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Ryan Ahmed
In this 1-hour long project-based course, we will predict titanic survivors’ using logistic regression and naïve bayes classifiers. The sinking of the Titanic is one of the key sad tragedies in history and it took place on April 15th, 1912. The numbers of survivors were low due to lack of lifeboats for all passengers. This practical guided project, we will analyze what sorts of people were likely to survive this tragedy with the power of machine learning.
Note: 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/u2hful/titanic
What's inside
Syllabus
Project Overview
In this project-based course, we will build, train and test a machine learning model to predict titanic survivors based on their features such as age, number of siblings, and ticket class. We will explore two machine learning algorithms, namely: (1) logistic regression classifier model and (2) Naive Bayes classifier model.
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Introduces learners to the theory behind logistic regression and Naïve Bayes, a common foundational element in many machine learning specialties
Applies machine learning to a real-world dataset, which translates concepts into useful models and improves comprehension
Helps learners develop strong problem-solving skills by using machine learning to address real-world problems
Teaches machine learning theory by walking the learner through every step of building and training a model, reinforcing these concepts
Instructs learners in the application of model building across varying data sets, a key skill for professionals in every industry where data is available
Provides hands-on practice with supervised machine learning techniques, such as logistic regression and Naïve Bayes, a standard foundational element in many machine learning specialties
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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 Titanic Survival Prediction Using Machine Learning with these
activities:
Organize and review course materials
Show steps
Keep organized and optimize your learning by reviewing and consolidating course materials
Show steps
-
Organize notes, assignments, and quizzes into a central location
-
Review materials regularly to reinforce concepts and identify areas for improvement
Review statistical concepts
Show steps
Refresh your understanding of statistical concepts to build a strong foundation for the course
Browse courses on
Statistical Concepts
Show steps
-
Review notes from previous statistics courses or textbooks
-
Work through practice problems to reinforce your understanding
Read 'An Introduction to Statistical Learning'
Show steps
Expand your understanding of statistical concepts and machine learning techniques for Titanic survivor prediction
View
An Introduction to Statistical Learning: with...
on Amazon
Show steps
-
Read the book's chapters on logistic regression and naive Bayes classifiers
-
Work through the practice problems provided in the book
Two other activities
Expand to see all activities and additional details
Show all five activities
Solve Titanic survivor prediction practice problems
Show steps
Practice solving Titanic survivor prediction problems to improve your understanding of logistic regression and naive Bayes classifiers
Browse courses on
Machine Learning
Show steps
-
Find practice problems online or in textbooks
-
Solve the problems using the techniques learned in class
-
Check your solutions against provided answer keys
Build a Titanic survivor prediction model
Show steps
Apply your knowledge of logistic regression and naive Bayes classifiers to build a model that predicts Titanic survivors
Show steps
-
Gather and clean the Titanic dataset
-
Choose and apply appropriate machine learning algorithms
-
Evaluate the performance of your model
Organize and review course materials
Show steps
Keep organized and optimize your learning by reviewing and consolidating course materials
Show steps
Show steps
Show steps
- Organize notes, assignments, and quizzes into a central location
- Review materials regularly to reinforce concepts and identify areas for improvement
Review statistical concepts
Show steps
Refresh your understanding of statistical concepts to build a strong foundation for the course
Browse courses on
Statistical Concepts
Show steps
Show steps
Show steps
- Review notes from previous statistics courses or textbooks
- Work through practice problems to reinforce your understanding
Read 'An Introduction to Statistical Learning'
Show steps
Expand your understanding of statistical concepts and machine learning techniques for Titanic survivor prediction
View
An Introduction to Statistical Learning: with...
on Amazon
Show steps
Show steps
Show steps
- Read the book's chapters on logistic regression and naive Bayes classifiers
- Work through the practice problems provided in the book
Two other activities
Expand to see all activities and additional details
Show all five activities
Solve Titanic survivor prediction practice problems
Show steps
Practice solving Titanic survivor prediction problems to improve your understanding of logistic regression and naive Bayes classifiers
Browse courses on
Machine Learning
Show steps
Show steps
Show steps
- Find practice problems online or in textbooks
- Solve the problems using the techniques learned in class
- Check your solutions against provided answer keys
Build a Titanic survivor prediction model
Show steps
Apply your knowledge of logistic regression and naive Bayes classifiers to build a model that predicts Titanic survivors
Show steps
Show steps
Show steps
- Gather and clean the Titanic dataset
- Choose and apply appropriate machine learning algorithms
- Evaluate the performance of your model
Career center
Learners who complete Titanic Survival Prediction Using Machine Learning will develop knowledge and skills
that may be useful to these careers:
Data Scientist
Data Scientist
A Data Scientist analyzes and interprets data to extract meaningful insights. By gaining the ability to utilize logistic regression and naïve Bayes classifiers to predict outcomes, you can significantly enhance your ability to make data-driven decisions. This course provides a solid foundation in machine learning techniques that can prove invaluable in this role.
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers develop and implement machine learning models to solve real-world problems. This course offers a practical approach to building and evaluating machine learning models using logistic regression and naïve Bayes classifiers, providing you with a valuable skill set for this in-demand role.
Business Analyst
Business Analyst
Business Analysts analyze business processes and develop solutions to improve efficiency. By mastering logistic regression and naïve Bayes classifiers, you can enhance your ability to identify patterns and make data-driven recommendations, becoming a more effective Business Analyst.
Operations Research Analyst
Operations Research Analyst
Operations Research Analysts use mathematical and analytical techniques to solve complex business problems. By gaining proficiency in logistic regression and naïve Bayes classifiers, you can develop optimization models and make data-driven decisions, enhancing your effectiveness in this role.
Statistician
Statistician
Statisticians apply statistical methods to analyze and interpret data. This course provides hands-on experience with logistic regression and naïve Bayes classifiers, equipping you with advanced techniques for data analysis and modeling, which are essential in this field.
Risk Manager
Risk Manager
Risk Managers identify and mitigate potential risks to an organization. This course provides a practical understanding of logistic regression and naïve Bayes classifiers, equipping you with the skills to assess and manage risks effectively, making you a valuable asset in this role.
Market Researcher
Market Researcher
Market Researchers collect and analyze data to understand consumer behavior and market trends. This course offers hands-on experience with logistic regression and naïve Bayes classifiers, providing you with valuable techniques for predicting customer preferences and making informed marketing decisions.
Quantitative Analyst (Quant)
Quantitative Analyst (Quant)
Quantitative Analysts (Quants) develop and implement mathematical and statistical models to analyze financial data. By learning logistic regression and naïve Bayes classifiers, you can enhance your ability to build predictive models and make informed decisions in the financial industry.
Data Analyst
Data Analyst
Data Analysts collect, analyze, and interpret data to uncover trends and patterns. By mastering logistic regression and naïve Bayes classifiers, you can strengthen your ability to extract meaningful insights from data, making you a more effective Data Analyst.
Financial Analyst
Financial Analyst
Financial Analysts evaluate financial data and make investment recommendations. By gaining proficiency in logistic regression and naïve Bayes classifiers, you can enhance your ability to build financial models and make informed investment decisions, increasing your effectiveness in this role.
Data Engineer
Data Engineer
Data Engineers design and build systems to store, process, and analyze data. This course provides a solid foundation in logistic regression and naïve Bayes classifiers, equipping you with the skills to develop and maintain machine learning pipelines, making you a more effective Data Engineer.
Actuary
Actuary
Actuaries assess and manage financial risks. This course provides a practical introduction to logistic regression and naïve Bayes classifiers, enabling you to develop a solid understanding of risk assessment and prediction techniques commonly used in the actuarial field.
Software Engineer
Software Engineer
Software Engineers design, develop, and maintain software systems. While not directly related to machine learning, this course may be helpful in providing a foundation for developing software solutions that incorporate machine learning capabilities.
Web Developer
Web Developer
Web Developers design and develop websites and web applications. While not directly related to machine learning, this course may be helpful in providing a foundation for developing web applications that incorporate machine learning capabilities.
Database Administrator
Database Administrator
Database Administrators manage and maintain databases. While not directly related to machine learning, this course may be helpful in providing a foundation for managing and maintaining databases used in machine learning applications.
For more career information including salaries, visit:
OpenCourser.com/course/u2hful/titanic
Reading list
We've selected nine 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
Titanic Survival Prediction Using Machine Learning.
Save
Describes the discovery and exploration of the Titanic wreck by Robert Ballard and his team. It provides a firsthand account of the expedition and the challenges involved in finding and documenting the wreck.
Challenges the traditional narrative of the Titanic disaster, arguing that the sinking was caused by a combination of factors, including design flaws, human error, and regulatory failures. It thought-provoking read for anyone interested in the Titanic disaster.
Save
Provides a scientific analysis of the sinking of the Titanic, examining the factors that contributed to the disaster and the lessons learned. It valuable resource for anyone interested in understanding the Titanic disaster from a technical perspective.
Presents the personal accounts of survivors and witnesses of the Titanic disaster. It provides a unique and moving perspective on the tragedy.
Examines the legacy of the Titanic disaster, exploring the impact it has had on popular culture, maritime safety, and our understanding of disaster. It thought-provoking read for anyone interested in the Titanic disaster.
Comprehensive collection of articles, essays, and documents related to the Titanic disaster. It valuable resource for anyone interested in learning more about the Titanic, from its design and construction to the aftermath of the sinking.
Classic account of the Titanic disaster, based on interviews with survivors and witnesses. It gripping and moving account of the tragedy, and it is considered one of the best books ever written about the Titanic.
Examines the aftermath of the Titanic disaster, exploring the impact it had on the shipping industry, maritime safety, and public opinion. It valuable resource for anyone interested in understanding the long-term consequences of the Titanic disaster.
Children's book that tells the story of the Titanic disaster in a simple and engaging way. It good book for introducing children to the Titanic disaster.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/u2hful/titanic
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Effort
2 hours
Level
Introductory
Via
Coursera
Institution
Coursera Project Network
Instructor
Ryan Ahmed
Language
English
Good to know
Introduces learners to the theory behind logistic regression and Naïve Bayes, a common foundational element in many machine learning specialties
Applies machine learning to a real-world dataset, which translates concepts into useful models and improves comprehension
Helps learners develop strong problem-solving skills by using machine learning to address real-world problems
Teaches machine learning theory by walking the learner through every step of building and training a model, reinforcing these concepts
Instructs learners in the application of model building across varying data sets, a key skill for professionals in every industry where data is available
Provides hands-on practice with supervised machine learning techniques, such as logistic regression and Naïve Bayes, a standard foundational element in many machine learning specialties
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