Gain insights into simplifying machine learning with PyCaret. Delve into regression, classification, clustering, and anomaly detection, and learn to deploy applications using Streamlit.

Simplifying Machine Learning with PyCaret_ .png

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PyCaret is an low-code, open-source, machine learning library for Python. It can be used in machine learning tasks such as data preparation and model deployment.

In this course, you will learn multiple topics related to machine learning. You will start with a brief introduction to the basic concepts of machine learning, and then continue with case studies of regression, classification, clustering, and anomaly detection based on the respective modules of the PyCaret library. Finally, we will focus on using the Streamlit library to develop and deploy machine learning applications.

By the end of this course, you will have the skills to deploy the robust PyCaret library for any of your machine learning projects.

Simplifying Machine Learning with PyCaret in Python

Now we’ll initialize the PyCaret NLP environment and create the transformation pipeline by using the `setup()` function. The `target` parameter lets us specify the dataset’s text column, which will go through a number of preprocessing steps as described below. After this process is completed, the first 10 instances of the preprocessed dataset are printed.

# Initializing the NLP environment

nlp_ = nlp.setup(data = data, target='text', session_id = 6842)
data_ = nlp.get_config('data_')
data_.head(10)



import React from 'react';
import { shallow, configure } from 'enzyme';
import Adapter from 'enzyme-adapter-react-16';

configure({ adapter: new Adapter() });

import HelloWorld from './app';

var TestResult = function() {
    this.succeeded = false;
    this.reason = "";
    this.input = "";
    this.expected_output = "";
    this.actual_output = "";
}

export const executeTests = function() {

  var results = [];

  result = new TestResult();
  result.input = 'HelloWorld Component';
  result.expected_output = "span containing text 'Hello World'"

  let wrapper = shallow(<HelloWorld />);

  // Call your Challenge function here.

  let type = wrapper.type();
  let testSuccessful = true;
  let failureReason;

  if (type !== 'span') {
    testSuccessful = false;
    failureReason = "You need to render exactly one span HTML element";
  } else if (wrapper.props().children != "Hello World") {
     testSuccessful = false;
     failureReason = "You have rendered wrong message in your span element";
  }

  result.actual_output = wrapper.html();

  if (testSuccessful) {
    result.succeeded = true;
    result.reason = "Succeeded"
  } else {
    result.succeeded = false;
    result.reason = failureReason;
  }

  results.push(result);

  return results;
}


React

Dart

GoJS React

Typescript React

Vue.js


# Numeric and special character removal
Numbers and punctuation are not informative in the context of natural language processing, so PyCaret removes all numeric and special characters from the corpus. Those unnecessary characters are replaced with spaces by using regular expressions.

# Word tokenization
**Tokenization** is the process of splitting the corpus into tokens smaller units which are usually words. This is fundamental and typically one of the first steps in NLP because it must be applied before any further analysis.

# Stopword removal
**Stopwords** are words that are automatically removed from the text because they are very common and provide no useful information. Common stopwords include articles ("a," "an," "the," and so on), pronouns ("he," "she," "they," and so on), and prepositions ("to," "for," "in," and so on.

Furthermore, the given corpus may also include specific additional words that appear too often without being informative. We can deal with that problem by assigning a list of words to the `custom_stopwords` parameter of the `setup()` function. These words will subsequently be removed from the corpus.

# Bigram and trigram extraction

The $\bm{n}$**-grams** are sequences of $n$ consecutive tokens used in NLP for a variety of tasks, including spelling error correction, text prediction, text mining, statistical analysis, and others. The PyCaret NLP module extracts all the n-grams of the given corpus for $n = 2$ and $n = 3$. In those special cases, they are known as bigrams and trigrams respectively.

# Lemmatization
The main form of a word as it appears in the dictionary is known as a **lemma**. Usually, words are inflected to express different grammatical functions, such as tense and case. **Lemmatization** is the process of grouping together the inflected forms of a word, so they can be identified by its lemma.

Let’s familiarize ourselves with the NLP environment initialization.

Introduction to Machine Learning

Regression

Classification

Clustering

Anomaly Detection

Natural Language Processing

Deploying a Machine Learning Model

Conclusion

Appendix

Initializing the NLP Environment

Numeric and special character removal

Word tokenization