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

4.31 (662 reviews)
Data Science
Natural Language Processing (NLP) in Python with 8 Projects
10.5 hours
Nov 2023
last update
regular price

What you will 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

Why take this course?

πŸš€ **Master Natural Language Processing with Python!** πŸš€ **Recent Reviews:** 🌟 "Thorough explanation, going great so far. A very simplistic and straightforward introduction to Natural Language Processing. I will recommend this class to any one looking towards Data Science." - Course Taker 🌟 "This course demystifies NLP and makes it accessible with hands-on projects and real-world applications." - Course Enthusiast --- πŸŽ‰ **What You'll Learn:** πŸŽ‰ **Basics of Natural Language Processing:** - Tokenization, Lemmatization, Stop Word Removal, Named Entity Recognition, Part of Speech Tagging with Spacy & NLTK. **Machine Learning for Text Classification:** - Preprocessing data for machine learning algorithms. - Applying Logistic Regression, SVM, Decision Trees to classify text in real-world datasets like IMDB reviews and Yelp reviews. **Deep Learning in NLP:** - Understanding the basics of Artificial Neural Networks. - Implementing word2vec on custom datasets and using Google's pretrained models. **Advanced Deep Learning Techniques:** - Text Classification with CNN & RNN. - Automatic Text Generation using TensorFlow, Keras and LSTM. **Data Analysis & Visualization:** - Refreshing your knowledge on Numpy, Pandas, and Matplotlib. - Processing text files and PDFs. --- πŸ“š **Course Structure:** πŸ“š 1. **Setup & Environment Configuration:** - Get your online environment ready on Google Colab. 2. **NLP Basics:** - Dive into the fundamental tasks of NLP with hands-on exercises. 3-5. **Text Classification Projects:** - Tackle real-world data sets for spam detection and sentiment analysis on platforms like IMDb, Amazon, and Yelp. 6-7. **NLP Applications:** - Explore automatic text summarization and Twitter sentiment analysis using the Twitter API (tweepy). 8. **Deep Learning Fundamentals:** - Understand ANNs and how they work. 9. **Word Embeddings:** - Implement word2vec and explore pre-trained Google models. 10-12. **Text Classification with CNN & RNN:** - Learn to apply advanced deep learning models for text classification tasks. 13. **Automatic Text Generation:** - Experience the power of LSTM networks in generating new text content. 14-16. **Data Analysis & Visualization:** - Brush up on Numpy, Pandas, and Matplotlib for data manipulation and visualization. --- πŸŽ“ **What's Included:** πŸŽ“ - Lifetime access to the "Natural Language Processing (NLP) with Python" course. - Udemy Certificate of Completion. - Support in the Q&A section for any queries you might have. --- πŸš€ **Enroll Now and Transform Your Career with NLP Expertise!** πŸš€ Don't miss out on this comprehensive learning journey. Dive into the fascinating world of Natural Language Processing with Python, and unlock new career opportunities. Let's empower your data analysis capabilities together! Start today, and I (Ankit) along with my co-instructor Vijay, will be there to guide you every step of the way. See you inside the class! 🌐 Regards, Ankit & Vijay


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Our review

🌟 **Course Review Summary** 🌟 **Overall Rating:** 4.44/5 **Pros:** - πŸš€ **Comprehensive Coverage:** The course covers a wide range of NLP topics from basics to advanced concepts like machine learning and deep learning, making it a great resource for beginners as well as those looking to expand their knowledge. - πŸ“š **Practical Approach:** The use of Google Colab Notebooks provides a hands-on learning experience, allowing students to apply concepts in real-time. - βœ… **Use of Libraries:** The course explains essential libraries such as NLTK and Spacy well, which are crucial for NLP tasks. - πŸ€– **Variety of Projects:** The course includes a variety of projects that are interesting and useful for gaining practical experience in NLP. - πŸŽ₯ **Interactive Slides and Materials:** Many learners found the slides and materials provided to be helpful, although some had issues accessing them. **Cons:** - ❓ **Complexity Gap:** Some learners felt that explanations for complex topics like LSTM, CNNs, and RNNs were undercomplex or not adequately explained, leaving a gap in understanding for those new to the concepts. - πŸ› οΈ **Structural Issues:** The order of chapters was noted to be problematic, with some suggesting that the basics should have been at the beginning rather than at the end or omitted. - 🧭 **Incomplete Content:** A few learners pointed out that while the course material is available freely online, the course itself did not provide a comprehensive understanding of more complex NLP topics like transformers and chatbots. - πŸ‘€ **Grammatical Errors:** Some learners were distracted by grammatical errors in the course material, which affected their learning experience. - πŸ“ˆ **Insufficient Justification:** A few learners felt that the course did not sufficiently explain why certain algorithms or features were chosen over others for specific tasks. **Additional Notes:** - 🐧 **Linux Compatibility:** One learner noted that a keyboard shortcut for editing Google Colab Notebooks was different on Linux compared to what was stated in the course. - πŸ“« **Material Access:** Some learners had difficulties accessing or downloading class materials and requested guidance from the course provider. - πŸŽ“ **Learning Approach:** A few learners suggested that a more beginner-friendly approach with less reliance on documentation and more focus on foundational concepts would be beneficial. **Conclusion:** This NLP course is highly regarded by most learners for its comprehensive coverage of the subject matter and practical, hands-on approach. However, some learners felt that certain aspects of the course, particularly around complex topics and structural organization, could be improved. Despite these criticisms, the course remains a valuable resource for those interested in learning NLP through projects and real-world applications. If you're looking to enhance your understanding of NLP with hands-on experience and have some background knowledge, this course is recommended.



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