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Di Wu
The "Classification Analysis" course provides you with a comprehensive understanding of one of the fundamental supervised learning methods, classification. You will explore various classifiers, including KNN, decision tree, support vector machine, naive bayes, and logistic regression, and learn how to evaluate their performance. Through tutorials and engaging case studies, you will gain hands-on experience and practice in applying classification techniques to real-world data analysis tasks.
Read more
The "Classification Analysis" course provides you with a comprehensive understanding of one of the fundamental supervised learning methods, classification. You will explore various classifiers, including KNN, decision tree, support vector machine, naive bayes, and logistic regression, and learn how to evaluate their performance. Through tutorials and engaging case studies, you will gain hands-on experience and practice in applying classification techniques to real-world data analysis tasks.
Read more
The "Classification Analysis" course provides you with a comprehensive understanding of one of the fundamental supervised learning methods, classification. You will explore various classifiers, including KNN, decision tree, support vector machine, naive bayes, and logistic regression, and learn how to evaluate their performance. Through tutorials and engaging case studies, you will gain hands-on experience and practice in applying classification techniques to real-world data analysis tasks.
By the end of this course, you will be able to:
1. Understand the concept and significance of classification as a supervised learning method.
2. Identify and describe different classifiers, such as KNN, decision tree, support vector machine, naive bayes, and logistic regression.
3. Apply each classifier to perform binary and multiclass classification tasks on diverse datasets.
4. Evaluate the performance of classifiers using appropriate metrics, including accuracy, precision, recall, F1 score, and ROC curves.
5. Select and fine-tune classifiers based on dataset characteristics and learning requirements.
Gain practical experience in solving classification problems through guided tutorials and case studies.
Register for this course and see more details by visiting:
OpenCourser.com/course/y6nuor/classification
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What's inside
Syllabus
Introduction to Classification
This week provides an overview of classification as a supervised learning method. You will also learn the K-Nearest Neighbors (KNN) algorithm, understanding its principles and applications in classification tasks.
Read more
Syllabus
Introduction to Classification
This week provides an overview of classification as a supervised learning method. You will also learn the K-Nearest Neighbors (KNN) algorithm, understanding its principles and applications in classification tasks.
Read more
Decision Tree Classification
This week you will explore the Decision Tree algorithm, learning its structure, construction, and applications in classification problems.
Support Vector Machine Classification
This week focuses on the Support Vector Machine (SVM) algorithm, where you will grasp its principles and how it is used for classification.
Naïve Bayes and Logistic Regression
This week will delve into two essential classifiers: Naive Bayes and Logistic Regression. You will gain insights into their assumptions, strengths, and applications.
Classification Evaluation
This week you will learn how to evaluate the performance of classifiers using various metrics and visualization techniques.
Case Study
In this final week, you will apply the knowledge and techniques learned throughout the course to solve a real-world classification problem through a comprehensive case study.
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Examines classification, a crucial aspect of supervised learning methods
Introduces essential classifiers like KNN, decision tree, SVM, Naïve Bayes, and logistic regression
Provides hands-on experience with tutorials and case studies, fostering practical skills
Teaches model evaluation techniques with metrics like accuracy, precision, recall, F1 score, and ROC curves
Develops the ability to select and fine-tune classifiers based on data and learning requirements
Covers a comprehensive range of classification algorithms, offering a thorough foundation
Save this course
Save Classification Analysis to your list so you can find it easily later:
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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 Classification Analysis with these
activities:
Review Probability and Linear Algebra concepts
Show steps
Boost your understanding of probability and linear algebra, which are essential mathematical concepts for classification.
Browse courses on
Probability
Show steps
-
Review textbooks or lecture notes on probability and linear algebra.
-
Work through practice problems and exercises to reinforce your understanding.
Explore tutorials on Decision Trees
Show steps
Deepen your understanding of decision trees and their applications in classification.
Browse courses on
Decision Trees
Show steps
-
Identify online tutorials or workshops on decision trees.
-
Follow the tutorials and complete the exercises to practice building and evaluating decision trees.
Solve classification problems using KNN
Show steps
Strengthen your skills in applying KNN for classification tasks.
Show steps
-
Find datasets with classification tasks.
-
Implement the KNN algorithm or use a library to solve the classification problems.
-
Optimize the KNN parameters and evaluate the models' performance.
Two other activities
Expand to see all activities and additional details
Show all five activities
Develop a presentation on Naive Bayes and Logistic Regression
Show steps
Solidify your understanding of Naive Bayes and Logistic Regression by creating a presentation.
Browse courses on
Naive Bayes
Show steps
-
Gather information and resources on Naive Bayes and Logistic Regression.
-
Organize and structure the content into a coherent presentation.
-
Design slides with clear visuals and explanations.
-
Practice delivering the presentation to reinforce your knowledge.
Read and review 'Pattern Recognition and Machine Learning' by Christopher Bishop
Show steps
Expand your knowledge of classification methods through a comprehensive book by an expert in the field.
View
Pattern Recognition and Machine Learning...
on Amazon
Show steps
-
Read the chapters relevant to classification methods.
-
Take notes and highlight key concepts.
-
Solve the exercises and review the solutions.
Review Probability and Linear Algebra concepts
Show steps
Boost your understanding of probability and linear algebra, which are essential mathematical concepts for classification.
Browse courses on
Probability
Show steps
Show steps
Show steps
- Review textbooks or lecture notes on probability and linear algebra.
- Work through practice problems and exercises to reinforce your understanding.
Explore tutorials on Decision Trees
Show steps
Deepen your understanding of decision trees and their applications in classification.
Browse courses on
Decision Trees
Show steps
Show steps
Show steps
- Identify online tutorials or workshops on decision trees.
- Follow the tutorials and complete the exercises to practice building and evaluating decision trees.
Solve classification problems using KNN
Show steps
Strengthen your skills in applying KNN for classification tasks.
Show steps
Show steps
Show steps
- Find datasets with classification tasks.
- Implement the KNN algorithm or use a library to solve the classification problems.
- Optimize the KNN parameters and evaluate the models' performance.
Two other activities
Expand to see all activities and additional details
Show all five activities
Develop a presentation on Naive Bayes and Logistic Regression
Show steps
Solidify your understanding of Naive Bayes and Logistic Regression by creating a presentation.
Browse courses on
Naive Bayes
Show steps
Show steps
Show steps
- Gather information and resources on Naive Bayes and Logistic Regression.
- Organize and structure the content into a coherent presentation.
- Design slides with clear visuals and explanations.
- Practice delivering the presentation to reinforce your knowledge.
Read and review 'Pattern Recognition and Machine Learning' by Christopher Bishop
Show steps
Expand your knowledge of classification methods through a comprehensive book by an expert in the field.
View
Pattern Recognition and Machine Learning...
on Amazon
Show steps
Show steps
Show steps
- Read the chapters relevant to classification methods.
- Take notes and highlight key concepts.
- Solve the exercises and review the solutions.
Career center
Learners who complete Classification Analysis will develop knowledge and skills
that may be useful to these careers:
Data Scientist
Data Scientist
Data Scientists use data to solve problems and make predictions. They work in a variety of industries, from finance to healthcare. The course will help you develop the skills you need to succeed as a Data Scientist. You will learn how to collect and clean data, analyze data, and build machine learning models. The course also covers important topics such as data visualization and communication.
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers build, deploy, and maintain machine learning models. These models power a wide range of applications, from self-driving cars to fraud detection systems. The course will help you build a strong foundation in machine learning, which is essential for success in this role. You will learn how to collect and prepare data, train and evaluate models, and deploy them into production. The course also covers important topics such as feature engineering and model optimization.
Software Engineer
Software Engineer
Software Engineers design, develop, and maintain software systems. They work in a variety of industries, from finance to healthcare. The course will help you build a strong foundation in software engineering, which is essential for success in this role. You will learn how to design and implement software systems, and how to test and debug them. The course also covers important topics such as software design patterns and agile development.
Quantitative Analyst
Quantitative Analyst
Quantitative Analysts use mathematical and statistical models to analyze financial data. They work in a variety of roles, from portfolio management to risk assessment. The course will help you build a strong foundation in quantitative analysis, which is essential for success in this role. You will learn how to build and evaluate financial models, and how to use them to make investment decisions. The course also covers important topics such as econometrics and financial data analysis.
Financial Analyst
Financial Analyst
Financial Analysts provide financial advice to individuals and businesses. They work in a variety of roles, from investment banking to corporate finance. The course will help you build a strong foundation in financial analysis, which is essential for success in this role. You will learn how to analyze financial data, and how to make investment recommendations. The course also covers important topics such as financial modeling and valuation.
Market Researcher
Market Researcher
Market Researchers collect and analyze data to understand consumer behavior. They work in a variety of industries, from retail to healthcare. The course will help you build a strong foundation in market research, which is essential for success in this role. You will learn how to collect and analyze data, and how to interpret your findings. The course also covers important topics such as survey design and data analysis.
Business Analyst
Business Analyst
Business Analysts help businesses improve their performance by identifying and solving problems. They work in a variety of industries, from finance to healthcare. The course will help you build a strong foundation in business analysis, which is essential for success in this role. You will learn how to identify and solve problems, and how to communicate your findings to stakeholders. The course also covers important topics such as business process modeling and data analysis.
Statistician
Statistician
Statisticians collect, analyze, and interpret data. They work in a variety of fields, from public health to market research. The course will help you build a strong foundation in statistics, which is essential for success in this role. You will learn how to collect and analyze data, and how to interpret your findings. The course also covers important topics such as probability and statistical modeling.
Data Analyst
Data Analyst
Data Analysts collect, clean, and analyze data to help businesses make better decisions. They work in a variety of industries, from retail to healthcare. The course will help you build a strong foundation in data analysis, which is essential for success in this role. You will learn how to collect and clean data, analyze data, and communicate your findings. The course also covers important topics such as data visualization and data mining.
Actuary
Actuary
Actuaries use mathematical and statistical models to assess risk. They work in a variety of industries, from insurance to healthcare. The course will help you build a strong foundation in actuarial science, which is essential for success in this role. You will learn how to build and evaluate financial models, and how to use them to make risk assessments. The course also covers important topics such as probability and statistical modeling.
Risk Manager
Risk Manager
Risk Managers identify and assess risks to organizations. They work in a variety of industries, from finance to healthcare. The course will help you build a strong foundation in risk management, which is essential for success in this role. You will learn how to identify and assess risks, and how to develop and implement risk management strategies. The course also covers important topics such as risk modeling and insurance.
Epidemiologist
Epidemiologist
Epidemiologists investigate the causes of disease and injury. They work in a variety of settings, from public health departments to research institutions. The course will help you build a strong foundation in epidemiology, which is essential for success in this role. You will learn how to collect and analyze data, and how to interpret your findings. The course also covers important topics such as biostatistics and environmental health.
Biostatistician
Biostatistician
Biostatisticians use statistical methods to analyze biological data. They work in a variety of settings, from pharmaceutical companies to research institutions. The course will help you build a strong foundation in biostatistics, which is essential for success in this role. You will learn how to collect and analyze data, and how to interpret your findings. The course also covers important topics such as clinical trials and genetic analysis.
Operations Research Analyst
Operations Research Analyst
Operations Research Analysts use mathematical and statistical models to improve the efficiency of organizations. They work in a variety of industries, from manufacturing to transportation. The course will help you build a strong foundation in operations research, which is essential for success in this role. You will learn how to build and evaluate mathematical models, and how to use them to solve real-world problems. The course also covers important topics such as linear programming and simulation.
Data Engineer
Data Engineer
Data Engineers build and maintain the infrastructure that stores and processes data. They work in a variety of industries, from technology to finance. The course will help you build a strong foundation in data engineering, which is essential for success in this role. You will learn how to design and implement data pipelines, and how to manage and maintain data warehouses and data lakes. The course also covers important topics such as cloud computing and big data technologies.
For more career information including salaries, visit:
OpenCourser.com/course/y6nuor/classification
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
Classification Analysis.
This comprehensive textbook provides a thorough treatment of support vector machines, a powerful classification algorithm. It covers the mathematical foundations, algorithms, and applications of SVMs.
This advanced textbook provides a comprehensive treatment of statistical learning methods, including classification algorithms such as KNN, decision trees, and SVM. It offers a rigorous mathematical foundation and in-depth analysis of these algorithms.
This comprehensive textbook provides a theoretical foundation for machine learning, including classification methods such as KNN, decision trees, SVMs, and Naive Bayes. It offers a thorough understanding of the principles and algorithms used in classification analysis.
This textbook provides a comprehensive treatment of logistic regression, a widely-used classification algorithm. It covers the theoretical foundations, algorithms, and applications of logistic regression.
This widely-used textbook covers a wide range of machine learning topics, including classification methods such as KNN, decision trees, SVM, and Naive Bayes. It provides a practical and accessible introduction to these algorithms and their applications.
Save
This classic textbook provides a comprehensive overview of machine learning concepts and algorithms, including classification methods such as KNN, decision trees, and SVM. It offers a solid foundation for understanding the principles and applications of classification analysis.
Save
This widely-used textbook covers data mining techniques, including classification methods such as KNN, decision trees, and Naive Bayes. It offers a practical approach to data analysis and provides hands-on examples and case studies.
Save
This research paper provides a detailed overview of Naive Bayes classifiers, a simple yet powerful classification method. It covers the theoretical foundations, algorithms, and applications of Naive Bayes.
This textbook provides a unique perspective on machine learning, emphasizing reinforcement learning. It covers fundamental concepts and algorithms, including classification methods such as KNN and decision trees.
Save
This comprehensive textbook covers deep learning techniques, including convolutional neural networks and recurrent neural networks. While it does not explicitly cover traditional classification methods such as KNN, it provides valuable insights into modern approaches to classification.
This online book provides a gentle introduction to machine learning using Python. It covers a wide range of topics, including classification methods such as KNN, decision trees, and SVM. It good resource for beginners who want to get started with practical machine learning.
This easy-to-read book provides a non-technical introduction to machine learning. It covers a wide range of topics, including classification methods such as KNN, decision trees, and SVM. It good resource for beginners who want to understand the basics of machine learning without getting too technical.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/y6nuor/classification
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Effort
5-7 hours/week
Level
Intermediate
Via
Coursera
Institution
University of Colorado Boulder
Instructor
Di Wu
Language
English
This course belongs to a
collection
called
Data Analysis with Python. It's comprised of five courses:
- Association Rules Analysis
- Data Analysis with Python Project
- Classification Analysis (viewing)
- Clustering Analysis
- Regression Analysis
Good to know
Examines classification, a crucial aspect of supervised learning methods
Introduces essential classifiers like KNN, decision tree, SVM, Naïve Bayes, and logistic regression
Provides hands-on experience with tutorials and case studies, fostering practical skills
Teaches model evaluation techniques with metrics like accuracy, precision, recall, F1 score, and ROC curves
Develops the ability to select and fine-tune classifiers based on data and learning requirements
Covers a comprehensive range of classification algorithms, offering a thorough foundation
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