Machine Learning: Clustering & Retrieval
This case study explores the use of machine learning techniques such as clustering and retrieval to find similar documents. By applying these methods, readers can be provided with a list of related articles to the one they are interested in. ▼
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Course Feature
Cost:
Free
Provider:
Coursera
Certificate:
No Information
Language:
English
Start Date:
Self Paced
Course Overview
❗The content presented here is sourced directly from Coursera platform. For comprehensive course details, including enrollment information, simply click on the 'Go to class' link on our website.
Updated in [March 06th, 2023]
This course provides an introduction to Machine Learning, with a focus on clustering and retrieval. Students will learn how to create a document retrieval system based on k-nearest neighbours, and determine various text data similarity metrics. They will also learn how to use KD-trees to reduce computations in k-nearest neighbour search, and use locality sensitive hashing to compute approximate nearest neighbours.
The differences between supervised and unsupervised learning tasks will be discussed, and students will use k-means to group documents by topic. They will also learn how to use MapReduce to parallelize k-means, and consider probabilistic clustering approaches that make use of mixtures models.
Expectation maximisation will be used to fit a Gaussian mixture model (EM), and latent Dirichlet allocation will be used to perform mixed membership modelling (LDA). Students will also learn how to explain the steps of a Gibbs sampler and how to use the output to draw conclusions.
Finally, students will compare and contrast non-convex optimization initialization techniques, and use Python to implement these techniques. By the end of the course, students will have a comprehensive understanding of Machine Learning, and be able to apply these techniques to real-world problems.
[Applications]
The application of this course can be seen in various areas such as text mining, natural language processing, and information retrieval. It can be used to create a document retrieval system based on k-nearest neighbours, determine various text data similarity metrics, use KD-trees to reduce computations in k-nearest neighbour search, and use locality sensitive hashing to compute approximate nearest neighbours. Additionally, it can be used to group documents by topic using k-means, and use MapReduce to parallelize k-means. Furthermore, it can be used to perform mixed membership modelling using latent Dirichlet allocation, and use expectation maximisation to fit a Gaussian mixture model. Finally, it can be used to compare and contrast non-convex optimization initialization techniques, and Python should be used to implement these techniques.
[Career Paths]
1. Machine Learning Engineer: Machine Learning Engineers are responsible for developing and deploying machine learning models. They use a variety of techniques, such as supervised and unsupervised learning, to create models that can be used to make predictions and decisions. They also need to be able to optimize and tune models to ensure they are performing as expected. The development of AI and ML technologies is driving the demand for Machine Learning Engineers, and this trend is expected to continue in the future.
2. Data Scientist: Data Scientists are responsible for analyzing large datasets to uncover patterns and insights. They use a variety of techniques, such as clustering and retrieval, to uncover meaningful information from data. They also need to be able to interpret and communicate their findings to stakeholders. The increasing availability of data and the need for businesses to make data-driven decisions is driving the demand for Data Scientists, and this trend is expected to continue in the future.
3. AI/ML Researcher: AI/ML Researchers are responsible for researching and developing new algorithms and techniques for machine learning and artificial intelligence. They need to be able to identify and solve complex problems, and develop new algorithms and techniques to improve the performance of machine learning models. The increasing demand for AI and ML technologies is driving the demand for AI/ML Researchers, and this trend is expected to continue in the future.
4. Software Developer: Software Developers are responsible for developing software applications that use machine learning and artificial intelligence. They need to be able to design, develop, and maintain software applications that use machine learning and artificial intelligence. The increasing demand for AI and ML technologies is driving the demand for Software Developers, and this trend is expected to continue in the future.
[Education Paths]
1. Bachelor of Science in Computer Science: This degree path focuses on the fundamentals of computer science, including programming, algorithms, data structures, and software engineering. It also covers topics such as artificial intelligence, machine learning, and natural language processing. As the demand for data-driven solutions increases, this degree path is becoming increasingly popular.
2. Master of Science in Artificial Intelligence: This degree path focuses on the development of intelligent systems, including machine learning, natural language processing, and computer vision. It also covers topics such as robotics, game theory, and optimization. This degree path is becoming increasingly popular as the demand for AI-driven solutions increases.
3. Master of Science in Data Science: This degree path focuses on the analysis and interpretation of large datasets. It covers topics such as data mining, machine learning, and statistical analysis. As the demand for data-driven solutions increases, this degree path is becoming increasingly popular.
4. Doctor of Philosophy in Machine Learning: This degree path focuses on the development of advanced machine learning algorithms and techniques. It covers topics such as deep learning, reinforcement learning, and probabilistic graphical models. This degree path is becoming increasingly popular as the demand for AI-driven solutions increases.
Course Syllabus
Welcome and introduction to clustering and retrieval tasks
Course overview
Module-by-module topics covered
Assumed background
Pros & Cons
Interesting subject matter.
Excellent professors.
Useful tools.
Advance concepts and real life implementation.
Dependence on proprietary ML framework.
Misleading hints in assignments.
High amount of work required.
Difficult to receive assistance.
Cancelled syllabus.
Quiz questions not specific.
Course Provider
Provider Coursera's Stats at AZClass
Discussion and Reviews
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Quiz
Submitted Sucessfully
1. What is the main purpose of k-means?
2. What is the main purpose of latent Dirichlet allocation?
3. Which language should be used to implement these techniques?
4. Which of the following is a supervised learning task?
5. What is LDA?
Correct Answer: Latent Dirichlet Allocation.
6. What is the main purpose of k-means clustering?
7. Which of the following is an unsupervised learning task?
8. Which of the following is a probabilistic clustering approach?
9. What is the main purpose of using k-means?
Correct Answer: To group documents by topic.
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