Welcome to the Course: Natural Language Processing and Regular Expressions

Welcome to this video course on natural language processing (NLP) and regular expressions. In this course, you will learn about the basics of NLP and how to apply them to various tasks such as topic identification, text classification, translation, and sentiment analysis. While this is the first course in a series, you can expect to get exposure to some of the challenges in the field, including the use of regular expressions.

Regular Expressions: A Powerful Tool for Pattern Matching

Regular expressions are strings that have a special syntax which allows you to match patterns and find other strings. A pattern is a series of letters or symbols that can map to an actual text or words or punctuation. You can use regular expressions to do things like find links in a webpage, parse email addresses, and remove unwanted strings or characters. Regular expressions are often referred to as reg X and can be used easily with Python via the re library.

Importing the Re Library

To get started with regular expressions, you need to import the re library in Python. Here is an example of how to do this: `import re`. This will allow you to use the various methods provided by the re library to match patterns and find strings that match them.

Matching a Substring with the Re Match Method

The re.match method is used to match a pattern with a string. It takes the pattern as the first argument and the string as the second argument, and returns a match object if the pattern matches the string, or None otherwise. Here is an example of how to use this method: `match = re.match('ABC', 'hi there')`. In this case, the pattern 'ABC' matches exactly with the substring 'ABC' in the string 'hi there'.

Using Special Patterns in Regular Expressions

Regular expressions understand special patterns that can be used to match different types of strings. For example, the backslash W+ pattern is used to match a word. You can see this in action using the re.match method: `match = re.match(r'\bW+\b', 'hi there')`. In this case, the pattern matches the first word 'hi' in the string.

Other Common Patterns in Regular Expressions

There are many other common patterns that you can use in regular expressions. For example, the backslash D pattern allows you to match digits, while the backslash s pattern matches spaces. The period is a wild-card character that will match any letter or symbol. You can also add the plus and asterisk characters to make the pattern more greedy and grab repeats of single letters or whole patterns.

Using Character Classes in Regular Expressions

Character classes are used to create groups of characters that you want to match. You can do this by putting them inside square brackets, as shown in the following example: `[abcdef]`. This will match any of the characters 'a', 'b', 'c', 'd', or 'e'.

Using the Ari Module

In the following exercises, you will use the Ari module to perform some simple activities with regular expressions. These may include splitting a string on a pattern and finding all patterns in a string.

Conclusion

Regular expressions are a powerful tool for pattern matching and can be used to solve many problems in natural language processing. By learning how to use them effectively, you can become more proficient in your ability to process and analyze text data. Now it's your turn! Get started writing your first regular expression and see what you can accomplish.

"WEBVTTKind: captionsLanguage: enwelcome to the course in this video you'll be learning about regular expressions natural language processing is a massive field of study and actively use practice which aims to make sense of language using statistics and computers in this course you will learn some of the basics of NLP which will help you move from simple to more difficult and advanced topics even though this is the first course you will still get some exposure to the challenges of the field such as topic identification and text classification some interesting NLP areas you might have heard about our topic identification chat box text classification translation and sentiment analysis there are also many more you will learn the fundamentals of some of these topics as we move through the course regular expressions are strings that you can use that have a special syntax which allows you to match patterns and find other strings a pattern is a series of letters or symbols which can map to an actual text or words or punctuation you can use regular expressions to do things like find links in a webpage parse email addresses and remove unwanted strings or characters regular expressions are often referred to as reg X and can be used easily with Python via the re library here we have a simple import of the library we can match a substring by using the re match method which matches a pattern with a string it takes the pattern as the first argument the string is the second argument and returns a match object here we see it match exactly what we expected ABC we can also use special patterns that reg X understands like the backslash W Plus which will match a word we can see here via the match object representation that it is matched the first word it found hi there are hundreds of characters and patterns you can learn and memorize for the regular expressions but to get started I just wanted to share a few common patterns the first pattern backslash W we already saw it's used to match words the backslash D pattern allows us to match digits which can be useful if you need to find them separate them in a string the backslash s pattern matches spaces the period is a wild-card character the wild-card will match any letter or symbol the plus and asterisk characters allow things to become greedy grabbing repeats of single letters or whole patterns for example to match a full word rather than just one character we need to add the plus symbol after the backslash W using these character classes as capital letters and engage them so backslash capital S matches anything that is not a space you can also create a group of characters you want by putting them inside square brackets like our lowercase group in the following exercises you'll use the Ari module to perform some simple activities like splitting on a pattern or finding all patterns in a string in addition to split and find all search and match are also quite popular you saw a simple match in the beginning of this video and search is similar but it doesn't require you to match the pattern from the beginning of the string the syntax for the regex library is always to pass the pattern first and the string second depending on the method it may return an iterator a new string or a match object here we see the re-split method will take a pattern four spaces and a string with some spaces and return a list object with the results of splitting on spaces this can be used for tokenization so you can pre-process texts using regex while doing natural language processing now it's your turn get started writing your first regex and I'll see you back here soonwelcome to the course in this video you'll be learning about regular expressions natural language processing is a massive field of study and actively use practice which aims to make sense of language using statistics and computers in this course you will learn some of the basics of NLP which will help you move from simple to more difficult and advanced topics even though this is the first course you will still get some exposure to the challenges of the field such as topic identification and text classification some interesting NLP areas you might have heard about our topic identification chat box text classification translation and sentiment analysis there are also many more you will learn the fundamentals of some of these topics as we move through the course regular expressions are strings that you can use that have a special syntax which allows you to match patterns and find other strings a pattern is a series of letters or symbols which can map to an actual text or words or punctuation you can use regular expressions to do things like find links in a webpage parse email addresses and remove unwanted strings or characters regular expressions are often referred to as reg X and can be used easily with Python via the re library here we have a simple import of the library we can match a substring by using the re match method which matches a pattern with a string it takes the pattern as the first argument the string is the second argument and returns a match object here we see it match exactly what we expected ABC we can also use special patterns that reg X understands like the backslash W Plus which will match a word we can see here via the match object representation that it is matched the first word it found hi there are hundreds of characters and patterns you can learn and memorize for the regular expressions but to get started I just wanted to share a few common patterns the first pattern backslash W we already saw it's used to match words the backslash D pattern allows us to match digits which can be useful if you need to find them separate them in a string the backslash s pattern matches spaces the period is a wild-card character the wild-card will match any letter or symbol the plus and asterisk characters allow things to become greedy grabbing repeats of single letters or whole patterns for example to match a full word rather than just one character we need to add the plus symbol after the backslash W using these character classes as capital letters and engage them so backslash capital S matches anything that is not a space you can also create a group of characters you want by putting them inside square brackets like our lowercase group in the following exercises you'll use the Ari module to perform some simple activities like splitting on a pattern or finding all patterns in a string in addition to split and find all search and match are also quite popular you saw a simple match in the beginning of this video and search is similar but it doesn't require you to match the pattern from the beginning of the string the syntax for the regex library is always to pass the pattern first and the string second depending on the method it may return an iterator a new string or a match object here we see the re-split method will take a pattern four spaces and a string with some spaces and return a list object with the results of splitting on spaces this can be used for tokenization so you can pre-process texts using regex while doing natural language processing now it's your turn get started writing your first regex and I'll see you back here soon\n"