Natural Language Processing (NLP) in Python with 8 Projects
Natural Language Processing (NLP) in Python with 8 Projects
Work on 8 Projects, Learn Natural Language Processing Python, Machine Learning, Deep Learning, SpaCy, NLTK, Sklearn, CNN
Created by Ankit Mistry, Vijay Gadhave | 10 hours on-demand video course
Complete Natural Language Processing (NLP) with Spacy & NLTK. This course has 10+ Hours of HD Quality video, and following content. Welcome In this section we will get complete idea about what we are going to learn in the whole course and understanding related to natural language processing. Installation & Setup In this section we will get our online environment Google Colab setup. Basics of Natural Language Processing In this section we will dive into all basic NLP task like Tokenization, Lemmatization, stop word removal, name entity recognition, part of speech tagging, and see how to apply with different functions available in a Spacy and NLTK library.
What you’ll learn
- The Complete understanding of Natural Language Processing
- Implement NLP related task with Scikit-learn, NLTK and SpaCy
- Apply Machine Learning Model to Classify Text Data
- Text Classification (Spam Detection, Amazon product Review Classification)
- Text Summarization (Turn 5000 word article into 200 Words)
- Calculate Sentiment Score from Recently Posted Tweet (Tweeter API)
- Refresh your Deep Learning Concepts (ANN, CNN & RNN)
- Build your own Word Embedding (Word2vec) Model with Keras
- Word Embeddings application with Google Pretrained Model
- Spam Message Detection with Neural Network Based CNN and RNN Model
- Automatic Text Generation using TensorFlow, Keras and LSTM
- Working with Text Files & PDF in Python (PyPDF2 module)
- Tokenization, Stemming and Lemmatization
- Stop Words, Parts of Speech (POS) Tagging with NLTK
- Vocabulary, Matching, Named Entity Recognition (NER)
- Data Analysis with Numpy and Pandas
- Data Visualization with Matplotlib library
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