Multi-Topic (Multi-Class) Text Classification Part A: Introduction

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Related Tutorial Playlists in English:
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Diğer İlgili Eğitimler (Türkçe):
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TURKISH: (ENGLISH BELOW)
Bu, metin sınıflandırmanın tüm aşamalarını kapsayan eğitim serisinin birinci Bölümüdür: Keşifsel Veri Analizi (EDA), Metin ön işleme TF Veri Hattı, Keras TextVectorization ön işleme katmanı
Çok sınıflı (çok konulu) metin sınıflandırması, Derin Öğrenme modeli tasarımı ve uçtan uca model uygulaması, Performans değerlendirmesi ve metrikler, Sınıflandırma raporu oluşturuluyor, Hiper parametre ayarı vb. kullanarak çeşitli Derin Öğrenme modelleri tasarlayacağız.
Keras Gömme katmanı, Evrişimsel (Conv1D) katman, Tekrarlayan (LSTM) katman, Transformer Encoder bloğu ve önceden eğitilmiş transformatör (BERT).
Python / TensorFlow / Keras ortamında örnek uygulamalarla Çok Sınıflı Metin Sınıflandırma problemlerinin çözümü ile ilgili tüm konuları işleyeceğiz. Metin Sınıflandırması Nedir?
Metin sınıflandırma, bir dizi önceden tanımlanmış kategoriyi (etiketler/sınıflar/konular) açık uçlu metne atayan bir makine öğrenimi tekniğidir. Sınıflandırma Türleri:
Genel olarak 3 tip sınıflandırma vardır: ikili sınıflandırma, Çok sınıflı sınıflandırma, Çok etiketli sınıflandırma.
Otomatik Metin Sınıflandırma Yaklaşımları Nelerdir? Otomatik metin sınıflandırmaya yönelik aşağıdakiler gibi birçok yaklaşım vardır: Kural tabanlı sistemler, Makine öğrenimi tabanlı sistemler, Derin öğrenme tabanlı sistemler, Hibrit sistemler
ENGLISH: Multi-Topic Multi-Class Text Classification
This is the Part A of the tutorial series that covers all the phases of text classification: Exploratory Data Analysis (EDA), Text preprocessing, TF Data Pipeline, Keras TextVectorization preprocessing layer, Multi-class (multi-topic) text classification, Deep Learning model design & end-to-end model implementation, Performance evaluation & metrics, Generating classification report, Hyper-parameter tuning, the Keras Embedding layer, Convolutional (Conv1D) layer,
Recurrent (LSTM) layer, Transformer Encoder block, and pre-trained transformer (BERT). We will cover all the topics related to solving Multi-Class Text Classification problems with sample implementations in Python / TensorFlow / Keras environment.
We will use a Kaggle Dataset in which there are 32 topics and more than 400K total reviews. What is Text Classification? Text classification is a machine learning technique that assigns a set of predefined categories (labels/classes/topics) to open-ended text. The categories depend on the selected dataset and can cover arbitrary subjects. Therefore, text classifiers can be used to organize, structure, and categorize any kind of text.
Types of Classifications: In general, there are 3 types of classification Binary classification Multi-class classification
Multi-label classification
How and Where can we use Text Classifiers?
Text classifiers can be used to organize, structure, and categorize pretty much any kind of text — from documents, medical studies and files, and all over the web.
What are the Automatic Text Classification Approaches?
There are many approaches to automatic text classification such as:
Rule-based systems
Machine learning-based systems
Deep learning-based systems
Hybrid systems

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