Document classification using Machine Learning

Introduce the basics of machine learning and explore unsupervised techniques for document classification, revealing patterns and practical techniques relevant to industry applications.

Learn how machine learning teaches computers to recognize patterns in data and predict future behavior, with examples from self-driving cars, facial recognition, and medical diagnosis.

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.

Explain what machine learning is and how the industry moves toward it, review techniques like linear regression, logistic regression, and unsupervised learning, and describe how topic modeling aids document classification.

Install Anaconda and Python, install libraries for document classification, and set up Jupiter for coding to begin exploring tools like sikat, natural language tollgate, and regular expression.

Install and verify Anaconda with Python (Python 3.6) on Windows or macOS, set up the Anaconda Navigator, create a shortcut, and confirm installation via the command line.

Install free open source libraries for document classification, using LDK for natural language processing, regex for text handling, matplotlib for plotting, and gensym for machine learning modeling, via Anaconda Navigator.

Set up a Python 3.6 environment with Anaconda and Jupyter, install the natural language toolkit (nltk) for preprocessing, remove stopwords, and learn data preparation steps while coding the application.

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.

Learn to preprocess data for document classification by importing libraries, removing stop words, and loading and splitting headlines into a clean document list for topic modeling.

Convert sentences to words by tokenizing and removing punctuation. Build meaningful features by creating bigrams and trigrams, removing stop words, and applying normalization.

Convert the text into a word-id dictionary and a bag-of-words corpus, then train a gensim LDA model to identify four topics. Classify new documents by assigning them to these topics.

Explore perplexity and coherence to evaluate an LDA model, and visualize four topics with word distributions to reveal technology, sports, politics, and world news themes.

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.

Analyze how to execute a Python core cell by cell, preprocess text, tokenize into words with unique IDs, build a corpus, and train a model while evaluating coherence and perplexity.

Discover real-world uses of document classification in machine learning, including topic labeling, fast information retrieval, training models to categorize new documents, and finding similar documents with document embeddings.