NLP Boot Camp / Hands-on Natural Language Processing (TTAI3030)

Course Objectives

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.

Course Prerequisites

Audience

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.

Pre-Requisites

• 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:

• TTPS4873      Fast Track to Python for Data Science
• TTML5503   Introduction to AI, AI Programming and Machine Learning

Course Agenda

Course Materials

Setup Made Simple! Learning Experience Platform (LXP) 

All applicable course software, digital courseware files or course notes, labs, data sets and solutions, live coaching support channels and rich extended learning and post training resources are provided for you in our “easy access, no install required” online Learning Experience Platform (LXP), remote lab and content environment. Access periods vary by course. We’ll collaborate with you to ensure your team is setup and ready to go well in advance of the class. Please inquire about set up details and options for your specific course of interest.