Overview
Unlock the insights in your text data, documents and comments with machine learning using Python
Most of the world’s data is in the form of text, whether it be comments on web sites, transcriptions of phone calls, Word or PDF documents, news articles, web pages, emails and SMS, social media, chats, feedback or one of many more. Today, our ability to analyse and understand text data means you no longer need to capture all your data in structured databases to get value out of it. Using natural language processing (NLP) techniques will help you analyse text and unleash the power of your data—the possibilities are amazing! In this course you will learn to use machine learning to analyse the sentiment in your text – whether it is positive, negative or neutral. Among other uses, this can help you classify customer or employee feedback into positive, negative or neutral sentiment.
Python is one of the world’s most powerful and most popular data science and machine learning tools. In this course you will learn to use Python to process and classify text.
Designed to be an accessible introduction to learning natural language processing with Python, this course includes numerous practical applications and examples.
Learning objectives
On completion of this course, you will be able to
- Read text data into Python and perform exploration
- Apply text preprocessing techniques including tokenisation, lemmatisation, and stemming
- Understand how to use stop words and parts of speech tagging in text processing
- Build a basic sentiment analysis calculator
- Apply machine learning models to classify text data and perform sentiment analysis
Who this course is designed for
This course is ideally suited for
- Those interested in analysing text but are unsure where to start
- Aspiring data scientists
- Business leaders who want to get insights from their text data
- Leaders of teams of data scientists
- Python developers looking to embed natural language processing and machine learning in their applications
- Business analysts who want to understand text analysis
Prerequisites
Although this course will explore using Python and machine learning, it does not require a background in either. What is required is a willingness to learn about text and a willingness to code. (A beginner’s knowledge of Python will be helpful, but not necessary.)
You will need to have access to the course’s virtual machine or set up your own environment using the course’s Docker image on your own personal or cloud computing resource. Do not worry though, as help will be on hand to guide you through the process.
Course Curriculum
- What is sentiment analysis and when should I use it? (10:47)
- Understanding the data and the problem (7:06)
- Foundations of preprocessing data for text analysis (3:36)
- Make a word cloud (2:47)
- Create your first sentiment calculator (2:55)
- Advanced text preprocessing (7:43)
- Train a naïve Bayes sentiment classifier (7:08)
Dr James Pearce
James is the founder of 3 Crowns Consulting, a data consulting and training firm that helps managers and analysts get value out of their data. His clients include major Australian banks, insurance companies, fundraisers and wealth management companies.
As well as being passionate about teaching machine learning and make sense of data, James works as a Principal Consultant in Data Science for Suncorp. In other roles he has developed several market-leading data products, as well as machine learning systems that are used in operations to cut costs and increase revenue.
In his spare time, James enjoys writing about analytics and data; he also writes fiction.