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Ilkay Altintas
and
Julian McAuley
This course will help us to evaluate and compare the models we have developed in previous courses. So far we have developed techniques for regression and classification, but how low should the error of a classifier be (for example) before we decide that the classifier is "good enough"? Or how do we decide which of two regression algorithms is better?
Read more
This course will help us to evaluate and compare the models we have developed in previous courses. So far we have developed techniques for regression and classification, but how low should the error of a classifier be (for example) before we decide that the classifier is "good enough"? Or how do we decide which of two regression algorithms is better?
Read more
This course will help us to evaluate and compare the models we have developed in previous courses. So far we have developed techniques for regression and classification, but how low should the error of a classifier be (for example) before we decide that the classifier is "good enough"? Or how do we decide which of two regression algorithms is better?
By the end of this course you will be familiar with diagnostic techniques that allow you to evaluate and compare classifiers, as well as performance measures that can be used in different regression and classification scenarios. We will also study the training/validation/test pipeline, which can be used to ensure that the models you develop will generalize well to new (or "unseen") data.
Register for this course and see more details by visiting:
OpenCourser.com/course/3f64fs/meaningful
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What's inside
Syllabus
Week 1: Diagnostics for Data
For this first week, we will go over the syllabus, download all course materials, and get your system up and running for the course. We will also introduce the basics of diagnostics for the results of supervised learning.
Read more
Syllabus
Week 1: Diagnostics for Data
For this first week, we will go over the syllabus, download all course materials, and get your system up and running for the course. We will also introduce the basics of diagnostics for the results of supervised learning.
Read more
Week 2: Codebases, Regularization, and Evaluating a Model
This week, we will learn how to create a simple bag of words for analysis. We will also cover regularization and why it matters when building a model. Lastly, we will evaluate a model with regularization, focusing on classifiers.
Week 3: Validation and Pipelines
This week, we will learn about validation and how to implement it in tandem with training and testing. We will also cover how to implement a regularization pipeline in Python and introduce a few guidelines for best practices.
Final Project
In the final week of this course, you will continue building on the project from the first and second courses of Python Data Products for Predictive Analytics with simple predictive machine learning algorithms. Find a dataset, clean it, and perform basic analyses on the data. Evaluate your model, validate your analyses, and make sure you aren't overfitting the data.
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Develops techniques for regression and classification, which are fundamental skills for data analysis and machine learning
Taught by Ilkay Altintas and Julian McAuley, who are recognized for their work in machine learning and data analysis
Introduces diagnostic techniques for evaluating and comparing models, which is crucial for building effective machine learning systems
Examines performance measures that can be used in different regression and classification scenarios, providing learners with a comprehensive understanding of model evaluation
Covers the training/validation/test pipeline, an essential technique for ensuring models generalize well to new data
Save this course
Save Meaningful Predictive Modeling to your list so you can find it easily later:
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Reviews summary
Predictive modeling insights
Learners say that this course provides insightful information on predictive modeling with engaging assignments and helpful instructors. However, some learners mention difficult exams and issues with the peer review system.
Course delivers valuable insights.
"The course provided a lot of insights into predictive modeling."
"Insightful. Thank you so much!!"
"Great course."
Exams are difficult
"difficult exams"
Peer review system needs improvement.
"Peer review system HAS TO CHANGE, IT IS VERY POOR currently"
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 Meaningful Predictive Modeling with these
activities:
Review Linear Algebra Concepts
Show steps
Strengthen your foundation in linear algebra for better understanding of machine learning algorithms.
Browse courses on
Linear Algebra
Show steps
-
Review core concepts of linear algebra.
-
Solve practice problems to reinforce understanding.
-
Consider taking an online course or workshop for further reinforcement.
Review supervised learning concepts
Show steps
Reinforce understanding of core supervised learning concepts by reviewing materials from previous courses. This will aid in building a deeper understanding as the course progresses.
Browse courses on
Regression
Show steps
-
Revisit lecture notes and presentations from previous courses on regression and classification techniques.
-
Review assignments and projects completed in previous courses related to supervised learning.
-
Complete practice problems or quizzes to test understanding of core concepts.
Practice Regularization Techniques
Show steps
Strengthen your grasp of regularization by applying it to your machine learning models.
Browse courses on
Regularization
Show steps
-
Implement L1 and L2 regularization in your model.
-
Evaluate the impact of regularization on model performance.
-
Optimize regularization parameters for different datasets.
Six other activities
Expand to see all activities and additional details
Show all nine activities
Implement Model Evaluation in Python
Show steps
Reinforce your understanding of model evaluation techniques by implementing them in Python.
Browse courses on
Model Evaluation
Show steps
-
Create a dataset and a machine learning model.
-
Calculate evaluation metrics for the model.
-
Iterate through different model parameters and evaluate the impact on model performance.
Assist Peers in Model Evaluation
Show steps
Enhance your understanding of model evaluation by guiding others through the process.
Browse courses on
Model Evaluation
Show steps
-
Identify opportunities to assist peers with model evaluation.
-
Provide support and guidance on evaluation techniques.
-
Review and provide feedback on peer's model evaluation results.
Explore Case Studies on Model Validation
Show steps
Deepen your knowledge of validation techniques and best practices by studying real-world case studies.
Browse courses on
Validation
Show steps
-
Find case studies on model validation.
-
Analyze the methodology and results of the case studies.
-
Identify common patterns and lessons learned.
Attend a Model Validation Workshop
Show steps
Gain practical insights into model validation techniques by attending a workshop.
Browse courses on
Validation
Show steps
-
Identify and register for a relevant workshop.
-
Attend the workshop and actively participate.
-
Apply the knowledge gained to your own modeling projects.
Develop a Model Evaluation Blog Post
Show steps
Solidify your understanding of model evaluation by explaining it in a blog post.
Browse courses on
Model Evaluation
Show steps
-
Choose a specific model evaluation technique to focus on.
-
Explain the methodology and benefits of the technique.
-
Provide examples of how the technique can be applied.
-
Write a draft and seek feedback from peers or mentors.
Build a Model Evaluation Dashboard
Show steps
Deepen your understanding of model evaluation by creating a dashboard to visualize and track model performance.
Browse courses on
Model Evaluation
Show steps
-
Design the dashboard and identify the relevant metrics to track.
-
Develop the dashboard using a suitable programming language and visualization library.
-
Integrate the dashboard with your machine learning pipeline.
-
Test and iterate on the dashboard to ensure its usability and effectiveness.
Review Linear Algebra Concepts
Show steps
Strengthen your foundation in linear algebra for better understanding of machine learning algorithms.
Browse courses on
Linear Algebra
Show steps
Show steps
Show steps
- Review core concepts of linear algebra.
- Solve practice problems to reinforce understanding.
- Consider taking an online course or workshop for further reinforcement.
Review supervised learning concepts
Show steps
Reinforce understanding of core supervised learning concepts by reviewing materials from previous courses. This will aid in building a deeper understanding as the course progresses.
Browse courses on
Regression
Show steps
Show steps
Show steps
- Revisit lecture notes and presentations from previous courses on regression and classification techniques.
- Review assignments and projects completed in previous courses related to supervised learning.
- Complete practice problems or quizzes to test understanding of core concepts.
Practice Regularization Techniques
Show steps
Strengthen your grasp of regularization by applying it to your machine learning models.
Browse courses on
Regularization
Show steps
Show steps
Show steps
- Implement L1 and L2 regularization in your model.
- Evaluate the impact of regularization on model performance.
- Optimize regularization parameters for different datasets.
Six other activities
Expand to see all activities and additional details
Show all nine activities
Implement Model Evaluation in Python
Show steps
Reinforce your understanding of model evaluation techniques by implementing them in Python.
Browse courses on
Model Evaluation
Show steps
Show steps
Show steps
- Create a dataset and a machine learning model.
- Calculate evaluation metrics for the model.
- Iterate through different model parameters and evaluate the impact on model performance.
Assist Peers in Model Evaluation
Show steps
Enhance your understanding of model evaluation by guiding others through the process.
Browse courses on
Model Evaluation
Show steps
Show steps
Show steps
- Identify opportunities to assist peers with model evaluation.
- Provide support and guidance on evaluation techniques.
- Review and provide feedback on peer's model evaluation results.
Explore Case Studies on Model Validation
Show steps
Deepen your knowledge of validation techniques and best practices by studying real-world case studies.
Browse courses on
Validation
Show steps
Show steps
Show steps
- Find case studies on model validation.
- Analyze the methodology and results of the case studies.
- Identify common patterns and lessons learned.
Attend a Model Validation Workshop
Show steps
Gain practical insights into model validation techniques by attending a workshop.
Browse courses on
Validation
Show steps
Show steps
Show steps
- Identify and register for a relevant workshop.
- Attend the workshop and actively participate.
- Apply the knowledge gained to your own modeling projects.
Develop a Model Evaluation Blog Post
Show steps
Solidify your understanding of model evaluation by explaining it in a blog post.
Browse courses on
Model Evaluation
Show steps
Show steps
Show steps
- Choose a specific model evaluation technique to focus on.
- Explain the methodology and benefits of the technique.
- Provide examples of how the technique can be applied.
- Write a draft and seek feedback from peers or mentors.
Build a Model Evaluation Dashboard
Show steps
Deepen your understanding of model evaluation by creating a dashboard to visualize and track model performance.
Browse courses on
Model Evaluation
Show steps
Show steps
Show steps
- Design the dashboard and identify the relevant metrics to track.
- Develop the dashboard using a suitable programming language and visualization library.
- Integrate the dashboard with your machine learning pipeline.
- Test and iterate on the dashboard to ensure its usability and effectiveness.
Career center
Learners who complete Meaningful Predictive Modeling will develop knowledge and skills
that may be useful to these careers:
Data Scientist
Data Scientist
Meaningful Predictive Modeling is designed for those pursuing a career in data science. This field involves developing and using models to make predictions about data, and this course provides the skills needed to build and evaluate these models. The course covers topics such as diagnostics for data, regularization, validation, and pipelines, which are all essential for data scientists who want to build accurate and reliable models.
Machine Learning Engineer
Machine Learning Engineer
Machine learning engineers are responsible for designing, developing, and deploying machine learning models. This course provides the skills needed to build and evaluate these models, including diagnostics for data, regularization, validation, and pipelines. These skills are essential for machine learning engineers who want to build accurate and reliable models.
Data Analyst
Data Analyst
Data analysts are responsible for collecting, cleaning, and analyzing data to identify trends and patterns. This course provides the skills needed to evaluate and compare different data models, which is essential for data analysts who want to make informed decisions about which models to use.
Statistician
Statistician
Statisticians are responsible for collecting, analyzing, and interpreting data. This course provides the skills needed to evaluate and compare different statistical models, which is essential for statisticians who want to make informed decisions about which models to use.
Business Analyst
Business Analyst
Business analysts are responsible for analyzing business data to identify opportunities and solve problems. This course provides the skills needed to evaluate and compare different data models, which is essential for business analysts who want to make informed decisions about which models to use.
Financial Analyst
Financial Analyst
Financial analysts are responsible for analyzing financial data to make investment decisions. This course provides the skills needed to evaluate and compare different financial models, which is essential for financial analysts who want to make informed decisions about which models to use.
Market Researcher
Market Researcher
Market researchers are responsible for collecting and analyzing data about consumer behavior. This course provides the skills needed to evaluate and compare different marketing models, which is essential for market researchers who want to make informed decisions about which models to use.
Operations Research Analyst
Operations Research Analyst
Operations research analysts are responsible for using data to improve the efficiency of organizations. This course provides the skills needed to evaluate and compare different operations research models, which is essential for operations research analysts who want to make informed decisions about which models to use.
Quantitative Analyst
Quantitative Analyst
Quantitative analysts are responsible for using data to make investment decisions. This course provides the skills needed to evaluate and compare different quantitative models, which is essential for quantitative analysts who want to make informed decisions about which models to use.
Risk Analyst
Risk Analyst
Risk analysts are responsible for identifying and managing risks. This course provides the skills needed to evaluate and compare different risk models, which is essential for risk analysts who want to make informed decisions about which models to use.
For more career information including salaries, visit:
OpenCourser.com/course/3f64fs/meaningful
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
Meaningful Predictive Modeling.
A comprehensive textbook that covers the fundamental concepts and methods of statistical learning. Provides a strong theoretical foundation for understanding and applying machine learning models.
Another comprehensive textbook that focuses on the theoretical foundations of statistical learning. It is well-suited for readers with a strong background in mathematics and statistics, and provides a detailed understanding of model selection and regularization techniques.
Save
A practical guide to building and evaluating predictive models. It covers a wide range of topics, including model selection, feature engineering, and performance evaluation. It is valuable for gaining a comprehensive understanding of model development and deployment.
Save
A classic textbook that covers a wide range of topics in machine learning, including supervised learning, unsupervised learning, and Bayesian methods. Provides a strong foundation in the theoretical and practical aspects of machine learning.
A practical guide to using popular machine learning libraries in Python, including Scikit-Learn, Keras, and TensorFlow. Provides hands-on experience with building and evaluating machine learning models.
A specialized resource that focuses on the important topic of feature engineering. It provides practical guidance and techniques for creating effective features that enhance model performance. It useful reference for developing a deeper understanding of this essential aspect of model development.
Provides a solid foundation in the statistical methods used in machine learning. It valuable resource for understanding the theoretical underpinnings of model evaluation and validation.
A practical guide to building and deploying machine learning models in production. Covers topics such as data preparation, feature engineering, model selection, and evaluation. Provides valuable insights into the real-world challenges of machine learning.
Save
A comprehensive textbook that covers the principles and practices of deep learning. Provides a detailed overview of deep learning architectures, algorithms, and applications.
Provides a critical perspective on model evaluation. It highlights the challenges and pitfalls of model assessment, and encourages a thoughtful approach to evaluating model performance. is valuable for developing a nuanced understanding of the limitations and strengths of model evaluation techniques.
Provides an in-depth exploration of interpretable machine learning techniques. It covers methods for understanding and explaining model predictions, and is valuable for building models that are both accurate and reliable.
A practical guide to building machine learning models from scratch using Python. Provides a gentle introduction to the concepts and techniques of machine learning.
Introduces the fundamental concepts of reinforcement learning. It valuable resource for understanding how to build models that can learn from their experiences and adapt to changing environments. While not directly focused on model evaluation, the book provides insights into the principles of learning and adaptation that are relevant to model assessment.
A collection of recipes and solutions for common problems encountered in machine learning. Provides practical guidance on how to apply machine learning techniques to real-world problems.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/3f64fs/meaningful
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Level
Intermediate
Via
Coursera
Institution
University of California San Diego
Instructors
Ilkay Altintas
Julian McAuley
Language
English
Transcripts
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آذربایجان دیلی
This course belongs to a
collection
called
Python Data Products for Predictive Analytics. It's comprised of four courses:
- Design Thinking and Predictive Analytics for Data Products
- Meaningful Predictive Modeling (viewing)
- Basic Data Processing and Visualization
- Deploying Machine Learning Models
Good to know
Develops techniques for regression and classification, which are fundamental skills for data analysis and machine learning
Taught by Ilkay Altintas and Julian McAuley, who are recognized for their work in machine learning and data analysis
Introduces diagnostic techniques for evaluating and comparing models, which is crucial for building effective machine learning systems
Examines performance measures that can be used in different regression and classification scenarios, providing learners with a comprehensive understanding of model evaluation
Covers the training/validation/test pipeline, an essential technique for ensuring models generalize well to new data
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