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What you would learn in Data Science: Natural Language Processing (NLP) in Python course?
In this course, you'll develop various systems that you can implement that utilize natural language processing, also known as NLP, a branch of data science and machine learning that handles speech and text. This course isn't an element of the deep-learning series I teach. Therefore, it's not a rigorous maths course- just basic Python programming. The materials for this course are provided for free.
In the wake of a quick discussion on the basics of what NLP can do and the things it's capable of doing, then we'll start building handy software. Our first item to create is an encryption algorithm that decrypts ciphers. They can be used in espionage and warfare. This section teaches how to construct and use various proper NLP instruments, including character-level language models (using the Markov principle) and genetic algorithms.
The second is where we employ more conventional " machine learning" to build automated spam detection. There is probably not much mail these days, compared to, for instance, the beginning of the 2000s due to technology like this.
The next step is to create an analysis model to perform sentiment analysis in Python. This will allow users to give a value to an entire block of text, which determines the score. Many people use sentiment analysis through Twitter to forecast the performance of stocks.
We'll review some of the valuable tools and techniques, such as that of the NLTK (natural language tools) library and the latent semantic analysis, or LSA.
The final step is to conclude the course by making the article spinning device. This is a problematic issue; even the best-known items available today do not have the correct answers. The lectures are designed to help you get going and provide suggestions on how to improve them on your own. Once you've mastered it, you can use this as an SEO, a search engine optimization device. Internet marketers worldwide will adore them if you make this work for them!
This course is explicitly focused on " how to build and understand," not "how to use." Anyone can master an API within 15 minutes of studying the documents. The focus is not on "remembering facts"; it's about "seeing for yourself" by doing experiments. This course will help you to see what's happening within the model's internals. If you're looking to know more than an introductory study of models that use machine learning, this course is perfect the right one for you.
Write your encryption strategy using genetic algorithm and language modeling using Markov models.
Create the spam-detection code using Python
Create your code for sentiment analysis in Python
Do latent semantic analyses, also known as latent semantic indexing, using Python.
Have an idea on how to create your article spinner using Python
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