosing these values is a time-consuming task. Even for simple algorithms like … Read moreAn introduction to high-dimensional hyper-parameter tuning
For aspiring data scientist it might sometimes be difficult to find their way through the forest of preprocessing techniques. Sklearn its preprocessing library forms a solid foundation to guide you through this important task in the data science pipeline. Although Sklearn a has pretty solid documentation, it often misses streamline … Read morePreprocessing with sklearn: a complete and comprehensive guide
Methodology Choose inputs and outputs. Collect and aggregate the data. Prepare the data. Explore and attempt to understand the data. Choose a Machine Learning Model. Measure the performance of the Model. Save the Model. Use the Model to make predictions. 1. Choosing Inputs and Outputs Inputs Choosing the right inputs and … Read moreIcoOmen: Using Machine Learning to Predict ICO Prices
An Application of Sequence Model to Mine Waze Open Data of Traffic Incidents, using Python and Keras. In this tutorial, I will show you how to use RNN deep learning model to find patterns from Waze Traffic Open Data of Incidents Report, and predict if severe traffic jams will happen shortly. … Read moreHow to Predict Severe Traffic Jams with Python and Recurrent Neural Networks?
Our Readers’ Guide We have received feedback that some of you find it difficult to efficiently navigate our Medium publication. So we have put together a few bullet points that will hopefully aid your experience on our blog. Subscribe to our publication to receive our Monthly Edition and Weekly Selection directly … Read moreHow to get the most out of Towards Data Science?
So… no pandas ?? There are some issues with pandas that the original author Wes McKinney outlines in his insightful blogpost: “Apache Arrow and the “10 Things I Hate About pandas”. Many of these issues will be tackled in the next version of pandas (pandas2?), building on top of Apache Arrow … Read moreVaex: Out of Core Dataframes for Python and Fast Visualization
Objective Companies nowadays use music classification, either to be able to place recommendations to their customers (such as Spotify, Soundcloud) or simply as a product (for example Shazam). Determining music genres is the first step in that direction. Machine Learning techniques have proved to be quite successful in extracting trends … Read moreMusic Genre Classification with Python
we are going to have a quick look at the architecture of four different state-of-the-art approaches by referring to the actual research paper and then we will move on to implement the one with the highest accuracy. Bidirectional LSTM-CRF: More details and implementation in keras. from the paper(Bidirectional LSTM-CRF Models … Read moreNamed Entity Recognition (NER), Meeting Industry’s Requirement by Applying state-of-the-art Deep…