Course Topics / Agenda
Please note that this list of topics is based on our standard course offering, evolved from typical industry uses and trends. We’ll work with you to tune this course and level of coverage to target the skills you need most. Topics, agenda and labs are subject to change, and may adjust during live delivery based on audience skill level, interests and participation.
Day 1
- Launch into the Universe of Natural Language Processing
- The journey begins: Unravel the layers of NLP
- Navigating through the history of NLP
- Merging paths: Text Analytics and NLP
- Decoding language: Word Sense Disambiguation and Sentence Boundary Detection
- First steps towards an NLP Project
- Unleashing the Power of Feature Extraction
- Dive into the vast ocean of Data Types
- Purification process: Cleaning Text Data
- Excavating knowledge: Extracting features from Texts
- Drawing connections: Finding Text Similarity through Feature Extraction
Day 2
- Engineer Your Text Classifier
- The new era of Machine Learning and Supervised Learning
- Architecting a Text Classifier
- Constructing efficient workflows: Building Pipelines for NLP Projects
- Ensuring continuity: Saving and Loading Models
- Master the Art of Web Scraping and API Usage
- Stepping into the digital world: Introduction to Web Scraping and APIs
- The great heist: Collecting Data by Scraping Web Pages
- Navigating through the maze of Semi-Structured Data
Day 3
- Unearth Hidden Themes with Topic Modeling
- Embark on the path of Topic Discovery
- Decoding algorithms: Understanding Topic-Modeling Algorithms
- Dialing the right numbers: Key Input Parameters for LSA Topic Modeling
- Tackling complexity with Hierarchical Dirichlet Process (HDP)
- Delving Deep into Vector Representations
- The Geometry of Language: Introduction to Vectors in NLP
- Text Manipulation: Generation and Summarization
- Playing the creator: Generating Text with Markov Chains
- Distilling knowledge: Understanding Text Summarization and Key Input Parameters for TextRank
- Peering into the future: Recent Developments in Text Generation and Summarization
- Solving real-world problems: Addressing Challenges in Extractive Summarization
- Riding the Wave of Sentiment Analysis
- Unveiling emotions: Introduction to Sentiment Analysis Tools
- Demystifying the Textblob library
- Preparing the canvas: Understanding Data for Sentiment Analysis
- Training your own emotion detectors: Building Sentiment Models
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. (1 hour)
- Tools Used: Python, Various Python Libraries (based on project needs)
Bonus Chapter: Generative AI and NLP
- Introduction to Generative AI and its role in NLP.
- Overview of Generative Pretrained Transformer (GPT) models.
- 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.
- Tools Used: Python, OpenAI GPT-3 API
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.
- Tools Used: Python, OpenAI GPT-3 API, TensorFlow
Learning Objectives
This course combines engaging instructor-led presentations and useful demonstrations with valuable hands-on labs and engaging group activities. Throughout the course you’ll:
- 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.
- Discover how to collect data from the web and navigate through semi-structured data, opening up a wealth of data sources for your projects.
- 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.
- Learn to analyze text sentiment, train emotion detectors, and interpret the results, providing a way to gauge public opinion or understand customer feedback.
- 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.
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You will receive a receipt and an enrollment confirmation sent to the email you submitted at purchase. Your enrollment email will have instructions on how to access the class. Any additional questions our team is here to support you. Please call us at 469-721-6100.
If a student is 15 minutes late, they risk losing their seat to a standby student. If a student is 30 minutes late or more, they will need to reschedule. A no-show fee will apply. Retakes are enrolled on a stand-by basis. The student must supply previously issued courseware. Additional fees may apply.
You will receive a ‘Certificate of Completion’ once you complete the class. If you purchased an exam voucher for the class, a team member from TOPTALENT LEARNING will reach out to discuss your readiness for the voucher and make arrangements to send it.
