Data Science: Natural Language Processing (NLP) in Python

Applications: decrypting ciphers, spam detection, sentiment analysis, article spinners, and latent semantic analysis.

4.65 (12864 reviews)

Udemy

platform

English

language

Data Science

What you will learn

Write your own cipher decryption algorithm using genetic algorithms and language modeling with Markov models

Write your own spam detection code in Python

Write your own sentiment analysis code in Python

Perform latent semantic analysis or latent semantic indexing in Python

Have an idea of how to write your own article spinner in Python

Understand important foundations for OpenAI ChatGPT, GPT-4, DALL-E, Midjourney, and Stable Diffusion

Course Gallery

Data Science: Natural Language Processing (NLP) in Python – Screenshot 1 — Screenshot 1 – Data Science: Natural Language Processing (NLP) in Python

Data Science: Natural Language Processing (NLP) in Python – Screenshot 2 — Screenshot 2 – Data Science: Natural Language Processing (NLP) in Python

Data Science: Natural Language Processing (NLP) in Python – Screenshot 3 — Screenshot 3 – Data Science: Natural Language Processing (NLP) in Python

Data Science: Natural Language Processing (NLP) in Python – Screenshot 4 — Screenshot 4 – Data Science: Natural Language Processing (NLP) in Python

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Comidoc Review

Our Verdict

Overall, the Data Science: Natural Language Processing (NLP) in Python course offers valuable insights into NLP applications with an emphasis on hands-on exercises. The wide range of topics and focus on modern OpenAI models provide a comprehensive learning experience for aspiring NLP practitioners. However, potential students should be aware that some may find the instructor's teaching style off-putting or overly focused on mathematical concepts. By weighing these factors against personal preferences and objectives, learners can make an informed decision on whether this course aligns with their needs.

What We Liked

Covers a wide range of NLP applications such as cipher decryption, spam detection, sentiment analysis, and latent semantic analysis using Python
Includes practical exercises that allow students to write their own algorithms for each topic, building confidence and hands-on experience
The course is consistently updated with a focus on OpenAI models like ChatGPT and DALL-E, making it relevant and valuable for modern NLP learners

Potential Drawbacks

Some students have reported the instructor's responses to be condescending and unhelpful when assistance was needed
The strong focus on mathematical foundations may alienate some learners who prefer a more practice-oriented approach without extensive formulas
A few learners mentioned that the content was basic and not applicable in real-life scenarios, although this varies depending on personal goals