Applied Machine Learning with NLPLaajuus (10 op)

Osaamistavoitteet

• Learn the basic concepts of NLP and its applications
• Learn various text preprocessing techniques like stemming, lemmatization, tokenizing
• Learn how to analyze a text
• Learn how to embed words
• Get familiar with text vectorization techniques
• Learn how to find the most used words in a text using wordcloud maps
• Learn how to create a simple recommender system
• Learn how to create a simple chatbot
• Gain hands-on experience performing sentiment analysis
• Learn how to classify texts
• Get familiar with machine learning models useful for NLP
• Gain hands-on experience training LSTM models
• Get familiar with deep learning and Keras
• Get acquainted with NLP libraries such as Gensim, NLTK, TextBlob, SpaCy
• Learn how to perform sentiment analysis with LSTM

Sisältö

1. Beginning with This Course
Our Approach in This Course- About TechClass AI Department- Your Expectations, Goals, and Knowledge

2. Introduction
What is NLP?- Why Should We Learn NLP?- Applications of Natural Language Processing- Python Libraries for NLP Problems- A General Overview of this course

3. Text Preprocessing Techniques
Introduction- Lower Case- Tokenization-Remove Unnecessary Elements- Stemming and Lemmatization-Numtowords- Pos tagging

4. Text Analysis
Introduction- Preprocessing- Length Analysis- Word Cloud - Sentiment Analysis- Topic Modeling

5. Language Model
Introduction- What is Language Model?- Text and Sentences Probability- N-Garm and LMS- N-Garm and LMS: Implementation- Neural Language Models- Named Entity Recognition

6. Text Vectorization Techniques
Introduction-One-Hot- Bag of Words- TF-IDF- Similarity Measures- Build Movie Recommender System: EDA- Build Movie Recommender System- Mini project: ChatBot

7. Word Embedding
Introduction- What is Word Embedding?- Count-Based Approach- Count-Based Methods- Word2Vec: Continuous Bag of Words (CBOW)- Word2Vec: Skip-Gram- Word2Vec for Your Corpus- GloVe- Word Embedding for News Category Dataset- Document Embedding

8. Classical Models for Text Classification
Introduction- Best Classical Models for Text Classification- Classification Evaluation Metrics: Review- Naïve Bayes- SVM- Maximum Entropy- Classification of News Category Dataset- Mini project

9. Deep Learning Overview
Introduction- What is Deep learning?- Deep Learning Architectures- What is Keras?- Overfitting- Regularization

10. LSTM for Text Classification
Introduction- Recurrent Neural Networks- How Does LSTM Work?- Why LSTM?- Implementing LSTM Using TensorFlow- Sentiment Analysis Using LSTM: Preprocessing- Sentiment Analysis using LSTM: Model Training- Building Simple Text Generator -Mini project

11. Final Tasks
Self-Study Essay

Esitietovaatimukset

Introduction to Python for Data Science

Arviointikriteeri, hyväksytty/hylätty

Exercises 40%
Quizzes 10%
Project 50%