Requirements Basic understanding of Python Description Work on 5 Real Life Projects with…
- No. Only some coding experience with any programming language
- You don’t need any prior knowledge on NLP or Python
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This course is carefully designed for you to learn the fundamentals of Natural Language Processing and then to advance gradually and to solve complex NLP problems using Machine Learning. Everything taught in this course is completely hands on. So you will be able to learn things by doing them yourself.
You don’t need prior experience in Natural Language Processing, Machine Learning or even Python. But you should be comfortable with programming, and should be familiar with at least one programming language. Python is by far one of the best programming language to work on Machine Learning problems and it applies here as well. If you’re new to Python, don’t worry, I’ll explain what you need to know, just before using it.
In this course, we use Google Colab to run our code. So, you don’t have to install or configure anything in your machine. It doesn’t matter what’s your OS or hardware spec, as long as you have access to the Internet. But if you’re interested, the same code can be run on Jupyter Notebook, installed in your machine.
First we will explore the basic concepts of Natural Language Processing, such as tokenization, stemming and lemmatization using NLTK. You will learn more than one way to get these things done, so you can understand the pros and cons of different approaches. Then we will study some pre-processing techniques for removing stop-words, whitespaces, punctuations, symbols, new lines, etc.
Next we will move to SpaCy – a state of the art NLP library heavily used in the industry. We will explore the NLP pipeline, and more advanced concepts such as Named Entity Recognition and Syntactic Dependencies. These techniques allow your code to automatically understand concepts like money, time, companies, products, locations, and many more simply by analysing the text information.
There we will cover Part-of-Speech tagging as well, where your code will be able to automatically assign words in text to their appropriate part of speech, such as nouns, verbs, adverbs and adjectives, an essential part of building intelligent language systems.
After that, you will learn how to transform text into a format where the computer can understand. This process is called vectorization. There’re more than one way to do this, and you will learn the two most common mechanisms. Count vectorization and TF-IDF vectorization.
What are you waiting for? Start your journey to become an expert in NLP today!
All of this comes with a 30 day money back garuantee, so you can try the course risk free.
I will see you inside the course.