You signed in with another tab or window. Reload to refresh your session.You signed out in another tab or window. Reload to refresh your session.You switched accounts on another tab or window. Reload to refresh your session.Dismiss alert
This repository is for my students of Udemy. You can find all lecture codes along with mentioned files for reading in here. So, feel free to clone it and if you have any problem just raise a question.
Solve classical computer vision topic, image recognition, with simplest method, tiny images and KNN(K Nearest Neighbor) classification, and then move forward to the state-of-the-art techniques, bags of quantized local features and linear classifiers learned by SVC(support vector classifier).
Sentiment analysis for amazon product reviews using NLTK, Scikit-Learn, and Keras. Using hyperparameter search and LSTM, our best model achieves ~96% accuracy.
The project is about Sentimental Analysis of YouTube Comments. Our Model analyses the comments whether it is positive, negative, or neutral, and gives feedback on the YouTube video. We are using two models to analyze the sentiment. i.e. VADER and TEXT BLOB.
The goal of this project is to design a classifier to use for sentiment analysis of product reviews. Our training set consists of reviews written by Amazon customers for various food products. The reviews, originally given on a 5 point scale, have been adjusted to a +1 or -1 scale, representing a positive or negative review, respectively.