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Jaekwang KIM
In this course you will:
a) understand the naïve Bayesian algorithm.
b) understand the Support Vector Machine algorithm.
c) understand the Decision Tree algorithm.
d) understand the Clustering.
Please make sure that you’re comfortable programming in Python and have a basic knowledge of mathematics including matrix multiplications, and conditional probability.
Register for this course and see more details by visiting:
OpenCourser.com/course/f0dd5y/machine
What's inside
Syllabus
Naïve Bayes
Support Vector Machine
Decision Tree
Read more
Syllabus
Naïve Bayes
Support Vector Machine
Decision Tree
Read more
Clustering
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Taught by Jaekwang KIM, who are recognized for their expertise in machine learning
Explores highly relevant topics in machine learning, such as Naïve Bayes and Support Vector Machine algorithms
Prerequisites explicitly recommend comfort with both Python programming and basic mathematics, which prepares students for success
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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 Machine Learning Algorithms with these
activities:
Review decision tree basics
Show steps
Refresh your knowledge of decision tree fundamentals.
Browse courses on
Decision Tree
Show steps
-
Review course notes or materials on decision trees
-
Complete practice exercises or problems on decision trees
Read 'Machine Learning for Dummies'
Show steps
Gain foundational knowledge of machine learning concepts.
View
Data Analytics & Visualization All-in-One For...
on Amazon
Show steps
-
Read the book and take notes on key concepts
-
Complete the practice exercises in the book
Practice naïve Bayes classification
Show steps
Enhance your understanding of naïve Bayes by completing practice problems.
Browse courses on
Naïve Bayes
Show steps
-
Find online practice exercises on naïve Bayes
-
Solve the practice problems and check your answers
-
Identify areas where you need additional practice
Nine other activities
Expand to see all activities and additional details
Show all 12 activities
Review support vector machine tutorials
Show steps
Solidify your understanding of support vector machines by following tutorials.
Browse courses on
Support Vector Machine
Show steps
-
Find video tutorials on support vector machines
-
Follow the video tutorial and take notes on key concepts
-
Practice implementing the support vector machine algorithm
Attend a workshop on Clustering techniques for data analysis
Show steps
Provides hands-on experience with Clustering techniques, enhancing practical skills and knowledge.
Browse courses on
Clustering
Show steps
-
Research and identify a workshop on Clustering techniques.
-
Register and attend the workshop.
-
Actively participate in the workshop exercises and discussions.
Join a machine learning study group
Show steps
Engage with peers and enhance your understanding of machine learning.
Browse courses on
Machine Learning
Show steps
-
Find a study group or create your own
-
Meet regularly with the group to discuss course material
-
Work together on projects and assignments
Practice Naïve Bayes algorithm with sample datasets
Show steps
Reinforces the theoretical concepts of Naïve Bayes by applying it to practical scenarios.
Browse courses on
Naïve Bayes
Show steps
-
Gather sample datasets related to the course content.
-
Implement the Naïve Bayes algorithm to classify the data in the datasets.
-
Analyze the results and compare them with expected outcomes.
Create a visual representation of a Decision Tree algorithm
Show steps
Improves comprehension of Decision Tree algorithm by visualizing its structure and decision-making process.
Browse courses on
Decision Tree
Show steps
-
Choose a Decision Tree algorithm to represent visually.
-
Use a flowchart or diagram to illustrate the algorithm's steps and decision points.
-
Explain the algorithm's functionality and how it makes predictions.
Implement Support Vector Machine algorithm on real-world datasets
Show steps
Enhances practical understanding of Support Vector Machine algorithm by leveraging real-world data.
Browse courses on
Support Vector Machine
Show steps
-
Collect real-world datasets relevant to the course material.
-
Preprocess and clean the data to make it suitable for analysis.
-
Train and evaluate a Support Vector Machine model using the prepared data.
Attend a data mining workshop
Show steps
Develop your skills in data mining techniques.
Browse courses on
Data Mining
Show steps
-
Find a data mining workshop in your area
-
Register for the workshop
-
Attend the workshop and actively participate
Develop a project that applies Naïve Bayes to a real-world problem
Show steps
Encourages deep engagement with Naïve Bayes by applying it to a practical problem.
Browse courses on
Naïve Bayes
Show steps
-
Identify a real-world problem that can be addressed using Naïve Bayes.
-
Gather and preprocess the necessary data.
-
Implement a Naïve Bayes model and train it on the data.
-
Evaluate the performance of the model and make recommendations for improvement.
Build a clustering model
Show steps
Apply your knowledge of clustering by building a model to solve a real-world problem.
Browse courses on
Clustering
Show steps
-
Identify a dataset that you can use for clustering
-
Select a clustering algorithm and implement it
-
Evaluate the performance of your model
-
Write a report on your findings
Review decision tree basics
Show steps
Refresh your knowledge of decision tree fundamentals.
Browse courses on
Decision Tree
Show steps
Show steps
Show steps
- Review course notes or materials on decision trees
- Complete practice exercises or problems on decision trees
Read 'Machine Learning for Dummies'
Show steps
Gain foundational knowledge of machine learning concepts.
View
Data Analytics & Visualization All-in-One For...
on Amazon
Show steps
Show steps
Show steps
- Read the book and take notes on key concepts
- Complete the practice exercises in the book
Practice naïve Bayes classification
Show steps
Enhance your understanding of naïve Bayes by completing practice problems.
Browse courses on
Naïve Bayes
Show steps
Show steps
Show steps
- Find online practice exercises on naïve Bayes
- Solve the practice problems and check your answers
- Identify areas where you need additional practice
Nine other activities
Expand to see all activities and additional details
Show all 12 activities
Review support vector machine tutorials
Show steps
Solidify your understanding of support vector machines by following tutorials.
Browse courses on
Support Vector Machine
Show steps
Show steps
Show steps
- Find video tutorials on support vector machines
- Follow the video tutorial and take notes on key concepts
- Practice implementing the support vector machine algorithm
Attend a workshop on Clustering techniques for data analysis
Show steps
Provides hands-on experience with Clustering techniques, enhancing practical skills and knowledge.
Browse courses on
Clustering
Show steps
Show steps
Show steps
- Research and identify a workshop on Clustering techniques.
- Register and attend the workshop.
- Actively participate in the workshop exercises and discussions.
Join a machine learning study group
Show steps
Engage with peers and enhance your understanding of machine learning.
Browse courses on
Machine Learning
Show steps
Show steps
Show steps
- Find a study group or create your own
- Meet regularly with the group to discuss course material
- Work together on projects and assignments
Practice Naïve Bayes algorithm with sample datasets
Show steps
Reinforces the theoretical concepts of Naïve Bayes by applying it to practical scenarios.
Browse courses on
Naïve Bayes
Show steps
Show steps
Show steps
- Gather sample datasets related to the course content.
- Implement the Naïve Bayes algorithm to classify the data in the datasets.
- Analyze the results and compare them with expected outcomes.
Create a visual representation of a Decision Tree algorithm
Show steps
Improves comprehension of Decision Tree algorithm by visualizing its structure and decision-making process.
Browse courses on
Decision Tree
Show steps
Show steps
Show steps
- Choose a Decision Tree algorithm to represent visually.
- Use a flowchart or diagram to illustrate the algorithm's steps and decision points.
- Explain the algorithm's functionality and how it makes predictions.
Implement Support Vector Machine algorithm on real-world datasets
Show steps
Enhances practical understanding of Support Vector Machine algorithm by leveraging real-world data.
Browse courses on
Support Vector Machine
Show steps
Show steps
Show steps
- Collect real-world datasets relevant to the course material.
- Preprocess and clean the data to make it suitable for analysis.
- Train and evaluate a Support Vector Machine model using the prepared data.
Attend a data mining workshop
Show steps
Develop your skills in data mining techniques.
Browse courses on
Data Mining
Show steps
Show steps
Show steps
- Find a data mining workshop in your area
- Register for the workshop
- Attend the workshop and actively participate
Develop a project that applies Naïve Bayes to a real-world problem
Show steps
Encourages deep engagement with Naïve Bayes by applying it to a practical problem.
Browse courses on
Naïve Bayes
Show steps
Show steps
Show steps
- Identify a real-world problem that can be addressed using Naïve Bayes.
- Gather and preprocess the necessary data.
- Implement a Naïve Bayes model and train it on the data.
- Evaluate the performance of the model and make recommendations for improvement.
Build a clustering model
Show steps
Apply your knowledge of clustering by building a model to solve a real-world problem.
Browse courses on
Clustering
Show steps
Show steps
Show steps
- Identify a dataset that you can use for clustering
- Select a clustering algorithm and implement it
- Evaluate the performance of your model
- Write a report on your findings
Career center
Learners who complete Machine Learning Algorithms will develop knowledge and skills
that may be useful to these careers:
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers design, develop, and deploy machine learning models. This course provides a comprehensive understanding of machine learning algorithms, enabling Machine Learning Engineers to select the most appropriate algorithms for specific tasks. The course covers topics such as Naive Bayes, Support Vector Machines, Decision Trees, and Clustering, which are widely used in machine learning applications.
Data Scientist
Data Scientist
Data Scientists use various techniques, including machine learning algorithms, to analyze data and extract meaningful insights. This course provides a strong foundation in machine learning algorithms, including Naive Bayes, Support Vector Machines, Decision Trees, and Clustering. These algorithms are essential for data scientists to build predictive models and gain valuable insights from data.
Data Analyst
Data Analyst
Data Analysts use machine learning algorithms to identify patterns and trends in data. This course provides a solid foundation in machine learning algorithms, including Naive Bayes, Support Vector Machines, Decision Trees, and Clustering. These algorithms help Data Analysts uncover hidden insights and make data-driven decisions.
Software Engineer
Software Engineer
Software Engineers specializing in machine learning develop and implement machine learning solutions. This course provides a strong foundation in machine learning algorithms, including Naive Bayes, Support Vector Machines, Decision Trees, and Clustering. These algorithms are essential for Software Engineers to build robust and efficient machine learning systems.
Business Analyst
Business Analyst
Business Analysts use machine learning algorithms to gain insights into business data. This course provides a comprehensive understanding of machine learning algorithms, including Naive Bayes, Support Vector Machines, Decision Trees, and Clustering. These algorithms help Business Analysts make informed decisions and drive business growth.
Quantitative Analyst
Quantitative Analyst
Quantitative Analysts use machine learning algorithms to analyze financial data and make predictions. This course provides a strong foundation in machine learning algorithms, including Naive Bayes, Support Vector Machines, Decision Trees, and Clustering. These algorithms are essential for Quantitative Analysts to develop accurate and reliable financial models.
Actuary
Actuary
Actuaries use machine learning algorithms to assess risk and uncertainty. This course provides a comprehensive understanding of machine learning algorithms, including Naive Bayes, Support Vector Machines, Decision Trees, and Clustering. These algorithms help Actuaries develop accurate and reliable risk models.
Statistician
Statistician
Statisticians use machine learning algorithms to analyze and interpret data. This course provides a strong foundation in machine learning algorithms, including Naive Bayes, Support Vector Machines, Decision Trees, and Clustering. These algorithms are essential for Statisticians to develop accurate and reliable statistical models.
Operations Research Analyst
Operations Research Analyst
Operations Research Analysts use machine learning algorithms to optimize decision-making. This course provides a comprehensive understanding of machine learning algorithms, including Naive Bayes, Support Vector Machines, Decision Trees, and Clustering. These algorithms help Operations Research Analysts develop effective and efficient solutions to complex problems.
Risk Manager
Risk Manager
Risk Managers use machine learning algorithms to assess and manage risk. This course provides a comprehensive understanding of machine learning algorithms, including Naive Bayes, Support Vector Machines, Decision Trees, and Clustering. These algorithms help Risk Managers develop accurate and reliable risk models.
Financial Analyst
Financial Analyst
Financial Analysts use machine learning algorithms to analyze financial data and make recommendations. This course provides a comprehensive understanding of machine learning algorithms, including Naive Bayes, Support Vector Machines, Decision Trees, and Clustering. These algorithms help Financial Analysts make informed decisions and drive financial growth.
Data Engineer
Data Engineer
Data Engineers design and build data pipelines that support machine learning algorithms. This course provides a comprehensive understanding of machine learning algorithms, including Naive Bayes, Support Vector Machines, Decision Trees, and Clustering. This knowledge helps Data Engineers develop efficient and scalable data pipelines that meet the demands of machine learning applications.
Database Administrator
Database Administrator
Database Administrators manage and maintain databases that store machine learning data and models. This course provides a comprehensive understanding of machine learning algorithms, including Naive Bayes, Support Vector Machines, Decision Trees, and Clustering. This knowledge helps Database Administrators optimize database performance and ensure data integrity for machine learning applications.
Information Security Analyst
Information Security Analyst
Information Security Analysts use machine learning algorithms to detect and prevent cyber threats. This course provides a comprehensive understanding of machine learning algorithms, including Naive Bayes, Support Vector Machines, Decision Trees, and Clustering. This knowledge helps Information Security Analysts develop effective security solutions and protect organizations from cyberattacks.
User Experience Designer
User Experience Designer
User Experience Designers use machine learning algorithms to improve the user experience of digital products. This course provides a comprehensive understanding of machine learning algorithms, including Naive Bayes, Support Vector Machines, Decision Trees, and Clustering. This knowledge helps User Experience Designers develop user-centric designs that meet the needs of users.
For more career information including salaries, visit:
OpenCourser.com/course/f0dd5y/machine
Reading list
We've selected 12 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
Machine Learning Algorithms.
Save
This comprehensive textbook covers the fundamentals of deep learning, including neural networks, convolutional neural networks, and recurrent neural networks. It provides a solid foundation for understanding and applying deep learning techniques.
This classic textbook provides a thorough treatment of statistical pattern recognition and machine learning. It covers advanced topics such as Bayesian methods, neural networks, and support vector machines.
Save
This algorithmic perspective textbook provides a comprehensive overview of machine learning. It covers a wide range of topics, including supervised learning, unsupervised learning, and reinforcement learning.
This advanced textbook provides a probabilistic approach to machine learning. It covers topics such as graphical models, Bayesian inference, and reinforcement learning.
This comprehensive guide provides a practical overview of deep learning using Python. It includes code examples, exercises, and real-world projects to help you apply deep learning techniques.
This advanced textbook provides a comprehensive overview of advanced analytics using Spark. It covers topics such as data preprocessing, feature engineering, model selection, and model evaluation.
Save
This comprehensive textbook covers a wide range of topics in machine learning, including supervised learning, unsupervised learning, and reinforcement learning. It provides a balanced mix of theory and practical applications.
This practical guide provides a hands-on approach to machine learning using Python. It includes code examples, exercises, and real-world projects to help you apply machine learning techniques.
This comprehensive guide provides a practical overview of machine learning using Python libraries such as Scikit-Learn, Keras, and TensorFlow. It includes code examples, exercises, and real-world projects.
This practical guide provides a comprehensive overview of machine learning using R. It includes code examples, exercises, and real-world projects to help you apply machine learning techniques using R.
This rigorous textbook provides a theoretical foundation for machine learning. It covers topics such as statistical learning theory, optimization, and generalization.
This comprehensive textbook covers the fundamental concepts, techniques, and applications of machine learning. It provides a solid theoretical foundation and practical insights.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/f0dd5y/machine
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Via
Coursera
Institution
Sungkyunkwan University
Instructor
Jaekwang KIM
Language
English
Good to know
Taught by Jaekwang KIM, who are recognized for their expertise in machine learning
Explores highly relevant topics in machine learning, such as Naïve Bayes and Support Vector Machine algorithms
Prerequisites explicitly recommend comfort with both Python programming and basic mathematics, which prepares students for success
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