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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.
Read more
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.
Read more
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.
Register for this course and see more details by visiting:
OpenCourser.com/course/3uox00/model
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What's inside
Syllabus
Course Overview
Model Training and Selection with Train-test Splits
Model Training and Selection with Cross Validation
Good to know
Good to know
Know what's good ,
what to watch for , 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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Save Model Training: Best Practices for Data Practitioners to your list so you can find it easily later:
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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
Show steps
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
Show steps
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
Show steps
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
Show steps
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
Show steps
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
Show steps
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
Show steps
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
Show steps
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
Review dataset handling and preparation techniques
Show steps
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
Show steps
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
Show steps
Follow a guided tutorial to reinforce your understanding of model training using Scikit-Learn.
Browse courses on
scikit-learn
Show steps
Show steps
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
Show steps
Collaborate with peers to discuss concepts, share knowledge, and reinforce your understanding of model training.
Show steps
Show steps
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
Show steps
Test your understanding of model selection and evaluation by solving practice problems.
Browse courses on
Model Selection
Show steps
Show steps
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
Show steps
Reinforce your understanding of model training by working through hands-on coding problems.
Browse courses on
Model Training
Show steps
Show steps
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
Show steps
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
Show steps
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
Show steps
Deepen your knowledge and skills in model training by attending a specialized workshop.
Browse courses on
Model Training
Show steps
Show steps
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
Show steps
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
Show steps
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
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.
Business 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.
Operations Research Analyst
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.
Statistician
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.
Software Engineer
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.
Data Analyst
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.
Database Administrator
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.
Financial Analyst
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.
Risk 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.
Product Manager
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.
Data Engineer
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.
Quantitative Analyst
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.
Actuary
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.
Data Scientist
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.
Market Researcher
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.
For more career information including salaries, visit:
OpenCourser.com/course/3uox00/model
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.
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.
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.
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.
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.
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.
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.
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.
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.
Save
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.
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.
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.
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.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/3uox00/model
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Time
46 hours
Level
Introductory
Via
Pluralsight
Instructor
Axel Sirota
Language
English
Good to know
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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