Words Embedding using GloVe Vectors

NLP Tutorial – GloVe Vectors Embedding with TF2.0 and Keras GloVe stands for global vectors for word representation. It is an unsupervised learning algorithm developed by Stanford for generating word embeddings by aggregating a global word-word co-occurrence matrix from a corpus. The resulting embeddings show interesting linear substructures of the word in Read more…

Multi-step-Time-series-predicting using RNN LSTM

Household Power Consumption Prediction using RNN-LSTM Power outage accidents will cause huge economic loss to the social economy. Therefore, it is very important to predict power consumption. Given the rise of smart electricity meters and the wide adoption of electricity generation technology like solar panels, there is a wealth of Read more…

Sentiment Analysis Using Scikit-learn

Sentiment Analysis Objective In this notebook we are going to perform a binary classification i.e. we will classify the sentiment as positive or negative according to the `Reviews’ column data of the IMDB dataset.  We will use TFIDF for text data vectorization and Linear Support Vector Machine for classification. Natural Read more…

Feature Selection Based on Univariate ROC_AUC for Classification and MSE for Regression | Machine Learning | KGP talkie

Feature Selection Based on Univariate ROC_AUC for Classification and MSE for Regression Watch Full Playlist: https://www.youtube.com/playlist?list=PLc2rvfiptPSQYzmDIFuq2PqN2n28ZjxDH What is ROC_AUC The Receiver Operator Characteristic (ROC) curve is well-known in evaluating classification performance. Owing to its superiority in dealing with imbalanced and cost-sensitive data, the ROC curve has been exploited as a Read more…

Feature Selection using Fisher Score and Chi2 (χ2) Test | Titanic Dataset | Machine Learning | KGP Talkie

Feature Selection using Fisher Score and Chi2 (χ2) Test Watch Full Playlist: https://www.youtube.com/playlist?list=PLc2rvfiptPSQYzmDIFuq2PqN2n28ZjxDH What is Fisher Score and Chi2 ( χ2) Test Fisher score is one of the most widely used supervised feature selection methods. However, it selects each feature independently according to their scores under the Fisher criterion, which Read more…

Feature Selection Based on Univariate (ANOVA) Test for Classification | Machine Learning | KGP Talkie

Feature Selection Based on Univariate (ANOVA) Test for Classification Watch Full Playlist: https://www.youtube.com/playlist?list=PLc2rvfiptPSQYzmDIFuq2PqN2n28ZjxDH What is Univariate (ANOVA) Test The elimination process aims to reduce the size of the input feature set and at the same time to retain the class discriminatory information for classification problems. An F-test is any statistical Read more…

Feature Selection Based on Mutual Information (Entropy) Gain for Classification and Regression | Machine Learning | KGP Talkie

Feature Selection Based on Mutual Information (Entropy) Gain Watch Full Playlist: https://www.youtube.com/playlist?list=PLc2rvfiptPSQYzmDIFuq2PqN2n28ZjxDH What is Mutual Information The elimination process aims to reduce the size of the input feature set and at the same time to retain the class discriminatory information for classification problems. Mutual information (MI) is a measure of Read more…

Feature Selection with Filtering Method | Constant, Quasi Constant and Duplicate Feature Removal

Filtering method Watch Full Playlist: https://www.youtube.com/playlist?list=PLc2rvfiptPSQYzmDIFuq2PqN2n28ZjxDH Unnecessary and redundant features not only slow down the training time of an algorithm, but they also affect the performance of the algorithm. There are several advantages of performing feature selection before training machine learning models: Models with less number of features have higher Read more…

Feature Dimention Reduction Using LDA and PCA with Python | Principal Component Analysis in Feature Selection | KGP Talkie

Feature Dimension Reduction Watch Full Playlist: https://www.youtube.com/playlist?list=PLc2rvfiptPSQYzmDIFuq2PqN2n28ZjxDH What is LDA (Linear Discriminant Analysis)? The idea behind LDA is simple. Mathematically speaking, we need to find a new feature space to project the data in order to maximize classes separability Linear Discriminant Analysis is a supervised algorithm as it takes the Read more…

Use of Linear and Logistic Regression Coefficients with Lasso (L1) and Ridge (L2) Regularization for Feature Selection in Machine Learning

Watch Full Playlist: https://www.youtube.com/playlist?list=PLc2rvfiptPSQYzmDIFuq2PqN2n28ZjxDH Linear Regression Let’s first understand what exactly linear regression is, it is a straight forward approach to predict the response y on the basis of different prediction variables such x and ε. . There is a linear relation between x and y. 𝑦𝑖 = 𝛽0 + Read more…

Lasso and Ridge Regularisation for Feature Selection in Classification | Embedded Method | KGP Talkie

What is Regularisation? Regularization adds a penalty on the different parameters of the model to reduce the freedom of the model. Hence, the model will be less likely to fit the noise of the training data and will improve the generalization abilities of the model. There are basically 3-types of Read more…

PCA with Python | Principal Component Analysis Machine Learning | KGP Talkie

Principal Component Analysis(PCA) According to Wikipedia, PCA is a statistical procedure that uses an orthogonal transformation to convert a set of observations of possibly correlated variables (entities each of which takes on various numerical values) into a set of values of linearly uncorrelated variables called principal components. Principal components These Read more…