U&P AI – Natural Language Processing (NLP) with Python
The U&P AI – Natural Language Processing (NLP) with Python is the best way for you to gain deep insight and knowledge of this topic. You will learn from industry experts and obtain an accredited certificate after completing the course. Enrol now for a limited-time discounted price.
Like all the courses of One Education, this U&P AI – Natural Language Processing (NLP) with Python is designed with the utmost attention and thorough research. All the topics are broken down into easy to understand bite-sized modules that help our learners to understand each lesson very easily.
We don’t just provide courses at One Education; we provide a rich learning experience. After purchasing a course from One Education, you get complete 1-year access with tutor support.
Our expert instructors are always available to answer all your questions and make your learning experience exquisite.
After completing the U&P AI – Natural Language Processing (NLP) with Python, you will instantly get an e-certificate that will help you get jobs in the relevant field and will enrich your CV.
If you want to learn about this topic and achieve certifications, you should consider this U&P AI – Natural Language Processing (NLP) with Python from One Education.
There are no hidden fees or exam charges. We are very upfront and clear about all the costs of the course.
The course is delivered through our online learning platform, accessible through any internet-connected device. There are no formal deadlines or teaching schedules, meaning you are free to study the course at your own pace.
You are taught through a combination of
- Video lessons
- Online study materials
Will I receive a certificate of completion?
Upon successful completion, you will qualify for the UK and internationally-recognised certification and you can choose to make your achievement formal by obtaining your PDF Certificate at a cost of £9 and Hard Copy Certificate for £15.
Why study this course
Whether you’re an existing practitioner or aspiring professional, this course will enhance your expertise and boost your CV with key skills and an accredited qualification attesting to your knowledge.
The U&P AI – Natural Language Processing (NLP) with Python is open to all, with no formal entry requirements. All you need is a passion for learning, a good understanding of the English language, numeracy and IT, and to be over the age of 16.
|Unit 01: Getting an Idea of NLP and its Applications|
|Module 01: Introduction to NLP||00:03:00|
|Module 02: By the End of This Section||00:01:00|
|Module 03: Installation||00:04:00|
|Module 04: Tips||00:01:00|
|Module 05: U – Tokenization||00:01:00|
|Module 06: P – Tokenization||00:02:00|
|Module 07: U – Stemming||00:02:00|
|Module 08: P – Stemming||00:05:00|
|Module 09: U – Lemmatization||00:02:00|
|Module 10: P – Lemmatization||00:03:00|
|Module 11: U – Chunks||00:02:00|
|Module 12: P – Chunks||00:05:00|
|Module 13: U – Bag of Words||00:04:00|
|Module 14: P – Bag of Words||00:04:00|
|Module 15: U – Category Predictor||00:05:00|
|Module 16: P – Category Predictor||00:06:00|
|Module 17: U – Gender Identifier||00:01:00|
|Module 18: P – Gender Identifier||00:08:00|
|Module 19: U – Sentiment Analyzer||00:02:00|
|Module 20: P – Sentiment Analyzer||00:07:00|
|Module 21: U – Topic Modeling||00:03:00|
|Module 22: P – Topic Modeling||00:06:00|
|Module 23: Summary||00:01:00|
|Unit 02: Feature Engineering|
|Module 01: Introduction||00:02:00|
|Module 02: One Hot Encoding||00:02:00|
|Module 03: Count Vectorizer||00:04:00|
|Module 04: N-grams||00:04:00|
|Module 05: Hash Vectorizing||00:02:00|
|Module 06: Word Embedding||00:11:00|
|Module 07: FastText||00:04:00|
|Unit 03: Dealing with corpus and WordNet|
|Module 01: Introduction||00:01:00|
|Module 02: In-built corpora||00:06:00|
|Module 03: External Corpora||00:08:00|
|Module 04: Corpuses & Frequency Distribution||00:07:00|
|Module 05: Frequency Distribution||00:06:00|
|Module 06: WordNet||00:06:00|
|Module 07: Wordnet with Hyponyms and Hypernyms||00:07:00|
|Module 08: The Average according to WordNet||00:07:00|
|Unit 04: Create your Vocabulary for any NLP Model|
|Module 01: Introduction and Challenges||00:08:00|
|Module 02: Building your Vocabulary Part-01||00:02:00|
|Module 03: Building your Vocabulary Part-02||00:03:00|
|Module 04: Building your Vocabulary Part-03||00:07:00|
|Module 05: Building your Vocabulary Part-04||00:12:00|
|Module 06: Building your Vocabulary Part-05||00:06:00|
|Module 07: Dot Product||00:03:00|
|Module 08: Similarity using Dot Product||00:03:00|
|Module 09: Reducing Dimensions of your Vocabulary using token improvement||00:02:00|
|Module 10: Reducing Dimensions of your Vocabulary using n-grams||00:10:00|
|Module 11: Reducing Dimensions of your Vocabulary using normalizing||00:10:00|
|Module 12: Reducing Dimensions of your Vocabulary using case normalization||00:05:00|
|Module 13: When to use stemming and lemmatization?||00:04:00|
|Module 14: Sentiment Analysis Overview||00:05:00|
|Module 15: Two approaches for sentiment analysis||00:03:00|
|Module 16: Sentiment Analysis using rule-based||00:05:00|
|Module 17: Sentiment Analysis using machine learning – 1||00:10:00|
|Module 18: Sentiment Analysis using machine learning – 2||00:04:00|
|Module 19: Summary||00:01:00|
|Unit 05: Word2Vec in Detail and what is going on under the hood|
|Module 01: Introduction||00:04:00|
|Module 02: Bag of words in detail||00:14:00|
|Module 03: Vectorizing||00:08:00|
|Module 04: Vectorizing and Cosine Similarity||00:10:00|
|Module 05: Topic modeling in Detail||00:16:00|
|Module 06: Make your Vectors will more reflect the Meaning, or Topic, of the Document||00:10:00|
|Module 07: Sklearn in a short way||00:03:00|
|Module 08: Summary||00:02:00|
|Unit 06: Find and Represent the Meaning or Topic of Natural Language Text|
|Module 01: Keyword Search VS Semantic Search||00:04:00|
|Module 02: Problems in TI-IDF leads to Semantic Search||00:10:00|
|Module 03: Transform TF-IDF Vectors to Topic Vectors under the hood||00:11:00|