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Dr. Nikunj Maheshwari

By the end of this project, you will learn how to create a professional looking wordcloud from a text dataset in Python. You will use an open source dataset containing Christmas recipes and will create a wordcloud of the most important ingredients used in these recipes. I will teach you how load a JSON dataset, clean the dataset by removing encodings and unwanted characters, and lemmatize your dataset. I will also teach you how to calculate TF-IDF weights of words in your dataset and use these weights to create a wordcloud. You will create a ready-to-use Jupyter notebook for creating a wordcloud on any text dataset.

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By the end of this project, you will learn how to create a professional looking wordcloud from a text dataset in Python. You will use an open source dataset containing Christmas recipes and will create a wordcloud of the most important ingredients used in these recipes. I will teach you how load a JSON dataset, clean the dataset by removing encodings and unwanted characters, and lemmatize your dataset. I will also teach you how to calculate TF-IDF weights of words in your dataset and use these weights to create a wordcloud. You will create a ready-to-use Jupyter notebook for creating a wordcloud on any text dataset.

Lemmatization is a process of removing inflectional endings only and to return the base or dictionary form of a word, which is known as the lemma. TF-IDF stands for term frequency-inverse document frequency. TF-IDF gives a weight to each word which tells how important that term is. Using both lemmatization and TF-IDF, one can find the important words in the text dataset and use these important words to create the wordcloud. For example, these datasets could be customer complaints and the business can focus on the important issues that the customers are facing. Wordcloud is a powerful resource which can be used in reports and presentations.

Note: This course works best for learners who are based in the North America region. We’re currently working on providing the same experience in other regions.

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Syllabus

Creating a Wordcloud using NLP and TF-IDF in Python
By the end of this project, you will learn how to create a professional looking wordcloud from a text dataset in Python. You will use an open source dataset containing Christmas recipes and will create a wordcloud of the most important ingredients used in these recipes. I will teach you how load a JSON dataset, clean the dataset by removing encodings and unwanted characters, and lemmatize your dataset. I will also teach you how to calculate TF-IDF weights of words in your dataset and use these weights to create a wordcloud. You will create a ready-to-use Jupyter notebook for creating a wordcloud on any text dataset. Lemmatization is a process of removing inflectional endings only and to return the base or dictionary form of a word, which is known as the lemma. TF-IDF stands for term frequency-inverse document frequency. TF-IDF gives a weight to each word which tells how important that term is. Using both lemmatization and TF-IDF, one can find the important words in the text dataset and use these important words to create the wordcloud. For example, these datasets could be customer complaints and the business can focus on the important issues that the customers are facing. Wordcloud is a powerful resource which can be used in reports and presentations.

Good to know

Know what's good
, what to watch for
, and possible dealbreakers
Teaches Python and NLP, which are essential for data scientists
Uses a real-world example with Christmas recipes, making it relatable and practical
Course materials are self-contained in a ready-to-use Jupyter notebook, simplifying the learning process
Suitable for beginners with no prior knowledge in NLP or Python
May require additional resources for learners with no prior programming experience
Although the course focuses on NLP in Python, it doesn't cover advanced NLP techniques

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Reviews summary

Adequately presented introduction

learners say this course does a good job at introducing NLP, TF-IDF in Python but have some critiques about the structure. While the explanations are clear, learners wish the video lectures were the traditional type as opposed to an online IDE environment.
The course provides clear explanations.
"Very good course with excellent progression and explanations!"
The video lectures are not traditional and use an online IDE environment.
"I did not like the environment used here vs. normal videos and CoLab environment like other courses"

Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in Creating a Wordcloud using NLP and TF-IDF in Python with these activities:
Organize Course Materials
Maintain a well-organized system for your course materials to enhance your learning experience.
Browse courses on Organization
Show steps
  • Create folders for different course modules and assignments.
  • Regularly file lecture notes, handouts, and assignments in the appropriate folders.
  • Color-code or label folders for easy identification.
Follow an Online Tutorial on Lemmatization
Supplement your knowledge of lemmatization by following an online tutorial.
Browse courses on Lemmatization
Show steps
  • Find a reputable online tutorial on lemmatization.
  • Follow the tutorial step-by-step.
  • Practice lemmatization on sample datasets.
Review 'Natural Language Processing with Python'
Strengthen your foundational understanding of NLP concepts by reviewing a comprehensive textbook.
Show steps
  • Read the selected chapters that align with the course curriculum.
  • Take notes and highlight key concepts.
  • Complete the exercises at the end of each chapter.
Four other activities
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Practice TF-IDF Calculations
Strengthen your understanding of TF-IDF by solving practice problems.
Browse courses on TF-IDF
Show steps
  • Calculate the term frequency for each word in a given document.
  • Calculate the inverse document frequency for each word across a set of documents.
  • Multiply the term frequency and inverse document frequency to obtain TF-IDF weights.
Create a Wordcloud using the provided dataset
Reinforce your understanding of Wordcloud creation and NLP techniques by applying them to a real-world dataset.
Browse courses on Python
Show steps
  • Load the Christmas recipes dataset into your Python environment.
  • Clean the dataset by removing encodings and unwanted characters.
  • Lemmatize the dataset to extract the base forms of words.
  • Calculate TF-IDF weights for each word.
  • Create a wordcloud using the TF-IDF weights.
Participate in an Online Wordcloud Creation Workshop
Immerse yourself in the practical aspects of Wordcloud creation by participating in an online workshop.
Browse courses on WordCloud
Show steps
  • Identify an online Wordcloud creation workshop.
  • Register for and attend the workshop.
  • Follow along with the instructor and complete the hands-on exercises.
Attend a Virtual NLP Meetup
Expand your network and learn from fellow NLP enthusiasts by attending a virtual meetup.
Browse courses on NLP
Show steps
  • Find a virtual NLP meetup that aligns with your interests.
  • Register for the meetup and attend.
  • Network with other participants and ask questions.

Career center

Learners who complete Creating a Wordcloud using NLP and TF-IDF in Python will develop knowledge and skills that may be useful to these careers:
Data Analyst
As a Data Analyst, you will use your skills in Python, NLP, and TF-IDF to analyze large datasets and extract meaningful insights. This course will provide you with the foundation you need to succeed in this role, as it will teach you how to create wordclouds, which are a powerful tool for visualizing data and identifying patterns. Additionally, you will learn how to clean and prepare data, which is an essential skill for any Data Analyst.
Machine Learning Engineer
Machine Learning Engineers use their knowledge of NLP and TF-IDF to develop and implement machine learning models that can extract insights from data. This course will provide you with the skills you need to succeed in this role, as it will teach you how to create wordclouds, which are a powerful tool for visualizing data and identifying patterns. Additionally, you will learn how to clean and prepare data, which is an essential skill for any Machine Learning Engineer.
Data Scientist
Data Scientists use their skills in NLP and TF-IDF to analyze data and extract meaningful insights. This course will provide you with the foundation you need to succeed in this role, as it will teach you how to create wordclouds, which are a powerful tool for visualizing data and identifying patterns. Additionally, you will learn how to clean and prepare data, which is an essential skill for any Data Scientist.
Natural Language Processing Engineer
Natural Language Processing Engineers use their skills in NLP and TF-IDF to develop and implement NLP models that can understand and generate human language. This course will provide you with the skills you need to succeed in this role, as it will teach you how to create wordclouds, which are a powerful tool for visualizing data and identifying patterns. Additionally, you will learn how to clean and prepare data, which is an essential skill for any Natural Language Processing Engineer.
Software Engineer
Software Engineers use their skills in NLP and TF-IDF to develop and implement software that can process and analyze text data. This course will provide you with the skills you need to succeed in this role, as it will teach you how to create wordclouds, which are a powerful tool for visualizing data and identifying patterns. Additionally, you will learn how to clean and prepare data, which is an essential skill for any Software Engineer.
Research Scientist
Research Scientists use their skills in NLP and TF-IDF to conduct research and develop new technologies. This course will provide you with the skills you need to succeed in this role, as it will teach you how to create wordclouds, which are a powerful tool for visualizing data and identifying patterns. Additionally, you will learn how to clean and prepare data, which is an essential skill for any Research Scientist.
Technical Writer
Technical Writers use their skills in NLP and TF-IDF to create technical documentation that is clear and easy to understand. This course will provide you with the skills you need to succeed in this role, as it will teach you how to create wordclouds, which are a powerful tool for visualizing data and identifying patterns. Additionally, you will learn how to clean and prepare data, which is an essential skill for any Technical Writer.
User Experience Researcher
User Experience Researchers use their skills in NLP and TF-IDF to conduct user research and improve the user experience of products and services. This course will provide you with the skills you need to succeed in this role, as it will teach you how to create wordclouds, which are a powerful tool for visualizing data and identifying patterns. Additionally, you will learn how to clean and prepare data, which is an essential skill for any User Experience Researcher.
Content Strategist
Content Strategists use their skills in NLP and TF-IDF to develop and implement content strategies that are effective and engaging. This course will provide you with the skills you need to succeed in this role, as it will teach you how to create wordclouds, which are a powerful tool for visualizing data and identifying patterns. Additionally, you will learn how to clean and prepare data, which is an essential skill for any Content Strategist.
Information Architect
Information Architects use their skills in NLP and TF-IDF to design and implement information systems that are effective and easy to use. This course will provide you with the skills you need to succeed in this role, as it will teach you how to create wordclouds, which are a powerful tool for visualizing data and identifying patterns. Additionally, you will learn how to clean and prepare data, which is an essential skill for any Information Architect.
Knowledge Manager
Knowledge Managers use their skills in NLP and TF-IDF to manage and share knowledge within an organization. This course will provide you with the skills you need to succeed in this role, as it will teach you how to create wordclouds, which are a powerful tool for visualizing data and identifying patterns. Additionally, you will learn how to clean and prepare data, which is an essential skill for any Knowledge Manager.
Librarian
Librarians use their skills in NLP and TF-IDF to organize and manage information resources. This course will provide you with the skills you need to succeed in this role, as it will teach you how to create wordclouds, which are a powerful tool for visualizing data and identifying patterns. Additionally, you will learn how to clean and prepare data, which is an essential skill for any Librarian.
Archivist
Archivists use their skills in NLP and TF-IDF to preserve and manage historical records. This course will provide you with the skills you need to succeed in this role, as it will teach you how to create wordclouds, which are a powerful tool for visualizing data and identifying patterns. Additionally, you will learn how to clean and prepare data, which is an essential skill for any Archivist.
Museum curator
Museum Curators use their skills in NLP and TF-IDF to manage and interpret museum collections. This course will provide you with the skills you need to succeed in this role, as it will teach you how to create wordclouds, which are a powerful tool for visualizing data and identifying patterns. Additionally, you will learn how to clean and prepare data, which is an essential skill for any Museum Curator.
Historian
Historians use their skills in NLP and TF-IDF to research and write about the past. This course may be useful for Historians, as it will provide them with the skills to create wordclouds, which are a powerful tool for visualizing data and identifying patterns.

Reading list

We've selected seven books that we think will supplement your learning. Use these to develop background knowledge, enrich your coursework, and gain a deeper understanding of the topics covered in Creating a Wordcloud using NLP and TF-IDF in Python.
Provides a comprehensive overview of natural language processing (NLP) techniques, including tokenization, stemming, lemmatization, and TF-IDF. It also covers more advanced topics such as machine learning and deep learning for NLP.
Provides a practical introduction to text mining using the R programming language. It covers a wide range of topics, including text preprocessing, feature extraction, and text classification.
Provides a comprehensive overview of deep learning for natural language processing. It covers a wide range of topics, including neural networks, attention mechanisms, and transformer models.
Provides a comprehensive overview of information retrieval techniques, including text preprocessing, indexing, and ranking. It also covers more advanced topics such as query expansion and relevance feedback.
Provides a comprehensive overview of speech and language processing, including topics such as speech recognition, speech synthesis, and natural language understanding. It valuable resource for anyone interested in learning more about how computers process and understand human language.
Provides a comprehensive overview of the Natural Language Toolkit (NLTK), a popular open-source NLP library for Python. It covers a wide range of topics, including text preprocessing, feature extraction, and text classification. It valuable resource for anyone who wants to learn more about NLP and how to use NLTK in practice.
Provides a practical introduction to natural language processing using the Python programming language. It covers a wide range of topics, including text preprocessing, feature extraction, and text classification. It valuable resource for anyone who wants to learn more about NLP and how to use it in practice.

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