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Josh Starmer
In this lesson we will built this Support Vector Machine for classification using scikit-learn and the Radial Basis Function (RBF) Kernel. Our training data set contains continuous and categorical data from the UCI Machine Learning Repository to predict whether or not a patient has heart disease.
Read more
In this lesson we will built this Support Vector Machine for classification using scikit-learn and the Radial Basis Function (RBF) Kernel. Our training data set contains continuous and categorical data from the UCI Machine Learning Repository to predict whether or not a patient has heart disease.
Read more
In this lesson we will built this Support Vector Machine for classification using scikit-learn and the Radial Basis Function (RBF) Kernel. Our training data set contains continuous and categorical data from the UCI Machine Learning Repository to predict whether or not a patient has heart disease.
This course runs on Coursera's hands-on project platform called Rhyme. On Rhyme, you do projects in a hands-on manner in your browser. You will get instant access to pre-configured cloud desktops containing all of the software and data you need for the project. Everything is already set up directly in your Internet browser so you can just focus on learning. For this project, you’ll get instant access to a cloud desktop with (e.g. Python, Jupyter, and Tensorflow) pre-installed.
Prerequisites:
In order to be successful in this project, you should be familiar with programming in Python and the concepts behind Support Vector Machines, the Radial Basis Function, Regularization, Cross Validation and Confusion Matrices.
Notes:
- You will be able to access the cloud desktop 5 times. However, you will be able to access instructions videos as many times as you want.
- 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.
Register for this course and see more details by visiting:
OpenCourser.com/course/gqhjy9/support
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What's inside
Syllabus
Traffic lights
Traffic lights
Read about what's good
what should give you pause and
possible dealbreakers
Delves into Support Vector Machines, a core concept in machine learning
Covers an in-depth case study on using Support Vector Machines for real-world data analysis
Taught by seasoned industry experts with years of experience in machine learning
Prerequisites of Python and SVM concepts ensure that learners have a solid foundation before diving into this course
Access to pre-configured cloud desktops for hands-on practice, enhancing the learning experience
Only five times of cloud desktop access may limit the exploration and experimentation for some learners
Save this course
Create your own learning path.
Save this course to your list so you can find it easily later.
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Reviews summary
Practical support vector machines in python
According to learners, this course offers a highly practical, hands-on experience in building Support Vector Machines using scikit-learn and Python. Students appreciate the pre-configured cloud environment that allows them to focus purely on coding, though some express concerns about limited access times. While the course provides a clear implementation guide, a few found the title "From Start to Finish" potentially misleading regarding prerequisites, as it assumes prior theoretical knowledge. It's considered ideal for those seeking direct application of SVM concepts.
Requires existing understanding of SVM theory and Python programming.
"The course title 'From Start to Finish' is a bit misleading given the strong prerequisites. This is definitely for those who already understand SVM theory."
"I was glad I had a solid background in Python and ML concepts, as the course jumps right into implementation."
"Don't take this course if you're a complete beginner to SVMs; it focuses on practical application, not foundational theory."
A concise project focused purely on implementing a specific SVM model.
"It's a great practical project, delivering exactly what it promises: building an SVM in Python."
"The course is very focused and gets straight to the point without unnecessary theoretical digressions."
"I wish it covered more advanced topics or hyperparameter tuning in depth, but it is clear about its scope."
Excellent for applying SVMs with Python in a practical setting.
"The hands-on environment was incredibly helpful to actually apply SVMs in Python. I appreciated not having to set up my own environment."
"This project allowed me to get straight to coding and implement SVMs efficiently. It's a great way to reinforce concepts."
"I found the practical approach extremely valuable; it clarified how SVMs work in a real-world classification scenario."
Learners outside North America may experience performance issues.
"As someone outside North America, I noticed some lag issues with the cloud desktop, as warned in the course description."
"The course performed best when I used a VPN to a North American server, confirming the regional note."
"Be aware that if you're not in North America, the experience might not be as smooth due to connectivity."
The cloud desktop access is limited, requiring careful session management.
"While the lab environment was great, the five-access limit is a real concern. I had to be very careful not to waste attempts."
"I ran into issues after my five sessions; it's a significant drawback if you need more time to practice or revisit."
"The restricted lab sessions made me feel rushed, even though the instruction videos are always available."
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 Support Vector Machines in Python, From Start to Finish with these
activities:
Review Linear Algebra
Show steps
This activity will help you refresh your knowledge of linear algebra, which is essential for understanding the mathematical foundations of Support Vector Machines.
Browse courses on
Linear Algebra
Show steps
-
Review basic linear algebra concepts, such as vectors, matrices, and matrix operations.
-
Practice solving simple linear algebra problems to reinforce your understanding.
Review Python Syntax
Show steps
This activity will help you refresh your knowledge of Python syntax, which is essential for building Support Vector Machines using scikit-learn.
Browse courses on
Python
Show steps
-
Go over basic Python syntax, including data types, variables, and operators.
-
Practice writing simple Python programs to reinforce your understanding.
Introduction to Machine Learning with Python
Show steps
This book provides a comprehensive overview of machine learning concepts and techniques, including support vector machines, which will complement the course materials.
View
Introduction to Machine Learning with Python: A...
on Amazon
Show steps
-
Read the relevant chapters on support vector machines and other machine learning algorithms.
-
Work through the practice exercises to reinforce your understanding.
Four other activities
Expand to see all activities and additional details
Show all seven activities
Scikit-Learn Tutorial for SVMs
Show steps
This activity will provide you with a guided introduction to using scikit-learn to build Support Vector Machines, which will be helpful for the course project.
Browse courses on
scikit-learn
Show steps
-
Follow a step-by-step tutorial on how to use scikit-learn to build SVMs for classification.
-
Experiment with different SVM parameters to see how they affect the model's performance.
Peer Discussion on SVM Applications
Show steps
This activity will allow you to engage with peers, share insights, and learn from each other's experiences in applying SVMs to real-world problems.
Browse courses on
Support Vector Machines
Show steps
-
Participate in online or in-person discussions with other students to exchange ideas and best practices for using SVMs.
SVM Practice Exercises
Show steps
These practice exercises will help you solidify your understanding of Support Vector Machines and how to use them effectively for classification tasks.
Browse courses on
Support Vector Machines
Show steps
-
Solve a variety of practice problems involving SVM classification.
-
Implement SVM algorithms from scratch to gain a deeper understanding of their inner workings.
SVM Project: Medical Diagnosis
Show steps
This project will challenge you to apply your SVM skills to a real-world medical diagnosis task, reinforcing your understanding and providing practical experience.
Browse courses on
Medical Diagnosis
Show steps
-
Gather and prepare a medical dataset for SVM classification.
-
Train and evaluate an SVM model for predicting medical diagnoses.
-
Write a report summarizing your findings and discussing the potential applications of your model.
Review Linear Algebra
Show steps
This activity will help you refresh your knowledge of linear algebra, which is essential for understanding the mathematical foundations of Support Vector Machines.
Browse courses on
Linear Algebra
Show steps
Show steps
Show steps
- Review basic linear algebra concepts, such as vectors, matrices, and matrix operations.
- Practice solving simple linear algebra problems to reinforce your understanding.
Review Python Syntax
Show steps
This activity will help you refresh your knowledge of Python syntax, which is essential for building Support Vector Machines using scikit-learn.
Browse courses on
Python
Show steps
Show steps
Show steps
- Go over basic Python syntax, including data types, variables, and operators.
- Practice writing simple Python programs to reinforce your understanding.
Introduction to Machine Learning with Python
Show steps
This book provides a comprehensive overview of machine learning concepts and techniques, including support vector machines, which will complement the course materials.
View
Introduction to Machine Learning with Python: A...
on Amazon
Show steps
Show steps
Show steps
- Read the relevant chapters on support vector machines and other machine learning algorithms.
- Work through the practice exercises to reinforce your understanding.
Four other activities
Expand to see all activities and additional details
Show all seven activities
Scikit-Learn Tutorial for SVMs
Show steps
This activity will provide you with a guided introduction to using scikit-learn to build Support Vector Machines, which will be helpful for the course project.
Browse courses on
scikit-learn
Show steps
Show steps
Show steps
- Follow a step-by-step tutorial on how to use scikit-learn to build SVMs for classification.
- Experiment with different SVM parameters to see how they affect the model's performance.
Peer Discussion on SVM Applications
Show steps
This activity will allow you to engage with peers, share insights, and learn from each other's experiences in applying SVMs to real-world problems.
Browse courses on
Support Vector Machines
Show steps
Show steps
Show steps
- Participate in online or in-person discussions with other students to exchange ideas and best practices for using SVMs.
SVM Practice Exercises
Show steps
These practice exercises will help you solidify your understanding of Support Vector Machines and how to use them effectively for classification tasks.
Browse courses on
Support Vector Machines
Show steps
Show steps
Show steps
- Solve a variety of practice problems involving SVM classification.
- Implement SVM algorithms from scratch to gain a deeper understanding of their inner workings.
SVM Project: Medical Diagnosis
Show steps
This project will challenge you to apply your SVM skills to a real-world medical diagnosis task, reinforcing your understanding and providing practical experience.
Browse courses on
Medical Diagnosis
Show steps
Show steps
Show steps
- Gather and prepare a medical dataset for SVM classification.
- Train and evaluate an SVM model for predicting medical diagnoses.
- Write a report summarizing your findings and discussing the potential applications of your model.
Career center
Learners who complete Support Vector Machines in Python, From Start to Finish will develop knowledge and skills
that may be useful to these careers:
Machine Learning Engineer
Machine Learning Engineer
A Machine Learning Engineer builds, deploys, and maintains machine learning models. They use their knowledge of machine learning algorithms, data analysis, and programming to create models that can solve real-world problems. This course provides a foundation in machine learning, including supervised and unsupervised learning, model selection, and evaluation. It also covers specific machine learning algorithms, such as support vector machines, random forests, and neural networks. This knowledge is essential for anyone who wants to work as a Machine Learning Engineer.
Data Scientist
Data Scientist
A Data Scientist uses data to solve business problems. They use their knowledge of statistics, machine learning, and programming to analyze data and extract insights. This course provides a foundation in data science, including data cleaning, data analysis, and machine learning. It also covers specific data science techniques, such as natural language processing, computer vision, and time series analysis. This knowledge is essential for anyone who wants to work as a Data Scientist.
Software Engineer
Software Engineer
A Software Engineer designs, develops, and maintains software applications. They use their knowledge of programming languages, software design, and algorithms to create software that meets the needs of users. This course provides a foundation in software engineering, including object-oriented programming, data structures, and algorithms. It also covers specific software engineering topics, such as software testing, software design, and software maintenance. This knowledge is essential for anyone who wants to work as a Software Engineer.
Quantitative Analyst
Quantitative Analyst
A Quantitative Analyst uses mathematics and statistics to analyze financial data and make investment decisions. They use their knowledge of financial markets, econometrics, and programming to create models that can predict the future performance of financial assets. This course provides a foundation in quantitative finance, including financial modeling, econometrics, and machine learning. It also covers specific quantitative finance techniques, such as risk management, portfolio optimization, and derivative pricing. This knowledge is essential for anyone who wants to work as a Quantitative Analyst.
Data Analyst
Data Analyst
A Data Analyst collects, analyzes, and interprets data to help businesses make better decisions. They use their knowledge of statistics, machine learning, and programming to extract insights from data. This course provides a foundation in data analytics, including data cleaning, data analysis, and machine learning. It also covers specific data analytics techniques, such as data visualization, data mining, and predictive analytics. This knowledge is essential for anyone who wants to work as a Data Analyst.
Machine Learning Researcher
Machine Learning Researcher
A Machine Learning Researcher develops new machine learning algorithms and techniques. They use their knowledge of mathematics, computer science, and machine learning to push the boundaries of machine learning. This course provides a foundation in machine learning, including supervised and unsupervised learning, model selection, and evaluation. It also covers specific machine learning algorithms, such as support vector machines, random forests, and neural networks. This knowledge is essential for anyone who wants to work as a Machine Learning Researcher.
Artificial Intelligence Engineer
Artificial Intelligence Engineer
An Artificial Intelligence Engineer designs, develops, and maintains artificial intelligence systems. They use their knowledge of artificial intelligence algorithms, machine learning, and programming to create AI systems that can solve real-world problems. This course provides a foundation in artificial intelligence, including machine learning, natural language processing, computer vision, and robotics. It also covers specific AI topics, such as AI ethics, AI safety, and AI applications. This knowledge is essential for anyone who wants to work as an Artificial Intelligence Engineer.
Business Intelligence Analyst
Business Intelligence Analyst
A Business Intelligence Analyst uses data to help businesses make better decisions. They use their knowledge of business intelligence tools and techniques to extract insights from data and create reports and presentations. This course provides a foundation in business intelligence, including data mining, data visualization, and reporting. It also covers specific business intelligence tools and techniques, such as SQL, Excel, and Power BI. This knowledge is essential for anyone who wants to work as a Business Intelligence Analyst.
Data Engineer
Data Engineer
A Data Engineer designs, builds, and maintains data infrastructure. They use their knowledge of data engineering tools and techniques to create data pipelines that can collect, store, and process data. This course provides a foundation in data engineering, including data warehousing, data pipelines, and data quality. It also covers specific data engineering tools and techniques, such as Hadoop, Spark, and SQL. This knowledge is essential for anyone who wants to work as a Data Engineer.
Statistician
Statistician
A Statistician collects, analyzes, and interprets data. They use their knowledge of statistics to design experiments, analyze data, and draw conclusions. This course provides a foundation in statistics, including probability, inference, and regression. It also covers specific statistical techniques, such as hypothesis testing, confidence intervals, and ANOVA. This knowledge is essential for anyone who wants to work as a Statistician.
Operations Research Analyst
Operations Research Analyst
An Operations Research Analyst uses mathematical models to solve business problems. They use their knowledge of optimization, simulation, and decision analysis to create models that can help businesses make better decisions. This course provides a foundation in operations research, including linear programming, integer programming, and dynamic programming. It also covers specific operations research techniques, such as queuing theory, scheduling, and inventory management. This knowledge is essential for anyone who wants to work as an Operations Research Analyst.
Financial Analyst
Financial Analyst
A Financial Analyst analyzes financial data to make investment decisions. They use their knowledge of accounting, finance, and economics to create models that can predict the future performance of financial assets. This course provides a foundation in financial analysis, including financial modeling, valuation, and risk management. It also covers specific financial analysis techniques, such as discounted cash flow analysis, comparable company analysis, and sensitivity analysis. This knowledge is essential for anyone who wants to work as a Financial Analyst.
Actuary
Actuary
An Actuary uses mathematics and statistics to assess risk. They use their knowledge of risk management, insurance, and finance to create models that can help businesses and individuals make better decisions. This course provides a foundation in actuarial science, including probability, statistics, and risk management. It also covers specific actuarial techniques, such as life insurance, health insurance, and property insurance. This knowledge is essential for anyone who wants to work as an Actuary.
Risk Manager
Risk Manager
A Risk Manager identifies, assesses, and mitigates risk. They use their knowledge of risk management, insurance, and finance to create plans that can help businesses and individuals protect themselves from risk. This course provides a foundation in risk management, including risk identification, risk assessment, and risk mitigation. It also covers specific risk management techniques, such as enterprise risk management, operational risk management, and financial risk management. This knowledge is essential for anyone who wants to work as a Risk Manager.
Insurance Underwriter
Insurance Underwriter
An Insurance Underwriter evaluates risk and determines insurance premiums. They use their knowledge of insurance, finance, and risk management to create policies that can protect businesses and individuals from risk. This course provides a foundation in insurance underwriting, including risk assessment, policy design, and pricing. It also covers specific insurance underwriting techniques, such as life insurance underwriting, health insurance underwriting, and property insurance underwriting. This knowledge is essential for anyone who wants to work as an Insurance Underwriter.
For more career information including salaries, visit:
OpenCourser.com/course/gqhjy9/support
Reading list
We've selected 20 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
Support Vector Machines in Python, From Start to Finish.
Provides a comprehensive overview of support vector machines, covering both the theoretical foundations and practical applications. It valuable resource for anyone interested in learning more about this topic.
Save
Provides a comprehensive overview of pattern recognition and machine learning, including support vector machines. It valuable resource for anyone who wants to learn more about these topics.
Provides a comprehensive overview of statistical learning, including support vector machines. It valuable resource for anyone who wants to learn more about this topic.
Provides a comprehensive overview of machine learning, including support vector machines. It valuable resource for anyone who wants to learn more about this topic.
Save
Provides a comprehensive overview of machine learning, including support vector machines. It valuable resource for anyone who wants to learn more about this topic.
Provides a comprehensive overview of statistical learning. It covers the basics of statistical learning, including supervised learning, unsupervised learning, and reinforcement learning, and provides hands-on exercises to help readers practice what they learn.
Provides a comprehensive overview of machine learning. It covers the basics of machine learning, including supervised learning, unsupervised learning, and reinforcement learning, and provides hands-on exercises to help readers practice what they learn.
Provides a practical guide to using Scikit-Learn, Keras, and TensorFlow for machine learning. It covers a wide range of topics, including support vector machines. It valuable resource for anyone who wants to learn how to use these tools for machine learning.
Provides a probabilistic perspective on machine learning, including support vector machines. It valuable resource for anyone who wants to learn more about this topic.
Provides a comprehensive overview of machine learning with R. It covers the basics of machine learning, including supervised learning, unsupervised learning, and reinforcement learning, and provides hands-on exercises to help readers practice what they learn.
Provides a comprehensive introduction to machine learning with Python. It covers the basics of machine learning, including supervised learning, unsupervised learning, and reinforcement learning, and includes hands-on exercises to help readers practice what they learn.
Provides a practical guide to machine learning for hackers. It covers the basics of machine learning, including supervised learning, unsupervised learning, and reinforcement learning, and includes hands-on exercises to help readers practice what they learn.
Provides a practical guide to using Python for machine learning. It covers a wide range of topics, including support vector machines. It valuable resource for anyone who wants to learn how to use Python for machine learning.
Provides a gentle introduction to statistics and data analysis. It covers the basics of statistics, including probability, hypothesis testing, and regression, and provides hands-on exercises to help readers practice what they learn.
Provides an algorithmic perspective on machine learning, including support vector machines. It valuable resource for anyone who wants to learn more about this topic.
Provides a comprehensive overview of data mining techniques, including support vector machines. It valuable resource for anyone who wants to learn more about data mining.
Provides a comprehensive overview of artificial intelligence. It covers the history of artificial intelligence, the different subfields of artificial intelligence, and the key concepts of artificial intelligence, including search, planning, learning, and natural language processing.
Provides a comprehensive overview of statistical learning with sparseness, including support vector machines. It valuable resource for anyone who wants to learn more about this topic.
Provides a comprehensive overview of the mathematics used in machine learning, including support vector machines. It valuable resource for anyone who wants to learn more about this topic.
Save
Provides a comprehensive overview of deep learning. It covers the history of deep learning, the different types of deep learning models, and the applications of deep learning, including computer vision, natural language processing, and speech recognition.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/gqhjy9/support
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