About this Course
The Hands-on Natural Language Processing (NLP) Boot Camp is an immersive, three-day course that serves as your guide to building machines that can read and interpret human language. NLP’s significance in understanding and leveraging text data has never been more critical, with applications ranging from analyzing customer feedback, implementing chatbot solutions, to extracting valuable insights from large volumes of text data.
The course offers an engaging blend of theoretical knowledge and practical skills, diving into key NLP areas such as text processing, sentiment analysis, topic modeling, and the application of machine learning and deep learning in NLP. Throughout the course, you’ll have the opportunity to apply your newfound skills in hands-on labs, focusing on real-world tasks such as text classification, sentiment analysis, and even building your very own chatbot. This hands-on approach, paired with expert guidance and the use of innovative tools like NLTK, sklearn, and Keras, ensures an active, engaging, and valuable learning experience.
By the end of the course, you’ll be equipped with the ability to process and analyze text data effectively, implement advanced text representations, apply machine learning algorithms for text data, and build simple chatbots. You will leave with practical skills and insights that you can immediately put to use, helping your organization gain valuable insights from text data, streamline business processes, and improve user interactions with automated text-based systems.
This in an intermediate and beyond-level course is geared for experienced Python developers looking to delve into the exciting field of Natural Language Processing. It is ideally suited for roles such as data analysts, data scientists, machine learning engineers, or anyone working with text data and seeking to extract valuable insights from it. If you're in a role where you're tasked with analyzing customer sentiment, building chatbots, or dealing with large volumes of text data, this course will provide you with practical, hands-on skills that you can apply right away.
At Course Completion
This course combines engaging instructor-led presentations and useful demonstrations with valuable hands-on labs and engaging group activities. Throughout the course you’ll learn how to:
· Master the fundamentals of Natural Language Processing (NLP) and understand how it can help in making sense of text data for valuable insights.
· Develop the ability to transform raw text into a structured format that machines can understand and analyze.
· Learn how to implement sentiment analysis and topic modeling to extract meaning from text data and identify trends.
· Gain proficiency in applying machine learning and deep learning techniques to text data for tasks such as classification and prediction.
· Acquire the skills to design and build simple chatbots and question-answering systems to automate text-based interactions.
Instructor did a great job, from experience this subject can be a bit dry to teach but he was able to keep it very engaging and made it much easier to focus.
Excellent presentation skills, subject matter knowledge, and command of the environment.
Instructor was outstanding. Knowledgeable, presented well, and class timing was perfect.
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· Proficiency in Python: As the course involves Python for hands-on labs and examples, attendees should have a good understanding of Python programming, including data structures, control flow, and basic coding practices.
· Basic knowledge of Machine Learning: Understanding the principles of machine learning, including concepts like training and testing splits, model evaluation, and overfitting, will be beneficial.
· Familiarity with Linear Algebra and Statistics: Some fundamental concepts in linear algebra (such as vectors and matrices) and statistics (mean, median, standard deviation, etc.) are essential for understanding the theory behind NLP.
· Experience with any Data Analysis Libraries: Having experience with Python data analysis libraries like Pandas, NumPy, or Matplotlib can be beneficial as they are often used in the preprocessing and analysis of text data.
· General Understanding of Natural Language Processing: While not strictly necessary, having a basic understanding of what NLP is and its potential applications can help attendees contextualize the learnings better.
Take Before: Students should have incoming practical skills aligned with those in the course(s) below, or should have attended the following course(s) as a pre-requisite:
· TTPS4876 Intermediate Python Programming for Data Science
· TTML5503 Introduction to AI, AI Programming and Machine Learning
Detailed Class Syllabus
1. Introduction to Natural Language Processing (NLP)
· Understand what NLP is and its importance.
· Differences between natural languages and programming languages.
· Practical applications of NLP.
· How computers interpret language.
· The main concepts and techniques in NLP.
· LP pipeline introduction.
· Python's NLP libraries
· Lab: Setting up the environment and exploring basic text processing in Python.
2. Text Processing and Analysis (2 Hours)
· Tokenization, lemmatization, and stemming.
· Part-of-speech tagging role in NLP.
· Text data cleaning techniques.
· Concept of stop words and their removal.
· Regular expressions for advanced text processing tasks.
· Named Entity Recognition (NER) introduction.
· Lab: Text preprocessing on a sample text dataset.
3. Text Representation and Feature Extraction
· Bag-of-Words and TF-IDF for text representation.
· Word embeddings and their importance.
· Creating word embeddings using Word2Vec and GloVe.
· Limitations of these techniques and contextual embeddings introduction.
· Advanced methods like BERT.
· Feature extraction techniques for NLP tasks.
· Lab: Creating word embeddings using Word2Vec on a sample dataset.
4. Sentiment Analysis
· Sentiment analysis concept and applications.
· Techniques for sentiment analysis.
· Training a model for sentiment analysis.
· Handling negations and multi-word expressions in sentiment analysis.
· Emotion detection and opinion mining.
· Evaluating a sentiment analysis model.
· Lab: Developing a sentiment analysis model on a movie reviews dataset.
5. Topic Modeling
· Topic modeling concept and applications.
· Techniques like Latent Dirichlet Allocation (LDA) for topic modeling.
· Non-negative Matrix Factorization (NMF) for topic modeling.
· Interpreting topic modeling results.
· Tuning topic models.
· Coherence score for determining the optimal number of topics.
· Lab: Developing a topic modeling system on a news dataset.
6. Text Classification
· Text classification concept and applications.
· Feature selection and dimensionality reduction in text classification.
· Training a text classification model.
· Handling imbalanced classes in text classification.
· Evaluation of text classification models.
· Multi-label text classification.
· Lab: Developing a text classification system on a customer complaints dataset.
7. Sequence-to-sequence Models and Chatbots
· Basics of sequence-to-sequence models.
· Encoder-Decoder architecture.
· Concept of attention in sequence-to-sequence models.
Design and implementation of chatbots.
· Limitations and challenges in developing chatbots.
· Lab: Developing a simple chatbot.
Optional / Bonus Content: Follow On
Optional / Bonus Content: Follow On
Optional: Capstone Project
· Apply the skills learned throughout the course.
· Define the problem and gather the data.
· Conduct exploratory data analysis for text data.
· Carry out preprocessing and feature extraction.
· Select and train a model.
· Evaluate the model and interpret the results.
· Lab: Complete a capstone project where students tackle a real-world NLP problem from start to finish.
Bonus Chapter: Generative AI and NLP
· Introduction to Generative AI and its role in NLP.
Using GPT models for text generation and completion.
· Applying GPT models for improving autocomplete features.
· Use cases of GPT in question answering systems and chatbots.
· Lab: Implementing a text completion application using GPT-3.
Bonus Chapter: Advanced Applications of NLP with GPT
· Fine-tuning GPT models for specific NLP tasks.
· Using GPT for sentiment analysis and text classification.
· Role of GPT in Named Entity Recognition (NER).
· Application of GPT in developing advanced chatbots.
· Ethics and limitations of GPT and generative AI technologies.
· Lab: Fine-tuning GPT-3 for a specific NLP task.