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Model Training

Best Practices for Data Practitioners

Axel Sirota

Pre-trained models are used everywhere right now to add AI functionalities to products. But have you wondered how they are trained? This course will teach you from start to finish the process of going from an idea and a dataset to a trained model.

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Pre-trained models are used everywhere right now to add AI functionalities to products. But have you wondered how they are trained? This course will teach you from start to finish the process of going from an idea and a dataset to a trained model.

Machine Learning is everywhere. Everyday more AI capabilities are added to every product, so to keep up to date you need to master that skill. In this course, Model Training: Best Practices for Data Practitioners, you’ll gain the ability to train a model to solve such AI problems. First, you’ll explore the model lifecycle and data preparation strategies. Next, you’ll discover how to train and select models. Finally, you’ll learn how to keep up to date with trends. When you’re finished with this course, you’ll have the skills and knowledge of model training needed to add any AI feature to any product when needed.

This course is no longer available. Find something similar by browsing:
Model Training Data Preparation Machine Learning Train-Test Splits Cross Validation Model Selection AI

What's inside

Syllabus

Traffic lights

Read about what's good
what should give you pause
and possible dealbreakers
Suitable for students looking to master Machine Learning for data science work
Taught by industry professional, Axel Sirota, whose work includes Google AI and Udacity
Develops the foundational skills needed to add AI to products
Utilizes up-to-date practices and hands-on labs, ensuring learners are prepared for industry
Recommended to take prior courses in statistics and programming for a successful learning experience

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Reviews summary

Practical model training for data practitioners

According to learners, this course is a highly practical deep dive into model training best practices, providing actionable insights for data practitioners. Students appreciate the clear explanations of complex topics and the hands-on approach with real-world examples. While some found the pace occasionally challenging, the course generally offers a solid foundation for implementing AI features in products.
Provides a solid overview but some topics could use more in-depth exploration.
"It's a great introduction to best practices, but I wish there were deeper dives into specific algorithms."
"The course is comprehensive enough for a general understanding of model lifecycle, but not exhaustive."
"Could use more in-depth coverage on optimization techniques or specific model types."
Complex concepts are broken down into easy-to-understand modules.
"The instructor did an excellent job explaining difficult topics like cross-validation."
"I appreciated the logical flow of the lectures, making everything very clear."
"The course materials clarified many areas I previously found confusing in model training."
Provides practical strategies applicable to real-world data problems.
"I found the content directly applicable to my daily work as a data scientist."
"The hands-on coding and projects are the strongest part of the course for me, truly practical."
"I learned how to use practical tools and strategies that I could apply immediately to my work."
Some learners may find the pace challenging without prior ML knowledge.
"While comprehensive, I felt the course moved quite quickly, especially on advanced topics."
"This course is definitely for data practitioners, not beginners, as it assumes some prior ML experience."
"I struggled a bit with the speed in the later modules; perhaps more time on foundational concepts would help."

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 Training: Best Practices for Data Practitioners with these activities:
Review dataset handling and preparation techniques
Review key data handling and preparation techniques to ensure you have a solid foundation before starting the course.
Browse courses on Data Preparation
Show steps
  • Review basics of data cleaning techniques such as removing duplicates and handling missing values
  • Practice data transformation techniques such as feature scaling and encoding
  • Explore advanced data preparation techniques such as dimensionality reduction and feature engineering
Complete the 'Model Training with Scikit-Learn' tutorial
Follow a guided tutorial to reinforce your understanding of model training using Scikit-Learn.
Browse courses on scikit-learn
Show steps
  • Set up your environment with Scikit-Learn
  • Load and prepare your dataset
  • Train and evaluate your model using Scikit-Learn's API
Participate in a peer study group for model training
Collaborate with peers to discuss concepts, share knowledge, and reinforce your understanding of model training.
Show steps
  • Find a group of peers who are also taking the course
  • Schedule regular study sessions to discuss the course material
  • Take turns presenting concepts, leading discussions, and sharing resources
Five other activities
Expand to see all activities and additional details
Show all eight activities
Solve practice problems on model selection and evaluation
Test your understanding of model selection and evaluation by solving practice problems.
Browse courses on Model Selection
Show steps
  • Identify the appropriate evaluation metrics for your problem
  • Practice using cross-validation to select and evaluate models
  • Interpret and analyze model evaluation results
Solve hands-on coding problems on model training
Reinforce your understanding of model training by working through hands-on coding problems.
Browse courses on Model Training
Show steps
  • Identify coding problems related to model training
  • Implement solutions using appropriate programming languages and tools
  • Debug and refine your code to achieve optimal model performance
Create a visual guide to the model training process
Solidify your understanding of the model training process by creating a visual guide that explains the key steps.
Browse courses on Model Training
Show steps
  • Identify the key steps involved in model training
  • Create a visual representation of the process, such as a flowchart or diagram
  • Explain each step in detail, including the purpose and methodology
Attend a workshop on advanced model training techniques
Deepen your knowledge and skills in model training by attending a specialized workshop.
Browse courses on Model Training
Show steps
  • Research and identify relevant workshops on advanced model training techniques
  • Register and attend the workshop, actively participating in discussions and hands-on exercises
  • Apply the techniques learned in the workshop to enhance your model training projects
Contribute to open-source projects related to model training
Gain practical experience and contribute to the broader machine learning community by working on open-source projects related to model training.
Browse courses on Open Source
Show steps
  • Identify open-source projects that align with your interests and skill level
  • Contribute to the project by fixing bugs, adding features, or improving documentation
  • Engage with the project community through discussions and code reviews

Career center

Learners who complete Model Training: Best Practices for Data Practitioners will develop knowledge and skills that may be useful to these careers:
Machine Learning Engineer
A Machine Learning Engineer designs, develops, and deploys machine learning models to solve real-world problems. This course, Model Training: Best Practices for Data Practitioners, would be a valuable asset to a Machine Learning Engineer because it teaches the process of training and selecting models, which is a core skill for Machine Learning Engineers. Additionally, the course covers model lifecycle and data preparation strategies, which are both important for Machine Learning Engineers to understand.
See salaries and explore the career path for Machine Learning Engineer
Data Analyst
A Data Analyst collects, processes, and analyzes data to help businesses make informed decisions. This course, Model Training: Best Practices for Data Practitioners, may be useful to a Data Analyst because it teaches the process of going from an idea and a dataset to a trained model, which can be used to analyze data and make predictions. Additionally, the course covers model lifecycle and data preparation strategies, which are both important for Data Analysts to understand.
See salaries and explore the career path for Data Analyst
Business Analyst
A Business Analyst helps businesses identify and solve problems by analyzing data and making recommendations. This course, Model Training: Best Practices for Data Practitioners, may be useful to a Business Analyst because it teaches the process of going from an idea and a dataset to a trained model, which can be used to analyze data and make predictions. Additionally, the course covers model lifecycle and data preparation strategies, which are both important for Business Analysts to understand.
See salaries and explore the career path for Business Analyst
Software Engineer
A Software Engineer designs, develops, and maintains software applications. This course, Model Training: Best Practices for Data Practitioners, may be useful to a Software Engineer because it teaches the process of going from an idea and a dataset to a trained model, which can be used to add AI features to software applications. Additionally, the course covers model lifecycle and data preparation strategies, which are both important for Software Engineers to understand.
See salaries and explore the career path for Software Engineer
Product Manager
A Product Manager is responsible for the development and launch of new products. This course, Model Training: Best Practices for Data Practitioners, may be useful to a Product Manager because it teaches the process of going from an idea and a dataset to a trained model, which can be used to develop and launch new AI-powered products. Additionally, the course covers model lifecycle and data preparation strategies, which are both important for Product Managers to understand.
See salaries and explore the career path for Product Manager
Quantitative Analyst
A Quantitative Analyst uses mathematical and statistical techniques to analyze data and make predictions. This course, Model Training: Best Practices for Data Practitioners, may be useful to a Quantitative Analyst because it teaches the process of going from an idea and a dataset to a trained model, which can be used to analyze data and make predictions. Additionally, the course covers model lifecycle and data preparation strategies, which are both important for Quantitative Analysts to understand.
See salaries and explore the career path for Quantitative Analyst
Statistician
A Statistician collects, analyzes, and interprets data to help businesses make informed decisions. This course, Model Training: Best Practices for Data Practitioners, may be useful to a Statistician because it teaches the process of going from an idea and a dataset to a trained model, which can be used to analyze data and make predictions. Additionally, the course covers model lifecycle and data preparation strategies, which are both important for Statisticians to understand.
See salaries and explore the career path for Statistician
Market Researcher
A Market Researcher conducts research to help businesses understand their customers and markets. This course, Model Training: Best Practices for Data Practitioners, may be useful to a Market Researcher because it teaches the process of going from an idea and a dataset to a trained model, which can be used to analyze data and make predictions about customers and markets. Additionally, the course covers model lifecycle and data preparation strategies, which are both important for Market Researchers to understand.
See salaries and explore the career path for Market Researcher
Financial Analyst
A Financial Analyst analyzes financial data to help businesses make informed decisions. This course, Model Training: Best Practices for Data Practitioners, may be useful to a Financial Analyst because it teaches the process of going from an idea and a dataset to a trained model, which can be used to analyze financial data and make predictions. Additionally, the course covers model lifecycle and data preparation strategies, which are both important for Financial Analysts to understand.
See salaries and explore the career path for Financial Analyst
Data Engineer
A Data Engineer builds and maintains data pipelines to ensure that data is available for analysis. This course, Model Training: Best Practices for Data Practitioners, may be useful to a Data Engineer because it teaches the process of going from an idea and a dataset to a trained model, which is a core skill for Data Engineers. Additionally, the course covers model lifecycle and data preparation strategies, which are both important for Data Engineers to understand.
See salaries and explore the career path for Data Engineer
Database Administrator
A Database Administrator manages and maintains databases to ensure that data is available for analysis. This course, Model Training: Best Practices for Data Practitioners, may be useful to a Database Administrator because it teaches the process of going from an idea and a dataset to a trained model, which can be used to analyze data and make predictions. Additionally, the course covers model lifecycle and data preparation strategies, which are both important for Database Administrators to understand.
See salaries and explore the career path for Database Administrator
Operations Research Analyst
An Operations Research Analyst uses mathematical and statistical techniques to solve business problems. This course, Model Training: Best Practices for Data Practitioners, may be useful to an Operations Research Analyst because it teaches the process of going from an idea and a dataset to a trained model, which can be used to analyze data and make predictions. Additionally, the course covers model lifecycle and data preparation strategies, which are both important for Operations Research Analysts to understand.
See salaries and explore the career path for Operations Research Analyst
Risk Analyst
A Risk Analyst analyzes risk to help businesses make informed decisions. This course, Model Training: Best Practices for Data Practitioners, may be useful to a Risk Analyst because it teaches the process of going from an idea and a dataset to a trained model, which can be used to analyze data and make predictions. Additionally, the course covers model lifecycle and data preparation strategies, which are both important for Risk Analysts to understand.
See salaries and explore the career path for Risk Analyst
Data Scientist
A Data Scientist is responsible for extracting knowledge from data using scientific methods, algorithms, and machine learning techniques. This course, Model Training: Best Practices for Data Practitioners, may be useful to a Data Scientist because it teaches the process of going from an idea and a dataset to a trained model, which is a core skill for Data Scientists. Additionally, the course covers model lifecycle and data preparation strategies, which are both important for Data Scientists to understand.
See salaries and explore the career path for Data Scientist
Actuary
An Actuary uses mathematical and statistical techniques to assess risk and uncertainty. This course, Model Training: Best Practices for Data Practitioners, may be useful to an Actuary because it teaches the process of going from an idea and a dataset to a trained model, which can be used to analyze data and make predictions. Additionally, the course covers model lifecycle and data preparation strategies, which are both important for Actuaries to understand.
See salaries and explore the career path for Actuary

Reading list

We've selected 12 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 Training: Best Practices for Data Practitioners.
Cover image
Hands-On Machine Learning with Scikit-Learn, Keras,...
Save
Provides a thorough introduction to machine learning concepts and techniques, using popular libraries like Scikit-Learn, Keras, and TensorFlow. It covers all aspects of the model training process, from data preparation to model evaluation and deployment.
Hands-On Machine Learning with Scikit-Learn, Keras,...
Paperback
$$
Hands-On Machine Learning with Scikit-Learn, Keras,...
Paperback
$$$
Hands-On Machine Learning with Scikit-Learn, Keras,...
Kindle Edition
$$
Cover image
Deep Learning
Save
Classic introduction to deep learning. It covers the fundamental concepts of deep learning, as well as the latest advances in the field. It must-read for anyone who wants to learn about deep learning.
Deep Learning
Paperback
$$$
Deep Learning (Adaptive Computation and Machine...
Hardcover
$$$
Deep Learning (Adaptive Computation and Machine...
Kindle Edition
$$$
Cover image
Probabilistic Machine Learning
Save
Provides a rigorous introduction to machine learning from a probabilistic perspective. It covers a wide range of topics, including Bayesian inference, graphical models, and reinforcement learning. It valuable resource for anyone who wants to gain a deeper understanding of the theoretical foundations of machine learning.
Probabilistic Machine Learning: Advanced Topics...
Hardcover
$$$$
Cover image
The Elements of Statistical Learning
Save
Classic introduction to statistical learning. It covers a wide range of topics, including linear regression, logistic regression, and support vector machines. It valuable resource for anyone who wants to gain a deeper understanding of the statistical foundations of machine learning.
The Elements of Statistical Learning: Data Mining,...
Hardcover
$$$
The Elements of Statistical Learning: Data Mining,...
Hardcover
$$$
The Elements of Statistical Learning: Data Mining,...
Kindle Edition
$$$
Pattern Recognition and Machine Learning
Save
Provides a comprehensive overview of pattern recognition and machine learning. It covers a wide range of topics, including supervised learning, unsupervised learning, and Bayesian inference. It valuable resource for anyone who wants to gain a deeper understanding of the field.
Pattern Recognition and Machine Learning...
Hardcover
$$$
Pattern Recognition and Machine Learning...
Paperback
$$$
Pattern Recognition and Machine Learning (text...
Unknown Binding
$$$$
Cover image
Machine Learning for Hackers
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Provides a practical introduction to machine learning for people with no prior experience in the field. It covers a wide range of topics, including data preparation, model training, and model evaluation. It valuable resource for anyone who wants to get started with machine learning.
Machine Learning for Hackers: Case Studies and...
Paperback
$$
Machine Learning for Hackers: Case Studies and...
Kindle Edition
$$
Cover image
Machine Learning with R
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Provides a comprehensive introduction to machine learning using the R programming language. It covers a wide range of topics, including data preparation, model training, and model evaluation. It valuable resource for anyone who wants to use R for machine learning.
Machine Learning with R - Fourth Edition: Learn...
Paperback
$$
Machine Learning with R - Second Edition: Expert...
Kindle Edition
$$
Machine Learning with R - Third Edition: Expert...
Paperback
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(Italiano) Machine Learning con R: Conoscere le tecniche per...
Kindle Edition
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R: Unleash Machine Learning Techniques
Kindle Edition
$$$
Machine Learning with R: Learn How to Use R to...
Paperback
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(中文) Machine learning and R language (3rd edition of the...
Paperback
$$
Cover image
Machine Learning Q and AI
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Provides a comprehensive introduction to machine learning using the Python programming language. It covers a wide range of topics, including data preparation, model training, and model evaluation. It valuable resource for anyone who wants to use Python for machine learning.
Machine Learning Q and AI: 30 Essential Questions...
Paperback
$$
Machine Learning Q and AI: 30 Essential Questions...
Kindle Edition
$$
Cover image
Granular Computing
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Provides a comprehensive introduction to machine learning using the Java programming language. It covers a wide range of topics, including data preparation, model training, and model evaluation. It valuable resource for anyone who wants to use Java for machine learning.
Granular Computing: An Introduction (The Springer...
Paperback
$$$$
Granular Computing: An Introduction (The Springer...
Kindle Edition
$$$$
Cover image
Learning Image Processing with OpenCV
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Provides a comprehensive introduction to machine learning using the C++ programming language. It covers a wide range of topics, including data preparation, model training, and model evaluation. It valuable resource for anyone who wants to use C++ for machine learning.
Learning Image Processing with OpenCV
Paperback
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Learning Image Processing with OpenCV
Kindle Edition
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Cover image
Machine Learning With Go
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Provides a comprehensive introduction to machine learning using the Go programming language. It covers a wide range of topics, including data preparation, model training, and model evaluation. It valuable resource for anyone who wants to use Go for machine learning.
Machine Learning With Go
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Machine Learning With Go
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The Rust Programming Language, 2nd Edition
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Provides a comprehensive introduction to machine learning using the Rust programming language. It covers a wide range of topics, including data preparation, model training, and model evaluation. It valuable resource for anyone who wants to use Rust for machine learning.
The Rust Programming Language, 2nd Edition
Paperback
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The Rust Programming Language, 2nd Edition
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Course image
Time
46 hours
Level
Introductory
Via
Pluralsight
Instructor
Axel Sirota
Language
English
Traffic lights
Read about what's good
what should give you pause
and possible dealbreakers
Suitable for students looking to master Machine Learning for data science work
Taught by industry professional, Axel Sirota, whose work includes Google AI and Udacity
Develops the foundational skills needed to add AI to products
Utilizes up-to-date practices and hands-on labs, ensuring learners are prepared for industry
Recommended to take prior courses in statistics and programming for a successful learning experience
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