What you would learn in Natural Language Processing using Python course?
Machine Learning projects rely on numeric and textual data that is stored in traditional databases. It isn't easy to develop intelligent applications using text data purely. Why is this? First, the text data available in the world is many times larger than the numeric information in conventional databases. The question is how can we extract useful information from this vast corpus of text data, which can easily run to several terabytes (or even petabytes). Machine learning is radically changed when you consider the size of the data. The traditional databases have a low number of columns. This means that the features available for machine learning are minimal. They typically range from tens to a few hundred. NLP applications do not have columns like structured databases. Therefore, every word in the text corpus can be considered a candidate for a model training feature. It isn't easy to train a model that has millions of features. To develop ML applications, it is necessary to reduce the features count by reducing vocabulary. Our dumb machine can only understand binary data, so it is a second requirement. This is where NLP learning differs from model development using structured databases. The rest of the model development process is the same as the traditional, also known as Good Old Fashioned AI.
This course will teach you how to prepare large text datasets for machine learning. There are many text-preprocessing techniques you can learn, including stemming, lemmatization, and removing stop words.
The traditional statistics-based algorithms will be used to train the models. Five industry-standard NLP applications will be developed. These applications will cover a broad range of NLP domains. You will be able to learn both binary and multi-class classifications. Both supervised and unsupervised learning will be used. Unsupervised clustering will be taught on text data. You will use LDA (LatentDirichletAllocation) algorithm for clustering. Support vector machines will be used to classify text.
You will also learn how to classify research articles and sentiment analysis.
This course will help you get a head start in NLP. It also teaches you how to master several NLP techniques using a practical approach. Every lesson includes code to practice, making learning quick and easy.
Text pre-processing techniques for huge datasets
NLP-based project development using Good Old Fashioned AI.
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