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Language Classification with Naive Bayes in Python
Ari Anastassiou
Ari Anastassiou
In this 1-hour long project, you will learn how to clean and preprocess data for language classification. You will learn some theory behind Naive Bayes Modeling, and the impact that class imbalance of training data has on classification performance. You will learn how to use subword units to further mitigate the negative effects of class imbalance, and build an even better model.
Register for this course and see more details by visiting:
OpenCourser.com/course/un4d0s/language
What's inside
Syllabus
Language Classification with Naive Bayes in Python
In this 1-hour long project, you will learn how to design a model end-to-end that can classify sentences into one of Slovak, Czech, and English. During this process, you will implement relevant preprocessing steps, as well as address class imbalance in your training set by employing the learned theory of Naive Bayes Models, as well as implementing a more advanced technique: subword units.
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Teaches industry-standard tools and techniques for language classification, including Naive Bayes Modeling and subword units
Provides hands-on experience in data cleaning, preprocessing, and model building, making it ideal for those seeking practical skills
Taught by experienced instructors, Ari Anastassiou, ensuring the quality and relevance of the content
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Reviews summary
Good introduction to the naive bayes model
Learners say this course is well received and provides a good overview of the Naive Bayes model. Many are happy with the pace of the instruction and find the instructor is encouraging and engaging. However, some reviewers caution that there is no detailed explanation for the concepts behind the code and the course requires students to jump back and forth between projects. Additionally, some students have had difficulty using the rhyme virtual machine.
The instructor is encouraging and engaging.
"Excellent and well organized course. Instructor explained concepts clearly and encouraged us to attempt the steps before he showed his solution."
"Good instructor. "
"a great explanation from the instructor"
This course explains concepts clearly.
"Excellent and well organized course. Instructor explained concepts clearly."
"The teaching pace and topics covered are just right."
"Good Course.Well Explained"
This course requires students to jump back and forth between projects.
"it would be better if it has more explanation."
The instructor uses too many user defined functions which make the course difficult to follow.
"The course was good. I would have given a 5 star review but instructor used a lot of user defined functions which made it a little bit tough to follow."
The Rhyme virtual machine is difficult to use.
"the material and explanation was great, but i was not able to download the project file and also using the cloud virtual machine was not a very smooth experience."
"The project was good enough to understand the concepts of Naive Bayes that too in Python, but the Rhyme virtual machine was just not right place to learn on the go things cause it's seriously slow and video buffers too much."
The course doesn't explain concepts in detail.
"Very bad teaching style and didn't explain the meaning behind the code used."
"Also, didn't explain the concepts /related mathematics involved in naive Bayes."
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 Language Classification with Naive Bayes in Python with these
activities:
Review key machine learning concepts
Show steps
Review the fundamentals of machine learning to strengthen your understanding of the course material.
Browse courses on
Supervised Learning
Show steps
-
Revisit a textbook or online resources on machine learning
-
Solve practice problems on classification and regression algorithms
Engage in peer discussion forums
Show steps
Connect with fellow learners to exchange perspectives, clarify doubts, and expand your understanding of the course material through peer collaboration.
Show steps
-
Identify online forums or discussion groups related to the course topic
-
Participate actively in discussions, sharing your insights and asking questions
-
Provide constructive feedback and engage in thoughtful discussions with peers
Explore Naive Bayes models in Python
Show steps
Follow online tutorials to gain hands-on experience with the implementation of Naive Bayes models.
Show steps
-
Locate a tutorial on implementing Naive Bayes in Python
-
Follow the tutorial and replicate the code examples
-
Experiment with different parameters to observe their impact on model performance
Five other activities
Expand to see all activities and additional details
Show all eight activities
Analyze real-world language classification datasets
Show steps
Engage in exercises that involve manipulating and analyzing real-world datasets, solidifying your understanding of the course concepts.
Show steps
-
Find a publicly available language classification dataset
-
Load the dataset into your preferred programming environment
-
Preprocess and clean the data according to industry best practices
-
Build and evaluate different classification models using the dataset
Contribute to open-source language classification projects
Show steps
Engage in hands-on learning by contributing to real-world language classification projects on platforms like GitHub.
Show steps
-
Explore open-source repositories related to language classification
-
Identify an issue or feature that you can contribute to
-
Fork the repository, make your changes, and submit a pull request
Seek guidance from experts in the field
Show steps
Connect with professionals who specialize in language classification to gain valuable insights and expand your knowledge.
Show steps
-
Identify potential mentors through professional connections or online platforms
-
Reach out to potential mentors and express your interest in learning from them
-
Schedule regular meetings or video calls to engage in discussions and seek guidance
Design a language classification web application
Show steps
Challenge yourself by creating a project that integrates the course concepts into a practical application, deepening your comprehension of the material.
Show steps
-
Plan the architecture and design of your web application
-
Implement the language classification functionality using Python and a web framework
-
Deploy your application and test its performance
Participate in language classification challenges
Show steps
Test your skills and expand your knowledge by participating in competitions that focus on language classification.
Show steps
-
Identify relevant competitions or challenges related to language classification
-
Form a team or work individually to develop innovative solutions
-
Submit your solution and receive feedback from industry experts
Review key machine learning concepts
Show steps
Review the fundamentals of machine learning to strengthen your understanding of the course material.
Browse courses on
Supervised Learning
Show steps
Show steps
Show steps
- Revisit a textbook or online resources on machine learning
- Solve practice problems on classification and regression algorithms
Engage in peer discussion forums
Show steps
Connect with fellow learners to exchange perspectives, clarify doubts, and expand your understanding of the course material through peer collaboration.
Show steps
Show steps
Show steps
- Identify online forums or discussion groups related to the course topic
- Participate actively in discussions, sharing your insights and asking questions
- Provide constructive feedback and engage in thoughtful discussions with peers
Explore Naive Bayes models in Python
Show steps
Follow online tutorials to gain hands-on experience with the implementation of Naive Bayes models.
Show steps
Show steps
Show steps
- Locate a tutorial on implementing Naive Bayes in Python
- Follow the tutorial and replicate the code examples
- Experiment with different parameters to observe their impact on model performance
Five other activities
Expand to see all activities and additional details
Show all eight activities
Analyze real-world language classification datasets
Show steps
Engage in exercises that involve manipulating and analyzing real-world datasets, solidifying your understanding of the course concepts.
Show steps
Show steps
Show steps
- Find a publicly available language classification dataset
- Load the dataset into your preferred programming environment
- Preprocess and clean the data according to industry best practices
- Build and evaluate different classification models using the dataset
Contribute to open-source language classification projects
Show steps
Engage in hands-on learning by contributing to real-world language classification projects on platforms like GitHub.
Show steps
Show steps
Show steps
- Explore open-source repositories related to language classification
- Identify an issue or feature that you can contribute to
- Fork the repository, make your changes, and submit a pull request
Seek guidance from experts in the field
Show steps
Connect with professionals who specialize in language classification to gain valuable insights and expand your knowledge.
Show steps
Show steps
Show steps
- Identify potential mentors through professional connections or online platforms
- Reach out to potential mentors and express your interest in learning from them
- Schedule regular meetings or video calls to engage in discussions and seek guidance
Design a language classification web application
Show steps
Challenge yourself by creating a project that integrates the course concepts into a practical application, deepening your comprehension of the material.
Show steps
Show steps
Show steps
- Plan the architecture and design of your web application
- Implement the language classification functionality using Python and a web framework
- Deploy your application and test its performance
Participate in language classification challenges
Show steps
Test your skills and expand your knowledge by participating in competitions that focus on language classification.
Show steps
Show steps
Show steps
- Identify relevant competitions or challenges related to language classification
- Form a team or work individually to develop innovative solutions
- Submit your solution and receive feedback from industry experts
Career center
Learners who complete Language Classification with Naive Bayes in Python will develop knowledge and skills
that may be useful to these careers:
Natural Language Processing Engineer
Natural Language Processing Engineer
Natural Language Processing Engineers build and maintain systems that enable computers to understand and generate human language. This course can help you develop the skills needed to design and implement these systems, which are used in a variety of applications, such as machine translation, chatbots, and text summarization. The course covers topics such as data cleaning and preprocessing, Naive Bayes modeling, and subword units, which are all essential for building effective NLP systems.
Computational Linguist
Computational Linguist
Computational Linguists use computers to study language. They develop algorithms and models to understand how language is structured and used. This course can help you develop the skills needed to become a Computational Linguist, including data cleaning and preprocessing, Naive Bayes modeling, and subword units. These skills are essential for building effective NLP systems, which are used in a variety of applications, such as machine translation, chatbots, and text summarization.
Data Scientist
Data Scientist
Data Scientists use data to solve business problems. They collect, clean, and analyze data to identify trends and patterns. This course can help you develop the skills needed to become a Data Scientist, including data cleaning and preprocessing, Naive Bayes modeling, and subword units. These skills are essential for building effective NLP systems, which are used in a variety of business applications.
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers build and maintain machine learning models. These models are used to make predictions and decisions, such as recommending products to customers or detecting fraud. This course can help you develop the skills needed to become a Machine Learning Engineer, including data cleaning and preprocessing, Naive Bayes modeling, and subword units. These skills are essential for building effective machine learning models.
Business Analyst
Business Analyst
Business Analysts use data to solve business problems. They collect, clean, and analyze data to identify trends and patterns. This course can help you develop the skills needed to become a Business Analyst, including data cleaning and preprocessing, Naive Bayes modeling, and subword units. These skills are essential for building effective business analysis pipelines.
Software Engineer
Software Engineer
Software Engineers design, develop, and maintain software systems. This course can help you develop the skills needed to become a Software Engineer, including data cleaning and preprocessing, Naive Bayes modeling, and subword units. These skills are essential for building effective software systems.
Data Analyst
Data Analyst
Data Analysts collect, clean, and analyze data to identify trends and patterns. This course can help you develop the skills needed to become a Data Analyst, including data cleaning and preprocessing, Naive Bayes modeling, and subword units. These skills are essential for building effective data analysis pipelines.
Product Manager
Product Manager
Product Managers are responsible for the development and launch of new products. They work with engineers, designers, and marketers to bring products to market. This course can help you develop the skills needed to become a Product Manager, including data cleaning and preprocessing, Naive Bayes modeling, and subword units. These skills are essential for building effective product development pipelines.
Marketing Manager
Marketing Manager
Marketing Managers are responsible for developing and executing marketing campaigns. They work with product managers, engineers, and designers to bring products to market. This course can help you develop the skills needed to become a Marketing Manager, including data cleaning and preprocessing, Naive Bayes modeling, and subword units. These skills are essential for building effective marketing campaigns.
Sales Manager
Sales Manager
Sales Managers are responsible for leading sales teams and generating revenue. They work with customers to identify their needs and close deals. This course can help you develop the skills needed to become a Sales Manager, including data cleaning and preprocessing, Naive Bayes modeling, and subword units. These skills are essential for building effective sales pipelines.
Customer Success Manager
Customer Success Manager
Customer Success Managers are responsible for ensuring that customers are satisfied with their products and services. They work with customers to identify their needs and resolve any issues. This course can help you develop the skills needed to become a Customer Success Manager, including data cleaning and preprocessing, Naive Bayes modeling, and subword units. These skills are essential for building effective customer success pipelines.
Technical Writer
Technical Writer
Technical Writers create documentation for software and other technical products. They work with engineers and designers to explain how products work and how to use them. This course can help you develop the skills needed to become a Technical Writer, including data cleaning and preprocessing, Naive Bayes modeling, and subword units. These skills are essential for building effective technical documentation.
User Experience Designer
User Experience Designer
User Experience Designers create user interfaces for software and other products. They work with engineers and designers to make sure that products are easy to use and enjoyable. This course can help you develop the skills needed to become a User Experience Designer, including data cleaning and preprocessing, Naive Bayes modeling, and subword units. These skills are essential for building effective user interfaces.
Instructional Designer
Instructional Designer
Instructional Designers create learning materials for courses and other training programs. They work with subject matter experts to develop materials that are effective and engaging. This course can help you develop the skills needed to become an Instructional Designer, including data cleaning and preprocessing, Naive Bayes modeling, and subword units. These skills are essential for building effective learning materials.
Librarian
Librarian
Librarians help people find and use information. They work with patrons to identify their needs and find the resources they need. This course can help you develop the skills needed to become a Librarian, including data cleaning and preprocessing, Naive Bayes modeling, and subword units. These skills are essential for building effective library services.
For more career information including salaries, visit:
OpenCourser.com/course/un4d0s/language
Reading list
We've selected 15 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
Language Classification with Naive Bayes in Python.
Provides a comprehensive overview of natural language processing, covering topics such as text classification, language models, and machine translation. It valuable resource for anyone interested in learning more about the field of natural language processing.
Provides a practical introduction to machine learning, covering topics such as supervised learning, unsupervised learning, and deep learning. It valuable resource for anyone interested in learning more about the field of machine learning.
Provides a comprehensive overview of computational linguistics and natural language processing, covering topics such as syntax, semantics, and pragmatics. It valuable resource for anyone interested in learning more about the field of computational linguistics and natural language processing.
Provides a comprehensive overview of the mathematics used in machine learning, covering topics such as linear algebra, calculus, and probability theory. It valuable resource for anyone interested in learning more about the mathematical foundations of machine learning.
Provides a comprehensive overview of information theory, inference, and learning algorithms, covering topics such as Bayesian inference, decision theory, and reinforcement learning. It valuable resource for anyone interested in learning more about the theoretical foundations of machine learning.
Save
Provides a comprehensive overview of pattern recognition and machine learning, covering topics such as supervised learning, unsupervised learning, and reinforcement learning. It valuable resource for anyone interested in learning more about the field of pattern recognition and machine learning.
Provides a comprehensive overview of statistical learning, covering topics such as linear regression, logistic regression, and tree-based methods. It valuable resource for anyone interested in learning more about the field of statistical learning.
Provides a comprehensive overview of machine learning from a probabilistic perspective, covering topics such as Bayesian inference, decision theory, and reinforcement learning. It valuable resource for anyone interested in learning more about the theoretical foundations of machine learning.
Save
Provides a comprehensive overview of speech and language processing, covering topics such as speech recognition, natural language understanding, and dialogue systems. It valuable resource for anyone interested in learning more about the field of speech and language processing.
Provides a comprehensive overview of artificial intelligence, covering topics such as machine learning, natural language processing, and computer vision. It valuable resource for anyone interested in learning more about the field of artificial intelligence.
Provides a practical introduction to deep learning using Python libraries such as Keras and TensorFlow. It valuable resource for anyone interested in learning more about the field of deep learning.
Provides a practical introduction to machine learning using Python libraries such as Scikit-Learn, Keras, and TensorFlow. It valuable resource for anyone interested in learning more about the field of machine learning.
Provides a comprehensive overview of machine learning using Python libraries such as Scikit-Learn and TensorFlow. It valuable resource for anyone interested in learning more about the field of machine learning.
Save
Provides a comprehensive overview of deep learning, covering topics such as convolutional neural networks, recurrent neural networks, and generative adversarial networks. It valuable resource for anyone interested in learning more about the field of deep learning.
Provides a concise overview of machine learning, covering topics such as supervised learning, unsupervised learning, and deep learning. It valuable resource for anyone interested in learning more about the field of machine learning.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/un4d0s/language
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Effort
60-75 minutes
Level
Intermediate
Via
Coursera
Institution
Coursera Project Network
Instructor
Ari Anastassiou
Language
English
Good to know
Teaches industry-standard tools and techniques for language classification, including Naive Bayes Modeling and subword units
Provides hands-on experience in data cleaning, preprocessing, and model building, making it ideal for those seeking practical skills
Taught by experienced instructors, Ari Anastassiou, ensuring the quality and relevance of the content
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