Created By Mo Medwani
Learning Objectives:
Understand the fundamentals of NLP, including its history, applications, components, pipeline, toolkits, challenges, and differences between NLU and NLG.
Learning Objectives:
Learn how to preprocess text data with various techniques, including tokenization, POS tagging, stop word removal, text normalization, spelling correction, stemming, lemmatization, NER, and sentence boundary detection.
Learning Objectives:
Understand the different data structures and methods for data cleaning, feature extraction, and vectorization in NLP, including bag-of-words, frequency vector, one-hot encoding, term frequency-inverse term frequency, distributed representation, and word embedding.
Learning Objectives:
Learn how to use TextBlob, a simple and easy-to-use NLP library, for language detection, part-of-speech tagging, word inflection, and sentiment analysis.
Learning Objectives:
Discover how by using spaCy's natural language processing (NLP) capabilities, developers can create applications that can analyze and comprehend large amounts of text. spaCy offers sophisticated tokenization, parsing, and entity recognition capabilities to make this possible.
Learning Objectives:
Gain an understanding of how Text Classification aids in transforming text into predetermined categories based on its content. Through the use of Natural Language Processing (NLP), text can be automatically analyzed and assigned a set of tags or categories based on its content.
Learning Objectives:
Learn how Text summarization is used for condensing a long document into a shorter one by keeping the main points intact and delivers results in a concise and fluent summary of the original text.
Learning Objectives:
Understand how Topic modeling is utilized as a type of unsupervised machine learning that can analyze a collection of documents and identify the main topics, words, and phrases that are used to describe them.
Learning Objectives:
Learn how and why Sentiment analysis is used to analyze text data in order to gauge customer sentiment regarding a brand or product, and to identify customer requirements.
Learning Objectives:
Understand how a chatbot is an AI-based system that allows people to have conversations with a computer in a natural way, helping them get the results they need quickly and efficiently.
| YouTube Videos | Online Courses | KnowledgeHut | |
|---|---|---|---|
| On-Demand Videos | |||
| Immersive Learning Experience | |||
| Hands-On Exercises in Cloud Labs | |||
| Structured Curriculum | |||
| Course Curated by Industry Experts | |||
| Auto-Graded Assessments | |||
| Real-World Projects | |||
| Lifetime Access to Courseware |
Yes, you will experience KnowledgeHut's immersive learning in an on-demand format. This will include e-learning material to help you:
Yes, our online course is designed to give you flexibility to skill up as per your convenience. The course is delivered in a Self-Paced mode so that you can balance your work and learning as per your schedule.
Yes! Upon passing this course, you will receive a signed certificate of completion from KnowledgeHut. Thousands of KnowledgeHut alumni use their course certificate to demonstrate skills to employers and their networks.
More than the certificate, however, you will get to showcase your newly acquired skills by working on real-world projects and adding these to your portfolio. KnowledgeHut’s courses are well-regarded by industry experts, who contribute to our curriculum and use our tech programs to train their own teams.
You can cancel your enrolment and receive refunds in line with our Cancellations and Refunds policy found at https://www.knowledgehut.com/refund-policy.
Please make sure that your computer meets the following software and system requirements:
Yes, it does! In the unlikely event that you are not satisfied with the course, and you wish to withdraw within the first seven days, we’ll issue a 100% refund. Refer to our Online Self-Paced Courses Refund Policy for more details.
