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Mírian Silva
In this 1-hour long project, you will be able to understand how to predict which passengers survived the Titanic shipwreck and make your first submission in an Machine Learning competition inside the Kaggle platform. Also, you as a beginner in Machine Learning applications, will get familiar and get a deep understanding of how to start a model prediction using basic supervised Machine Learning models. We will choose classifiers to learn, predict, and make an Exploratory Data Analysis (also called EDA). At the end, you will know how to measure a model performance, and submit your model to the competition and get a score from Kaggle.
Read more
In this 1-hour long project, you will be able to understand how to predict which passengers survived the Titanic shipwreck and make your first submission in an Machine Learning competition inside the Kaggle platform. Also, you as a beginner in Machine Learning applications, will get familiar and get a deep understanding of how to start a model prediction using basic supervised Machine Learning models. We will choose classifiers to learn, predict, and make an Exploratory Data Analysis (also called EDA). At the end, you will know how to measure a model performance, and submit your model to the competition and get a score from Kaggle.
Read more
In this 1-hour long project, you will be able to understand how to predict which passengers survived the Titanic shipwreck and make your first submission in an Machine Learning competition inside the Kaggle platform. Also, you as a beginner in Machine Learning applications, will get familiar and get a deep understanding of how to start a model prediction using basic supervised Machine Learning models. We will choose classifiers to learn, predict, and make an Exploratory Data Analysis (also called EDA). At the end, you will know how to measure a model performance, and submit your model to the competition and get a score from Kaggle.
This guided project is for beginners in Data Science who want to do a practical application using Machine Learning. You will get familiar with the methods used in machine learning applications and data analysis.
In order to be successful in this project, you should have an account on the Kaggle platform (no cost is necessary). Be familiar with some basic Python programming, we will use numpy and pandas libraries. Some background in Statistics is appreciated, like as knowledge in probability, but it’s not a requirement.
Register for this course and see more details by visiting:
OpenCourser.com/course/ovfzfe/get
What's inside
Syllabus
Project Overview
By the end of this project, you will be able to understand how to predict which passengers survived the Titanic shipwreck and make your first submission in an ML competition inside the Kaggle platform. Also, you as a beginner in Machine Learning applications, will get familiar and get a deep understanding of how to start a model prediction using basic supervised Machine Learning models. We will choose classifiers to learn, predict, and make an Exploratory Data Analysis (also called EDA). At the end, you will know how to measure a model performance, and submit your model to the competition and get a score from Kaggle.
This guided project is for beginners in Data Science who want to do a practical application using Machine Learning. You will get familiar with the methods used in machine learning applications and data analysis.
In order to be successful in this project, you should have an account on the Kaggle platform (no cost is necessary). Be familiar with some basic Python programming, we will use numpy and pandas libraries. Some background in Statistics is appreciated, like as knowledge in probability, but it’s not a requirement.
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Suitable for beginners interested in applying Machine Learning in a practical context
Covers fundamental supervised Machine Learning concepts
Guides learners through an end-to-end ML project
Facilitates a hands-on Kaggle competition submission
Utilizes Python's NumPy and Pandas libraries for data manipulation
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Reviews summary
Basics of kaggle competitions
Learners say this course is perfect for beginners looking for a guided project and the basics of Kaggle competitions. Students particularly enjoyed the practical nature of the class and were able to learn all the basics needed to get started with Kaggle. However, even for beginners, some learners felt as though the course was lacking in theoretical background and explanations.
Suitable as a starting point for learners new to Kaggle
"This is a really good guided project to start with Kaggle Competition."
"perfect"
Sparse theoretical explanations make it difficult for beginners
"there is very little theoretical explanation as to why the preprocessing should be done"
"Overall, there was too little explanation of the theoretical background in this class."
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 Get Familiar with ML basics in a Kaggle Competition with these
activities:
Introduction to Machine Learning, 4th Edition
Show steps
This book provides a comprehensive overview of machine learning, covering a wide range of topics from linear regression to neural networks.
View
Machine Learning, revised and updated edition
on Amazon
Show steps
Find a Mentor
Show steps
A mentor can provide guidance and support as you learn about machine learning.
Show steps
-
Identify the skills and knowledge you want to develop
-
Network with people in your field
-
Ask for introductions to potential mentors
Machine Learning Tutorial
Show steps
Machine learning is a vast field, and it is important to have a strong foundation in the basics. This tutorial will provide you with the background and practical knowledge
Browse courses on
Machine Learning
Show steps
-
Review the basics of machine learning
-
Understand the different types of machine learning algorithms
-
Learn how to train and test machine learning models
-
Apply machine learning to solve real-world problems
Three other activities
Expand to see all activities and additional details
Show all six activities
Practice Machine Learning Algorithms
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Practice makes perfect. This activity will provide you with the opportunity to practice using different machine learning algorithms.
Browse courses on
Machine Learning
Show steps
-
Use a machine learning library to implement different algorithms
-
Experiment with different hyperparameters to improve model performance
-
Analyze the results of your experiments
Machine Learning Project
Show steps
This is an opportunity to apply your new skills to a real-world problem. Choose a dataset and build a model to solve a problem that interests you.
Show steps
-
Define the problem you want to solve
-
Gather and prepare the data
-
Choose and train a machine learning model
-
Evaluate the model and make improvements
Machine Learning Project
Show steps
This is a major project that will allow you to apply your skills and knowledge to a real-world problem. You will work in a team to build a machine learning model that solves a specific problem.
Show steps
-
Define the problem you want to solve
-
Gather and prepare the data
-
Choose and train a machine learning model
-
Evaluate the model and make improvements
-
Present your results
Introduction to Machine Learning, 4th Edition
Show steps
This book provides a comprehensive overview of machine learning, covering a wide range of topics from linear regression to neural networks.
View
Machine Learning, revised and updated edition
on Amazon
Show steps
Show steps
Show steps
Find a Mentor
Show steps
A mentor can provide guidance and support as you learn about machine learning.
Show steps
Show steps
Show steps
- Identify the skills and knowledge you want to develop
- Network with people in your field
- Ask for introductions to potential mentors
Machine Learning Tutorial
Show steps
Machine learning is a vast field, and it is important to have a strong foundation in the basics. This tutorial will provide you with the background and practical knowledge
Browse courses on
Machine Learning
Show steps
Show steps
Show steps
- Review the basics of machine learning
- Understand the different types of machine learning algorithms
- Learn how to train and test machine learning models
- Apply machine learning to solve real-world problems
Three other activities
Expand to see all activities and additional details
Show all six activities
Practice Machine Learning Algorithms
Show steps
Practice makes perfect. This activity will provide you with the opportunity to practice using different machine learning algorithms.
Browse courses on
Machine Learning
Show steps
Show steps
Show steps
- Use a machine learning library to implement different algorithms
- Experiment with different hyperparameters to improve model performance
- Analyze the results of your experiments
Machine Learning Project
Show steps
This is an opportunity to apply your new skills to a real-world problem. Choose a dataset and build a model to solve a problem that interests you.
Show steps
Show steps
Show steps
- Define the problem you want to solve
- Gather and prepare the data
- Choose and train a machine learning model
- Evaluate the model and make improvements
Machine Learning Project
Show steps
This is a major project that will allow you to apply your skills and knowledge to a real-world problem. You will work in a team to build a machine learning model that solves a specific problem.
Show steps
Show steps
Show steps
- Define the problem you want to solve
- Gather and prepare the data
- Choose and train a machine learning model
- Evaluate the model and make improvements
- Present your results
Career center
Learners who complete Get Familiar with ML basics in a Kaggle Competition will develop knowledge and skills
that may be useful to these careers:
Machine Learning Scientist
Machine Learning Scientist
A Machine Learning Scientist researches, develops, and deploys machine learning solutions, optimizing and automating systems, processes, and workflows through the data. This course helps build a foundation in machine learning in a practical way, allowing one to apply those principles to build and deploy machine learning solutions to real-world situations, such as optimizing logistics and process flows..
Data Analyst
Data Analyst
A Data Analyst is responsible for collecting, cleaning, processing, and interpreting data, using analytical and statistical models to identify trends and provide useful recommendations to stakeholders. By completing this course, one will gain a strong foundation in using machine learning, statistics, and data analysis to solve real-world problems. The course also covers the Kaggle competition aspect of data science, which is a valuable skill for individuals looking to apply their knowledge in a competitive setting.
Data Scientist
Data Scientist
Data Scientists use their knowledge of data analysis, statistics, and machine learning to extract meaningful insights from data and communicate these insights to stakeholders. By enrolling in this course, which covers machine learning principles, techniques, and practical examples, one may be able to enhance their fundamental skills as a Data Scientist and develop a specialization in machine learning. This course will help establish a strong foundation in machine learning concepts, making one a more valuable asset to any organization seeking to leverage data for improved decision-making.
Quantitative Analyst
Quantitative Analyst
Quantitative Analysts use mathematical and statistical models to analyze financial data and make investment recommendations. This course helps build a foundation in machine learning, providing skills relevant to the field of Quantitative Analysis where machine learning is increasingly used for tasks such as risk management, algorithmic trading, and financial modeling.
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers design, build, test, and deploy machine learning models. With this course, one may be able to establish a robust foundation in machine learning and gain hands-on experience applying these concepts to real-world data. The focus on practical application in a Kaggle competition setting is valuable for Machine Learning Engineers, as it provides an opportunity to showcase skills and learn from other practitioners.
Software Engineer
Software Engineer
Software Engineers design, develop, test, and maintain software systems. This course may be helpful for Software Engineers with an interest in incorporating machine learning into their work. By learning the basics of machine learning and applying them to a real-world dataset, one can gain valuable experience that may enhance their ability to develop and maintain software systems leveraging machine learning.
Quantitative Researcher
Quantitative Researcher
Quantitative Researchers develop and apply mathematical and statistical models to analyze financial data and make investment decisions. This course may be helpful for those interested in pursuing a career in quantitative research, providing a basic understanding of machine learning concepts and techniques. By learning about feature engineering, model selection, and evaluation, one can gain valuable skills that can complement their quantitative research work.
Statistician
Statistician
Statisticians collect, analyze, and interpret data to provide insights for decision-making. Completing this course may be useful for Statisticians who wish to expand their knowledge of machine learning. By gaining experience with supervised machine learning models, one may be able to enhance their ability to analyze data and develop more robust statistical models.
Product Manager
Product Manager
Product Managers are responsible for the development and management of products, ensuring they meet customer needs and business objectives. This course may be useful for aspiring or current Product Managers interested in gaining a fundamental understanding of machine learning. By learning about the potential applications of machine learning in product development, one may be able to make more informed decisions about incorporating machine learning into their products and enhancing their overall value proposition.
Consultant
Consultant
Consultants provide advice and expertise to organizations on a wide range of topics, including business strategy, operations, and technology. This course may be helpful for aspiring or current Consultants interested in specializing in data science and machine learning. By gaining exposure to real-world case studies and industry best practices, one can develop a deeper understanding of how machine learning can be applied to solve business problems and deliver value to clients.
Business Analyst
Business Analyst
Business Analysts analyze business processes and recommend solutions to improve efficiency and effectiveness. While this course may be useful for Business Analysts who want to gain a basic understanding of machine learning, it may be more beneficial for those interested in applying machine learning to specific industries or domains.
Researcher
Researcher
Researchers conduct scientific studies and analyze data to advance knowledge and understanding in various fields. This course may be helpful for aspiring or current Researchers interested in incorporating machine learning into their research. By gaining familiarity with supervised machine learning models, feature engineering techniques, and model evaluation, one can enhance their ability to design and conduct more robust research studies.
Data Engineer
Data Engineer
Data Engineers design, build, and maintain data infrastructure. This course may be helpful for aspiring or current Data Engineers who wish to gain a foundational understanding of machine learning. By learning about data preparation, feature engineering, and model evaluation, one can gain valuable skills that may complement their data engineering responsibilities. However, it may be more beneficial to focus on more specialized courses tailored specifically for Data Engineering.
Freelance Data Scientist
Freelance Data Scientist
Freelance Data Scientists provide data science services to clients on a contract basis. This course may be helpful for aspiring or current Freelance Data Scientists who wish to enhance their skills and knowledge in machine learning. By gaining experience with real-world case studies and industry best practices, one can develop a deeper understanding of how to apply machine learning to solve business problems and deliver value to clients.
Entrepreneur
Entrepreneur
Entrepreneurs start and run their own businesses, taking on the risks and rewards of entrepreneurship. While this course may provide a basic understanding of machine learning, it may be more beneficial for Entrepreneurs to focus on courses and resources tailored specifically to the challenges and opportunities of starting and running a successful business.
For more career information including salaries, visit:
OpenCourser.com/course/ovfzfe/get
Reading list
We've selected 14 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
Get Familiar with ML basics in a Kaggle Competition.
Save
Provides a comprehensive introduction to deep learning, covering both the theoretical foundations and practical applications. It valuable resource for both beginners and experienced practitioners alike.
Provides a comprehensive introduction to speech and language processing, covering both the theoretical foundations and practical applications. It valuable resource for both beginners and experienced practitioners alike.
Provides a comprehensive introduction to pattern recognition and machine learning, covering both the theoretical foundations and practical applications. It valuable resource for both beginners and experienced practitioners alike.
Provides a comprehensive introduction to statistical learning, covering both the theoretical foundations and practical applications. It valuable resource for both beginners and experienced practitioners alike.
Provides a comprehensive introduction to data science, covering both the theoretical foundations and practical applications. It valuable resource for both beginners and experienced practitioners alike.
Provides a comprehensive introduction to reinforcement learning, covering both the theoretical foundations and practical applications. It valuable resource for both beginners and experienced practitioners alike.
Provides a comprehensive introduction to deep reinforcement learning, covering both the theoretical foundations and practical applications. It valuable resource for both beginners and experienced practitioners alike.
Provides a comprehensive introduction to natural language processing, using Python. It valuable resource for both beginners and experienced practitioners alike.
Provides a practical guide to data science for business professionals. It valuable resource for anyone who wants to learn how to use data to make better decisions.
Provides a practical guide to machine learning, using Python. It valuable resource for beginners who want to learn how to apply machine learning to real-world problems.
Provides a practical guide to machine learning, using Python. It valuable resource for beginners who want to learn how to apply machine learning to real-world problems.
Provides a gentle introduction to machine learning, using Python. It valuable resource for beginners who want to learn the basics of machine learning without getting bogged down in the details.
Provides a more theoretical introduction to machine learning, focusing on the probabilistic foundations of the field. It valuable resource for students who want to understand the underlying principles of machine learning.
Provides a gentle introduction to data science, using Python. It valuable resource for beginners who want to learn the basics of data science without getting bogged down in the details.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/ovfzfe/get
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Effort
2 hours
Level
Introductory
Via
Coursera
Institution
Coursera Project Network
Instructor
Mírian Silva
Language
English
Good to know
Suitable for beginners interested in applying Machine Learning in a practical context
Covers fundamental supervised Machine Learning concepts
Guides learners through an end-to-end ML project
Facilitates a hands-on Kaggle competition submission
Utilizes Python's NumPy and Pandas libraries for data manipulation
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