2024 Natural Language Processing in Python for Beginners

Text Cleaning, Spacy, NLTK, Scikit-Learn, Deep Learning, word2vec, GloVe, LSTM for Sentiment, Emotion, Spam & CV Parsing

4.48 (833 reviews)
Data Science
2024 Natural Language Processing in Python for Beginners
30 hours
Jun 2024
last update
regular price

What you will learn

Learn complete text processing with Python

Learn how to extract text from PDF files

Use Regular Expressions for search in text

Use SpaCy and NLTK to extract complete text features from raw text

Use Latent Dirichlet Allocation for Topic Modelling

Use Scikit-Learn and Deep Learning for Text Classification

Learn Multi-Class and Multi-Label Text Classification

Use Spacy and NLTK for Sentiment Analysis

Understand and Build word2vec and GloVe based ML models

Use Gensim to obtain pretrained word vectors and compute similarities and analogies

Learn Text Summarization and Text Generation using LSTM and GRU

Understand the basic concepts and techniques of natural language processing and their applications.

Learn how to use Python and its popular libraries such as NLTK and spaCy to perform common NLP tasks.

Be able to tokenize and stem text data using Python.

Understand and apply common NLP techniques such as sentiment analysis, text classification, and named entity recognition.

Learn how to apply NLP techniques to real-world problems and projects.

Understand the concept of topic modeling and implement it using Python.

Learn the basics of text summarization and its implementation using Python.

Understand the concept of text generation and implement it using Python

Understand the concept of text-to-speech and speech-to-text conversion and implement them using Python.

Learn how to use deep learning techniques for NLP such as RNN, LSTM, and word embedding.

Why take this course?

๐Ÿš€ **Dive into 2024 Natural Language Processing with Python for Beginners!** ๐Ÿ“š Welcome to the "Natural Language Processing in Python for Beginners" course at KGP Talkie, where you'll embark on a journey through the fascinating world of NLP with our expert instructor, Laxmi Kant. This comprehensive course is designed to provide you with a solid foundation and advanced skills in text processing and mining using state-of-the-art NLP algorithms in Python. ๐Ÿงต **Course Highlights:** - **Text Cleaning**: Learn the intricacies of text data preprocessing, essential for accurate analysis. - **Spacy & NLTK**: Master these powerful libraries to parse and understand natural language data. - **Scikit-Learn**: Explore the world of machine learning with hands-on projects using Python's most popular ML library. - **Deep Learning Techniques**: Dive deep into CNN, ANN, LSTM for sentiment analysis, emotion detection, spam filtering, and more. - **word2vec & GloVe**: Understand contextual word embeddings that form the backbone of many NLP applications. - **Project-Driven Learning**: Engage with at least 12 NLP projects to solidify your understanding and apply what you've learned. **What You'll Learn:** - **Machine Learning Fundamentals**: If you're new to Python or Machine Learning, don't worry! We start from the basics. - **Python Essentials**: Get up to speed with Python, NumPy, and Pandas through a crash course if needed. - **Text Data Handling**: Learn how to handle files for storing and loading text data, which is critical in NLP tasks. - **Preprocessing Mastery**: Create your own Python package for text preprocessing to streamline your workflow and enhance your coding skills. - **Classification Techniques**: Develop a text classifier for SPAM and HAM message classification to understand real-world applications of NLP. - **Word Embeddings**: Explore different word representation techniques, including Bag of Words, Term Frequency, Inverse Document Frequency (TF-IDF), and learn how to implement them using Scikit-Learn. - **Machine Learning Model Deployment**: Learn the essentials of deploying NLP models in production environments. **Essential Tools & Techniques Covered:** - **word2vec, GloVe, Deep Learning (CNN, LSTM, RNN)** - **Scikit-Learn, TensorFlow, Keras** - **Numpy, Pandas, Jupyter Notebook** - **Data Visualization** **Why Take This Course?** - **Real-World Applications**: From sentiment analysis to speech recognition, you'll learn how to apply NLP in real-life scenarios. - **Comprehensive Coverage**: This course is packed with information, from the basics of NLP to advanced topics like language modeling and text generation. - **Hands-On Projects**: You'll work on at least 12 NLP projects, ensuring you can solve actual problems by the end of this course. - **Skill Improvement**: By creating your own preprocessing package, you'll improve your coding skills and be able to reuse your code system-wide. **Join Us and Transform Your Data into Meaningful Insights! ๐ŸŒŸ** Enroll in the "Natural Language Processing in Python for Beginners" course today and take your first step towards becoming an NLP expert. With Laxmi Kant's guidance, you'll not only understand how to process and analyze text data but also how to generate creative content using cutting-edge technologies like LSTM. Don't miss out on this opportunity to future-proof your skills in the exciting field of Data Science and Natural Language Processing! ๐Ÿ’ปโœจ


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Our review

๐Ÿ” **Overview of Course Rating and Feedback** The global course rating for this NLP course on Udemy stands at an impressive 4.54 out of 5 stars, based on recent reviews. The feedback from learners has been predominantly positive, highlighting the course's depth and practical presentation of knowledge in the field of Natural Language Processing (NLP). **Pros:** - **Comprehensive Coverage:** Learners have praised the course for its comprehensive coverage of NLP topics, including deep dives into various techniques and models. - **Practical Application:** The course has been recommended for beginners to advanced learners due to its practical approach, with hands-on experience provided on the latest NLP techniques. - **Clear Explanations:** Many learners have found the explanations in the course to be clear and the codebase useful as a reference guide. - **Well-Organized Materials:** The well-organized notebooks that accompany the video lessons have been singled out as an excellent resource for understanding and applying NLP concepts. - **Relevant Topics:** Learners appreciated the relevance of the topics covered in the course, with lessons neither too long nor too short, striking a balance between depth and brevity. - **Engaging Content:** Some learners have described the course as thrilling and enthusiastic, similar to a movie experience. **Cons:** - **Technical Issues:** A few learners have reported technical issues such as slow pacing, difficulty with the instructor's English accent, and minor audio quality concerns. - **Subtitle Misalignments:** Some reviewers have pointed out issues with subtitles not matching the audio, which can be a significant barrier for learners relying on captions. - **Installation Challenges:** There have been complaints about the difficulty in installing the specific software version recommended by the instructor. - **Inadequate Information:** At least one learner felt that there was not enough information provided, with some content appearing redundant and only covering narrow aspects of NLP. - **Limited Interaction:** The absence of timely answers to questions posed by learners has been a point of criticism. - **Accent Challenges:** A few learners have found it difficult to understand the instructor due to his English accent, which has impacted their learning experience. - **Dataset Accessibility:** Some concerns have been raised about the accessibility of datasets used in the course, as one such dataset is no longer available from the provided Git repository. - **Theoretical Understanding:** A learner suggested that while the course provides high-value content, a deeper explanation of theoretical concepts would be beneficial. **Additional Considerations:** - **Course Content and Structure:** The course seems to consist of a series of tutorials rather than a balanced curriculum. Some learners have felt the need to supplement the course with additional research and resources to fully grasp certain topics. - **Model Training:** There are concerns about the models being trained with very small datasets, raising doubts about the practical applicability of the knowledge gained for real-world work scenarios. In summary, this NLP course is highly rated for its comprehensive coverage and practical application but faces challenges related to technical issues, subtitle misalignments, software installation, limited interaction, accent comprehensibility, and dataset accessibility. Learners have found the content engaging and valuable but often required additional resources to achieve a complete understanding of NLP concepts.



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