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Model Building and Evaluation for Data Scientists
Daniel Stern
Daniel Stern
Building and evaluating machine learning (ML) models is daunting, but correctly engineered models can provide millions of dollars in value. In this course, you'll learn to build and evaluate these tools, leveraging existing data science knowledge.
Read more
Building and evaluating machine learning (ML) models is daunting, but correctly engineered models can provide millions of dollars in value. In this course, you'll learn to build and evaluate these tools, leveraging existing data science knowledge.
Read more
Building and evaluating machine learning (ML) models is daunting, but correctly engineered models can provide millions of dollars in value. In this course, you'll learn to build and evaluate these tools, leveraging existing data science knowledge.
Building and evaluating machine learning (ML) models unlocks a myriad of rewarding business opportunities for organizations that are able to do so effectively. But what skills do data scientists, with a strong understanding of statistics, data warehousing, and data analysis, need to master before they can create an effective ML model?
In this course, Model Building and Evaluation for Data Scientists, you’ll learn a solid foundation of model building and evaluation fundamentals, with the core skills needed to begin building and deploying your own models.
First, you’ll learn to match different kinds of datasets and business success requirements to the model type that is best suited to make inferences from that data or achieve that goal.
Next, you’ll explore advanced data processing and preparation techniques, such as feature engineering and continuous data pipelines, which can all be used to improve model performance and outcomes.
Finally, you’ll discover how to evaluate models, understand evaluation metrics, and adjust data and model training pipelines to optimize performance.
When you’re finished with this course, you’ll have the skills of ML model building and evaluation needed to train and evaluate models of several different types and improve their performance, and the knowledge to continue learning more ML skills.
Register for this course and see more details by visiting:
OpenCourser.com/course/nu03xu/model
What's inside
Syllabus
Course Overview
Turning Data into Effective ML Models
Evaluating and Optimizing ML Models
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Develops model building and evaluation fundamentals, which are core skills for data scientists
Taught by Daniel Stern, who are recognized for their work in data science
Teaches skills, knowledge, and/or tools that are highly relevant in an academic setting
Examines advanced data processing and preparation techniques, such as feature engineering and continuous data pipelines, which are standard in industry
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Model Building and Evaluation for Data Scientists.
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Career center
Learners who complete Model Building and Evaluation for Data Scientists will develop knowledge and skills
that may be useful to these careers:
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers are the professionals responsible for the design, development, deployment, monitoring, and maintenance of machine learning models. A solid grasp of model building and evaluation fundamentals, as well as experience with advanced data processing and preparation techniques, such as feature engineering and continuous data pipelines, are critical to success in this role. This course helps build a foundation in these areas and provides the hands-on experience needed to excel as a Machine Learning Engineer.
Data Scientist
Data Scientist
Data Scientists use their expertise in statistics, data warehousing, and data analysis to build and evaluate machine learning models. This course provides the advanced skills needed for a Data Scientist to design and implement effective ML models for a variety of business applications. From matching datasets and business requirements to the best model type, to optimizing model performance, this course provides crucial knowledge for career advancement.
Data Analyst
Data Analyst
Data Analysts play a key role in understanding and communicating data insights to stakeholders. The ability to evaluate models and understand evaluation metrics is essential for Data Analysts to effectively communicate the strengths and limitations of machine learning models to non-technical audiences. This course helps Data Analysts bridge the gap between model building and business impact, enhancing their ability to make data-driven recommendations.
Business Analyst
Business Analyst
Business Analysts help organizations identify and solve business problems. They use data analysis and modeling to understand business needs and develop solutions that leverage machine learning technology. This course provides Business Analysts with the knowledge and skills to evaluate and optimize ML models, enabling them to make better data-driven decisions and drive business value.
Software Engineer
Software Engineer
Software Engineers are responsible for the design, development, and maintenance of software systems. Machine learning is increasingly used to enhance software applications, and Software Engineers need to understand how to integrate ML models into their software solutions. This course provides a solid foundation in model building and evaluation, helping Software Engineers develop robust and reliable ML-powered software.
Statistician
Statistician
Statisticians use statistical methods to collect, analyze, interpret, and present data. Machine learning models are becoming increasingly important in statistical analysis, and Statisticians need to understand how to build and evaluate these models. This course provides Statisticians with the necessary skills to apply machine learning techniques to their work and enhance their ability to derive meaningful insights from data.
Research Scientist
Research Scientist
Research Scientists conduct research to advance scientific knowledge and develop new technologies. Machine learning is used in a wide range of research areas, and Research Scientists need to understand how to build and evaluate ML models to support their research. This course provides a solid foundation in model building and evaluation, helping Research Scientists leverage ML techniques to make significant contributions to their fields of study.
Quantitative Analyst
Quantitative Analyst
Quantitative Analysts use mathematical and statistical methods to analyze financial data and make investment decisions. Machine learning models are increasingly used in quantitative analysis, and Quantitative Analysts need to understand how to build and evaluate these models. This course provides a solid foundation in model building and evaluation, helping Quantitative Analysts make more informed investment decisions and enhance their risk management strategies.
Marketing Analyst
Marketing Analyst
Marketing Analysts use data analysis to understand customer behavior and develop marketing strategies. Machine learning models are becoming increasingly important in marketing analysis, and Marketing Analysts need to understand how to build and evaluate these models. This course provides a solid foundation in model building and evaluation, helping Marketing Analysts leverage ML techniques to create more effective marketing campaigns and drive business growth.
Product Manager
Product Manager
Product Managers are responsible for the development and management of products. Machine learning models are increasingly used to improve product functionality and user experience. This course provides Product Managers with the knowledge and skills to understand how ML models work, evaluate their performance, and make data-driven decisions about product development.
Operations Research Analyst
Operations Research Analyst
Operations Research Analysts use mathematical and analytical methods to solve business problems. Machine learning models are increasingly used in operations research, and Operations Research Analysts need to understand how to build and evaluate these models. This course provides a solid foundation in model building and evaluation, helping Operations Research Analysts develop more efficient and effective solutions to business problems.
Financial Analyst
Financial Analyst
Financial Analysts use data analysis to make investment decisions. Machine learning models are increasingly used in financial analysis, and Financial Analysts need to understand how to build and evaluate these models. This course provides a solid foundation in model building and evaluation, helping Financial Analysts make more informed investment decisions and enhance their risk management strategies.
Risk Manager
Risk Manager
Risk Managers are responsible for identifying and managing risks to an organization. Machine learning models are increasingly used in risk management, and Risk Managers need to understand how to build and evaluate these models. This course provides a solid foundation in model building and evaluation, helping Risk Managers make more informed decisions and develop more effective risk management strategies.
Actuary
Actuary
Actuaries use mathematical and statistical methods to assess risk and uncertainty. Machine learning models are increasingly used in actuarial science, and Actuaries need to understand how to build and evaluate these models. This course provides a solid foundation in model building and evaluation, helping Actuaries develop more accurate and reliable risk assessments.
Data Engineer
Data Engineer
Data Engineers are responsible for designing and managing data systems. Machine learning models are increasingly used in data engineering, and Data Engineers need to understand how to build and evaluate these models. This course provides a solid foundation in model building and evaluation, helping Data Engineers develop more efficient and effective data systems.
For more career information including salaries, visit:
OpenCourser.com/course/nu03xu/model
Reading list
We've selected 16 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
Model Building and Evaluation for Data Scientists.
A comprehensive guide to building machine learning models using popular Python libraries. Provides detailed explanations and code examples, and is valuable as a primary reference or additional reading.
Provides a practical introduction to statistical learning methods using R. Covers a wide range of models and techniques, and is useful as a complementary reference.
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Provides a comprehensive overview of the fundamentals of deep learning, including model building and evaluation. It is particularly useful for those with a background in mathematics and computer science.
Provides a comprehensive overview of machine learning for data science. Covers a wide range of models and techniques, and is valuable as a primary reference or additional reading.
Provides a step-by-step guide to building machine learning models using R. Covers a wide range of models and techniques, and is useful as a complementary reference.
Provides a code-heavy introduction to deep learning using Fastai and PyTorch. Useful for practitioners who want to quickly get started with building and deploying deep learning models.
Provides a probabilistic approach to machine learning, including model building and evaluation. It is particularly useful for those with a background in statistics and probability.
Provides a beginner-friendly introduction to machine learning concepts and algorithms. Useful for gaining a basic understanding of the field.
Provides a comprehensive overview of the fundamentals of machine learning, including model building and evaluation. It is particularly useful for those with a background in mathematics and computer science.
Provides a comprehensive overview of the fundamentals of statistical learning, including model building and evaluation. It is particularly useful for those with a background in statistics.
Provides a practical guide to machine learning, including model building and evaluation. It is particularly useful for those who want to apply machine learning to real-world problems.
Provides a practical guide to machine learning for business users.
Provides a simplified overview of machine learning for readers who are new to the topic.
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Provides a gentle introduction to machine learning for beginners.
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Provides advanced readers with a systems perspective on machine learning.
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Provides an algorithmic perspective on machine learning, making it more accessible to computer scientists than those from other backgrounds.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/nu03xu/model
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Time
28 hours
Level
Intermediate
Via
Pluralsight
Instructor
Daniel Stern
Language
English
Good to know
Develops model building and evaluation fundamentals, which are core skills for data scientists
Taught by Daniel Stern, who are recognized for their work in data science
Teaches skills, knowledge, and/or tools that are highly relevant in an academic setting
Examines advanced data processing and preparation techniques, such as feature engineering and continuous data pipelines, which are standard in industry
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