Скачать с ютуб NLP Tutorial 17 - Multi-Label Text Classification for Stack Overflow Tag Prediction в хорошем качестве

NLP Tutorial 17 - Multi-Label Text Classification for Stack Overflow Tag Prediction

Multi-Label Text Classification in Python with Scikit-Learn. We will use the “StackSample:10% of Stack Overflow Q&A” dataset. It is a problem statement of a multilabel text classification algorithm. We will be developing a text classification model that analyzes a textual description of questions and predicts multiple labels associated with the question. We will implement a multilabel text classification algorithm for a tag suggestion system using Multi-Label Text Classification in Python which is a subset of multiple output models. Text preprocessing is performed on the text data and the cleaned data is loaded for text classification. We will be implementing text Vectorization on text data, encode the tag labels using MultilabelBinarizer and model Classical classifiers(SGC classifier, MultiNomial Naive Bayes Classifier, Random Forest Classifier,…) for modeling and compare the results. In machine learning, Classification is a type of supervised learning. classification refers to a predictive modeling problem where a class label is predicted for a given input sample. It specifies the class to which data point belongs to and is best used when the output has finite and discrete values. There are 4 types of classification tasks that you encounter, they are 1. Binary Classification 2. Multiclass Classification 3. MultiLabel Classification 4. Imbalanced classification The algorithm's accuracy can be increased if we use multi-label text classification using BERT or Keras multi-label text classification. You can also use multi-label text classification with XLNET and GPT-2 and GPT-3. #NaturalLanguageProcessing #MultiLabelClassification #StackOverflowDataset Watch till last for a detailed explanation. 00:00 Introduction 04:36 Notebook Setup 18:30 Multi-Label Binarizer 21:14 TF-IDF Vectorizer 31:02 SGDClassifier, LogisticRegression, and SVM 34:00 OneVsRestClassifier 42:48 Jaccard Similarity Score 51:16 Test the Model with Real Dataset 💯 Read Full Blog with Code: https://kgptalkie.com/multi-label-tex... ~~~~~~~~~~~~~~~~~~~~ 🆓 Watch My Top Free Data Science Videos 👉🏻 Python for Data Scientist https://bit.ly/3dETtFb 👉🏻 Machine Learning for Beginners https://bit.ly/2WOVh7N 👉🏻 Feature Selection in Machine Learning https://bit.ly/2YW6ZQH 👉🏻 Text Preprocessing and Mining for NLP https://bit.ly/31sYMUN 👉🏻 Natural Language Processing (NLP) Tutorials https://bit.ly/3dF1cTL 👉🏻 Deep Learning with TensorFlow 2.0 and Keras https://bit.ly/3dFl09G 👉🏻 COVID 19 Data Analysis and Visualization Masterclass https://bit.ly/31vNC1U 👉🏻 Machine Learning Model Deployment Using Flask at AWS https://bit.ly/3b1svaD 👉🏻 Make Your Own Automated Email Marketing Software in Python https://bit.ly/2QqLaDy ********************************* 📢 BE MY FRIEND 🌍 Check Out ML Blogs: https://kgptalkie.com 🐦Add me on Twitter:   / laxmimerit   📄 Follow me on GitHub: https://github.com/laxmimerit 📕 Add me on Facebook:   / kgptalkie   💼 Add me on LinkedIn:   / laxmimerit   👉🏻 Complete Udemy Courses: https://bit.ly/32taBK2 📣 Check out my Recent Videos: https://bit.ly/3ldnbWm 🔔 Subscribe me for Free Videos: https://bit.ly/34wN6T6 ✍️🏆🏅🎁🎊🎉✌️👌⭐️⭐️⭐️⭐️⭐️ ENROLL in My Highest Rated Udemy Courses to 🔑 Unlock Data Science Interviews 🔎 and Tests 📚 📗 NLP: Natural Language Processing ML Model Deployment at AWS Build & Deploy ML NLP Models with Real-world use Cases. Multi-Label & Multi-Class Text Classification using BERT. Course Link: https://bit.ly/bert_nlp 📊 📈 Data Visualization in Python Masterclass: Beginners to Pro Visualization in matplotlib, Seaborn, Plotly & Cufflinks, EDA on Boston Housing, Titanic, IPL, FIFA, Covid-19 Data. Course Link: https://bit.ly/udemy95off_kgptalkie 📘 📙 Natural Language Processing (NLP) in Python for Beginners NLP: Complete Text Processing with Spacy, NLTK, Scikit-Learn, Deep Learning, word2vec, GloVe, BERT, RoBERTa, DistilBERT Course Link: https://bit.ly/intro_nlp