
Learn how document classification uses machine learning to assign texts to categories like sports, business, and technology. Real-world examples from news and bookstores illustrate how classification improves content discovery.
Explore document classification techniques and their industry applications. Learn what you will gain from this course and the benefits of learning.
Explore supervised and unsupervised machine learning techniques, including linear and logistic regression, gradient descent, and clustering, with applications to housing prices, spam, and recommendations.
Latent Dirichlet allocation enables unsupervised document classification into a probabilistic mixture of topics, using local and global topic metrics and iterative word reassignment to infer distributions.
Overview of building a document classification model using machine learning, covering coding setup, preprocessing with stopword removal, tokenization, training on a news dataset, and evaluating performance with hyperparameters.
Set up your programming environment with Jupyter notebook and Anaconda navigator, load and explore a text dataset, and run a topic modeling workflow using Python code cells.
Explore the LDA Mallet model for document classification by loading the Mallet implementation, running with various topic counts, and optimizing the coherence score to identify the best number of topics.
Course Description
Learn the document classification with the machine learning and popular programming language Python.
Build a strong foundation in Machine Learning with this tutorial for beginners.
Understanding of document classification
Leverage Machine Learning to classify documents
User Jupyter Notebook for programming
Use Latent Dirichlet Allocation Machine Learning Algorithm for document classification
A Powerful Skill at Your Fingertips Learning the fundamentals of document classification puts a powerful and very useful tool at your fingertips. Python and Jupyter are free, easy to learn, has excellent documentation.
Jobs in machine learning area are plentiful, and being able to learn document classification with machine learning will give you a strong edge.
Machine Learning is becoming very popular. Alexa, Siri, IBM Deep Blue and Watson are some famous example of Machine Learning application. Document classification is vital in information retrieval, sentiment analysis and document annotation. Learning document classification with machine learning will help you become a machine learning developer which is in high demand.
Big companies like Google, Facebook, Microsoft, AirBnB and Linked In already using document classification with machine learning in information retrieval and social platforms. They claimed that using Machine Learning and document classification has boosted productivity of entire company significantly.
Content and Overview
This course teaches you on how to build document classification using open source Python and Jupyter framework. You will work along with me step by step to build following answers
Introduction to document classification.
Introduction to Machine Learning
Build an application step by step using LDA to classify documents
Tune the accuracy of LDA model
Learn variation of LDA model
Learn use cases of LDA model
What am I going to get from this course?
Learn document classification and Machine Learning programming from professional trainer from your own desk.
Over 10 lectures teaching you document classification programming
Suitable for beginner programmers and ideal for users who learn faster when shown.
Visual training method, offering users increased retention and accelerated learning.
Breaks even the most complex applications down into simplistic steps.
Offers challenges to students to enable reinforcement of concepts. Also solutions are described to validate the challenges.
Note: Please note that I am using short documents in this example to illustrate concepts. You can use same code for longer documents as well.