Model Building and Evaluation for Data Scientists from Pluralsight

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.

What's inside

Syllabus

Course Overview

Turning Data into Effective ML Models

Evaluating and Optimizing ML Models

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

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 Model Building and Evaluation for Data Scientists with these activities:

Read 'Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow'

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This book provides a comprehensive overview of machine learning concepts and techniques, which will complement the course material.

View Hands-On Machine Learning with Scikit-Learn,... on Amazon

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Read the chapters that cover the topics introduced in the course.
Work through the code examples provided in the book.
Apply the concepts learned from the book to your own machine learning projects.

Review Linear Discriminant Analysis

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LDA is introduced later in the course, but it will be helpful to brush up on the basics to accelerate your learning.

Browse courses on Discriminant Analysis

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Explain the basic assumptions of linear discriminant analysis.
Describe the process of fitting a linear discriminant analysis model.
Evaluate the performance of a linear discriminant analysis model.

Organize a Study Group for Model Evaluation

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Peer learning can enhance your understanding of model evaluation methods and help you identify areas where you need additional support.

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Find a group of classmates who are interested in forming a study group.
Establish regular meeting times and a schedule for covering different topics.
Take turns presenting different model evaluation techniques.
Discuss and critique each other's presentations.
Work together to solve problems and answer questions related to model evaluation.

Four other activities

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Walkthrough a Machine Learning Pipeline in Python

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The course covers machine learning pipelines, but this tutorial will provide additional hands-on experience in building and evaluating a pipeline in Python.

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Set up a Python environment for machine learning.
Acquire and load the necessary data.
Perform data preprocessing, including cleaning, feature engineering, and scaling.
Split the data into training and testing sets.
Train and evaluate a machine learning model.

Complete Kaggle Challenges for Model Building

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Kaggle challenges provide a practical way to apply model building skills and receive feedback from the community.

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Identify a Kaggle challenge that aligns with your interests and skill level.
Explore the data provided by the challenge.
Build and train machine learning models using the data.
Evaluate and refine your models based on the challenge metrics.
Submit your final model and receive feedback from the Kaggle community.

Develop a Machine Learning-Based Recommendation System

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Building a recommendation system will provide practical experience in applying machine learning techniques to solve a real-world problem.

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Gather and prepare a dataset for recommendation modeling.
Explore different machine learning algorithms for building recommendation models.
Implement and evaluate the recommendation system.
Deploy and monitor the recommendation system.

Create a Data Science Portfolio of Machine Learning Projects

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Creating a portfolio will showcase your skills in model building and evaluation and help you stand out in job applications.

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Develop a portfolio website or online presence.
Create case studies that describe your machine learning projects.
Include sections for your resume, LinkedIn profile, and other relevant materials.
Share your portfolio with potential employers and recruiters.
Update your portfolio regularly with new projects and accomplishments.

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 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.

See salaries and explore the career path for Machine Learning Engineer

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.

See salaries and explore the career path for Data Scientist

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.

See salaries and explore the career path for Data 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.

See salaries and explore the career path for Business Analyst

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.

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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