Articles About Machine Learning

How to Develop Elastic Net Regression Models in Python

Regression is a modeling task that involves predicting a numeric value given an input. Linear regression is the standard algorithm for regression that assumes a linear relationship between inputs and the target variable. An extension to linear regression involves adding penalties to the loss function during training that encourage simpler models that have smaller coefficient values. These extensions are referred to as regularized linear regression or penalized linear regression. Elastic net is a popular type of regularized linear regression that […]

October 13, 2020 Machine Learning

How to Develop Ridge Regression Models in Python

Last Updated on October 11, 2020 Regression is a modeling task that involves predicting a numeric value given an input. Linear regression is the standard algorithm for regression that assumes a linear relationship between inputs and the target variable. An extension to linear regression invokes adding penalties to the loss function during training that encourages simpler models that have smaller coefficient values. These extensions are referred to as regularized linear regression or penalized linear regression. Ridge Regression is a popular […]

October 13, 2020 Machine Learning

How to Develop LASSO Regression Models in Python

Regression is a modeling task that involves predicting a numeric value given an input. Linear regression is the standard algorithm for regression that assumes a linear relationship between inputs and the target variable. An extension to linear regression invokes adding penalties to the loss function during training that encourages simpler models that have smaller coefficient values. These extensions are referred to as regularized linear regression or penalized linear regression. Lasso Regression is a popular type of regularized linear regression that […]

October 12, 2020 Machine Learning

Flexible neural models outperform grammar- and automaton-based counterparts on a variety of sequence modeling tasks. However, neural models perform poorly in settings requiring compositional generalization beyond the training data — particularly to rare or unseen subsequences… Past work has found symbolic scaffolding (e.g. grammars or automata) essential in these settings. Here we present a family of learned data augmentation schemes that support a large category of compositional generalizations without appeal to latent symbolic structure. Our approach to data augmentation has […]

October 11, 2020 Analytics Vidhya, Beginner, Deep Learning, Listicle, Machine Learning, NLP, Winners Approach Leave a comment

The Winning Approaches from codeFest 2018 – NLP, Computer Vision and Machine Learning!

Introduction Analytics Vidhya’s hackathons are one of the best ways to evaluate how far you’ve traveled in your data science journey. And what better way than to put your skills to the test against the top data scientists from around the globe? Participating in these hackathons also helps you understand where you need to improve and what else you can learn to get a better score in the next competition. And a very popular demand after each hackathon is to […]

October 11, 2020 Classification, Machine Learning, NLP, Project, Python, Supervised, Technique, Telecom, Text, Unstructured Data Leave a comment

Predicting Movie Genres using NLP – An Awesome Introduction to Multi-Label Classification

Introduction I was intrigued going through this amazing article on building a multi-label image classification model last week. The data scientist in me started exploring possibilities of transforming this idea into a Natural Language Processing (NLP) problem. That article showcases computer vision techniques to predict a movie’s genre. So I had to find a way to convert that problem statement into text-based data. Now, most NLP tutorials look at solving single-label classification challenges (when there’s only one label per observation). […]

October 11, 2020 Computer Vision, Data Science, Intermediate, Listicle, Machine Learning, Maths, NLP, Python, Use Cases Leave a comment

10 Powerful Applications of Linear Algebra in Data Science (with Multiple Resources)

Overview Linear algebra powers various and diverse data science algorithms and applications Here, we present 10 such applications where linear algebra will help you become a better data scientist We have categorized these applications into various fields – Basic Machine Learning, Dimensionality Reduction, Natural Language Processing, and Computer Vision Introduction If Data Science was Batman, Linear Algebra would be Robin. This faithful sidekick is often ignored. But in reality, it powers major areas of Data Science including the hot […]

October 11, 2020 Beginner, Machine Learning, NLP, Python, Technique Leave a comment

Machine Learning in Cyber Security — Malicious Software Installation

Introduction Monitoring of user activities performed by local administrators is always a challenge for SOC analysts and security professionals. Most of the security framework will recommend the implementation of a whitelist mechanism. However, the real world is often not ideal. You will always have different developers or users having local administrator rights to bypass controls specified. Is there a way to monitor the local administrator activities?

October 9, 2020 Machine Learning

Combined Algorithm Selection and Hyperparameter Optimization (CASH Optimization)

Machine learning model selection and configuration may be the biggest challenge in applied machine learning. Controlled experiments must be performed in order to discover what works best for a given classification or regression predictive modeling task. This can feel overwhelming given the large number of data preparation schemes, learning algorithms, and model hyperparameters that could be considered. The common approach is to use a shortcut, such as using a popular algorithm or testing a small number of algorithms with default […]

October 7, 2020 Machine Learning

Hyperparameter Optimization With Random Search and Grid Search

Last Updated on September 19, 2020 Machine learning models have hyperparameters that you must set in order to customize the model to your dataset. Often the general effects of hyperparameters on a model are known, but how to best set a hyperparameter and combinations of interacting hyperparameters for a given dataset is challenging. There are often general heuristics or rules of thumb for configuring hyperparameters. A better approach is to objectively search different values for model hyperparameters and choose a […]

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