Course Details

Natural Language Processing (NLP) is a rapidly growing field that focuses on teaching machines to understand and interpret human language. NLP is a core component of many AI applications and is essential for businesses to be able to analyze large amounts of textual data, such as social media posts, emails, customer reviews, and more. This course will give you the skills and knowledge you need to work with NLP and apply it to a variety of use cases.

Significant growth in the volume and variety of data is due to the accumulation of unstructured text data—in fact, up to 80% of all your data is unstructured text data. Companies collect massive amounts of documents, emails, social media, and other text-based information to get to know their customers better, offer customized services, or comply with federal regulations. However, most of this data is unused and untouched.

Text analytics, through the use of natural language processing (NLP), holds the key to unlocking the business value within these vast data assets. In the era of big data, the right platform enables businesses to fully utilize their data lake and take advantage of the latest parallel text analytics and NLP algorithms. In such an environment, text analytics facilitates the integration of unstructured text data with structured data (e.g., customer transaction records) to derive deeper and more complete depictions of business operations and customers.

Our NLP course is designed to provide you with a strong foundation in the key concepts and techniques used in NLP. You will learn how to process and analyze text data using various techniques, including tokenization, parsing, and part-of-speech tagging. You will also learn how to build machine-learning models for text classification, sentiment analysis, and more.

Course Curriculum:

• Introduction to Natural Language Processing • What is NLP and why is it important? • Applications of NLP in business

• Text Preprocessing • Tokenization • Parsing • Part-of-speech tagging • Named entity recognition • Stemming and lemmatization

• Text Classification • Supervised and unsupervised learning • Naive Bayes classifier • Logistic regression • Support vector machines • Deep learning models for text classification

• Sentiment Analysis • Sentiment lexicons • Rule-based methods • Machine learning-based methods • Deep learning-based methods

• Topic Modeling • Latent Dirichlet Allocation (LDA) • Non-negative Matrix Factorization (NMF) • Applications of topic modeling

• Word Embeddings • Distributed representations of words • Word2vec • GloVe • Applications of word embeddings

• Text Generation • Language models • Recurrent Neural Networks (RNNs) • Long Short-Term Memory (LSTM) networks • Applications of text generation

This course is designed for data scientists, machine learning engineers, and anyone interested in working with NLP. Basic knowledge of programming in Python and statistics is recommended. Upon completion of this course, you will have the knowledge and skills to work with NLP tools and techniques to process, analyze, and understand textual data. You will also have the ability to build and evaluate machine learning models for text classification, sentiment analysis, and more