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Trent McMillan
This course will teach you how to use MLBox for quickly building high quality automated machine learning models.
Read more
This course will teach you how to use MLBox for quickly building high quality automated machine learning models.
Read more
This course will teach you how to use MLBox for quickly building high quality automated machine learning models.
Building high-performing machine learning models typically requires a lot of time, effort, and specialized knowledge. In this course, Getting Started with MLBox, you’ll learn to build dominant machine learning models in a fraction of the time by using an automated machine learning library. First, you’ll explore MLBox’s automated preprocessing and feature selection components. Next, you’ll discover automated algorithm selection for classification and regression problems. Finally, you’ll learn how to optimize your model with automatic hyper-parameter tuning and learn how to interpret the results. When you’re finished with this course, you’ll have the skills and knowledge needed to quickly create high performing ML models with MLBox.
Register for this course and see more details by visiting:
OpenCourser.com/course/u8pls7/getting
What's inside
Syllabus
Course Overview
Getting Started
Data Preprocessing
Model Building & Optimization
Read more
Syllabus
Course Overview
Getting Started
Data Preprocessing
Model Building & Optimization
Read more
Summary
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Covers a niche topic that may not immediately stand out to learners but will be highly valuable for those who work in that area
Taught by Trent McMillan, who are recognized for their work in the field of machine learning and data science
Save this course
Save Getting Started with MLBox 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 Getting Started with MLBox with these
activities:
Identify MLBox Experts for Guidance
Show steps
Enhance your learning by seeking guidance from experienced MLBox professionals. Connect with mentors through online platforms or networking events.
Show steps
-
Research MLBox experts in your industry
-
Reach out and request mentorship
-
Schedule regular touchpoints
Explore MLBox Documentation
Show steps
Familiarize yourself with MLBox's functionality and capabilities by thoroughly reviewing its documentation.
Show steps
-
Navigate the MLBox documentation website
-
Read through the tutorials and examples
-
Identify relevant sections for future reference
Review Python Basics
Show steps
Ensure students have a solid foundation in Python, facilitating their understanding of MLBox's Python-based interface.
Browse courses on
Python
Show steps
-
Review data types and variables
-
Practice data manipulation
-
Refresh control flow and loops
11 other activities
Expand to see all activities and additional details
Show all 14 activities
Review Machine Learning Concepts
Show steps
Strengthen students' understanding of fundamental ML concepts, preparing them for the course's more advanced topics.
Show steps
-
Revisit key concepts and algorithms
-
Review model evaluation metrics
-
Explore different ML applications
Practice Data Preprocessing Techniques
Show steps
Reinforce your understanding of data preprocessing by completing exercises that involve cleaning, transforming, and preparing data for ML models.
Browse courses on
Data Preprocessing
Show steps
-
Load a dataset and identify missing values
-
Handle missing values using different techniques
-
Apply data transformation methods
Discuss MLBox Project Ideas
Show steps
Foster collaboration and idea sharing among students, encouraging them to apply their MLBox knowledge to meaningful projects.
Browse courses on
Machine Learning Projects
Show steps
-
Present project ideas
-
Provide feedback and suggestions
-
Collaborate on project development
Practice MLBox Commands
Show steps
Help students become familiar with the MLBox interface and common commands, improving their ability to apply MLBox effectively in the course.
Browse courses on
MLBox
Show steps
-
Access MLBox documentation
-
Review command list
-
Practice basic commands
-
Create a sample project
Feature Engineering Exercises
Show steps
Reinforce understanding of feature engineering concepts and techniques, enhancing students' ability to prepare data for ML models.
Browse courses on
Feature Engineering
Show steps
-
Identify relevant features
-
Transform and scale features
-
Create dummy variables
-
Handle missing values
Develop an ML Model Using MLBox
Show steps
Apply your MLBox skills by creating an end-to-end ML project, defining the problem, preparing data, selecting algorithms, and evaluating the model.
Show steps
-
Choose a real-world problem to solve
-
Collect and prepare the necessary data
-
Build and train an ML model using MLBox
-
Evaluate the model's performance
Explore MLBox Optimization Techniques
Show steps
Enhance students' understanding of optimization techniques in MLBox, empowering them to improve model performance.
Browse courses on
Hyperparameter Tuning
Show steps
-
Review optimization algorithms
-
Apply hyperparameter tuning
-
Interpret optimization results
Build a Classification Model with MLBox
Show steps
Provide hands-on experience in building a complete ML model using MLBox, fostering understanding of the end-to-end process.
Show steps
-
Define project goals
-
Load and preprocess data
-
Train and evaluate models
-
Deploy the model
Deploy a Machine Learning Model with MLBox
Show steps
Develop students' ability to deploy ML models using MLBox, enabling them to apply their models in real-world scenarios.
Browse courses on
Machine Learning Deployment
Show steps
-
Select deployment platform
-
Prepare model for deployment
-
Deploy model and monitor performance
Discuss MLBox Implementation Strategies
Show steps
Deepen your understanding of MLBox by sharing and discussing implementation strategies and approaches with peers in a collaborative setting.
Show steps
-
Join or create a study group
-
Present your own MLBox implementation approach
-
Discuss and analyze alternative approaches
Assist Others in Learning MLBox
Show steps
Solidify your MLBox knowledge and reinforce understanding by helping others learn. Engage in peer mentorship or Q&A forums.
Show steps
-
Identify opportunities to provide guidance
-
Answer questions and provide support
-
Collaborate on MLBox projects
-
Share your experiences and insights
Identify MLBox Experts for Guidance
Show steps
Enhance your learning by seeking guidance from experienced MLBox professionals. Connect with mentors through online platforms or networking events.
Show steps
Show steps
Show steps
- Research MLBox experts in your industry
- Reach out and request mentorship
- Schedule regular touchpoints
Explore MLBox Documentation
Show steps
Familiarize yourself with MLBox's functionality and capabilities by thoroughly reviewing its documentation.
Show steps
Show steps
Show steps
- Navigate the MLBox documentation website
- Read through the tutorials and examples
- Identify relevant sections for future reference
Review Python Basics
Show steps
Ensure students have a solid foundation in Python, facilitating their understanding of MLBox's Python-based interface.
Browse courses on
Python
Show steps
Show steps
Show steps
- Review data types and variables
- Practice data manipulation
- Refresh control flow and loops
11 other activities
Expand to see all activities and additional details
Show all 14 activities
Review Machine Learning Concepts
Show steps
Strengthen students' understanding of fundamental ML concepts, preparing them for the course's more advanced topics.
Show steps
Show steps
Show steps
- Revisit key concepts and algorithms
- Review model evaluation metrics
- Explore different ML applications
Practice Data Preprocessing Techniques
Show steps
Reinforce your understanding of data preprocessing by completing exercises that involve cleaning, transforming, and preparing data for ML models.
Browse courses on
Data Preprocessing
Show steps
Show steps
Show steps
- Load a dataset and identify missing values
- Handle missing values using different techniques
- Apply data transformation methods
Discuss MLBox Project Ideas
Show steps
Foster collaboration and idea sharing among students, encouraging them to apply their MLBox knowledge to meaningful projects.
Browse courses on
Machine Learning Projects
Show steps
Show steps
Show steps
- Present project ideas
- Provide feedback and suggestions
- Collaborate on project development
Practice MLBox Commands
Show steps
Help students become familiar with the MLBox interface and common commands, improving their ability to apply MLBox effectively in the course.
Browse courses on
MLBox
Show steps
Show steps
Show steps
- Access MLBox documentation
- Review command list
- Practice basic commands
- Create a sample project
Feature Engineering Exercises
Show steps
Reinforce understanding of feature engineering concepts and techniques, enhancing students' ability to prepare data for ML models.
Browse courses on
Feature Engineering
Show steps
Show steps
Show steps
- Identify relevant features
- Transform and scale features
- Create dummy variables
- Handle missing values
Develop an ML Model Using MLBox
Show steps
Apply your MLBox skills by creating an end-to-end ML project, defining the problem, preparing data, selecting algorithms, and evaluating the model.
Show steps
Show steps
Show steps
- Choose a real-world problem to solve
- Collect and prepare the necessary data
- Build and train an ML model using MLBox
- Evaluate the model's performance
Explore MLBox Optimization Techniques
Show steps
Enhance students' understanding of optimization techniques in MLBox, empowering them to improve model performance.
Browse courses on
Hyperparameter Tuning
Show steps
Show steps
Show steps
- Review optimization algorithms
- Apply hyperparameter tuning
- Interpret optimization results
Build a Classification Model with MLBox
Show steps
Provide hands-on experience in building a complete ML model using MLBox, fostering understanding of the end-to-end process.
Show steps
Show steps
Show steps
- Define project goals
- Load and preprocess data
- Train and evaluate models
- Deploy the model
Deploy a Machine Learning Model with MLBox
Show steps
Develop students' ability to deploy ML models using MLBox, enabling them to apply their models in real-world scenarios.
Browse courses on
Machine Learning Deployment
Show steps
Show steps
Show steps
- Select deployment platform
- Prepare model for deployment
- Deploy model and monitor performance
Discuss MLBox Implementation Strategies
Show steps
Deepen your understanding of MLBox by sharing and discussing implementation strategies and approaches with peers in a collaborative setting.
Show steps
Show steps
Show steps
- Join or create a study group
- Present your own MLBox implementation approach
- Discuss and analyze alternative approaches
Assist Others in Learning MLBox
Show steps
Solidify your MLBox knowledge and reinforce understanding by helping others learn. Engage in peer mentorship or Q&A forums.
Show steps
Show steps
Show steps
- Identify opportunities to provide guidance
- Answer questions and provide support
- Collaborate on MLBox projects
- Share your experiences and insights
Career center
Learners who complete Getting Started with MLBox will develop knowledge and skills
that may be useful to these careers:
Data Science Manager
Data Science Manager
As a Data Science Manager, you lead teams of data professionals across the data science lifecycle. This role combines technical abilities with managerial prowess, as you will need expertise in machine learning to lead model building, oversee feature engineering, and manage data wrangling. MLBox can help you succeed as a Data Science Manager by providing a foundation in the automation of building high-quality machine learning models, allowing you to spend less time on manual tasks and more time leading your team to achieve success.
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers develop, deploy, and maintain machine learning models. You will need a strong foundation in machine learning to succeed in this role, as you will be responsible for the end-to-end lifecycle of machine learning projects. MLBox can help by providing a solid foundation in automated machine learning model building, which will allow you to focus on other aspects of the machine learning lifecycle.
Data Scientist
Data Scientist
Data Scientists leverage machine learning, statistical analysis, and programming to extract insights from data. If you want to become a Data Scientist, you will need a strong background in machine learning, and MLBox can help you achieve that by providing a foundation in automated machine learning model creation.
Business Intelligence Analyst
Business Intelligence Analyst
Business Intelligence Analysts use data to gain insights into a company's performance and identify areas for improvement. To do this, you will need expertise in machine learning to understand and apply machine learning models to business problems. MLBox can help you succeed by providing a foundation in automated machine learning model building, which will help you quickly and easily build models to solve business problems.
Software Engineer
Software Engineer
Software Engineers design, develop, and maintain software systems. While the focus is on software development, having a foundation in machine learning is becoming increasingly important, as more and more software systems incorporate machine learning models. By taking MLBox, you can develop a solid foundation in the automation of machine learning model creation, which will set you apart from other candidates and help you succeed as a Software Engineer.
Data Analyst
Data Analyst
Data Analysts collect, clean, and analyze data to provide insights for decision-making. To succeed in this role, you will need a strong foundation in machine learning, as machine learning models are often used to analyze data. MLBox can help you build a foundation in automated machine learning model creation, which will allow you to quickly and easily build models to gain insights from data.
Research Scientist
Research Scientist
Research Scientists conduct scientific research to develop new knowledge and technologies. The field of machine learning is constantly evolving, and MLBox can help you stay up-to-date on the latest advancements by providing a foundation in automated machine learning model creation.
Product Manager
Product Manager
Product Managers are responsible for the development and launch of new products. While a background in machine learning is not required, having a foundation in the field can be beneficial, as machine learning is increasingly being used to develop new products and services. MLBox can help you build a foundation in automated machine learning model creation, which will give you the knowledge you need to succeed as a Product Manager.
Quantitative Analyst
Quantitative Analyst
Quantitative Analysts use mathematical and statistical modeling to analyze financial data. While a background in machine learning is not required, having a foundation in the field can be beneficial, as machine learning models are increasingly being used to analyze financial data. MLBox can help you build a foundation in automated machine learning model creation, which will give you the knowledge you need to succeed as a Quantitative Analyst.
Risk Analyst
Risk Analyst
Risk Analysts assess the risk of potential events and develop strategies to mitigate those risks. While a background in machine learning is not required, having a foundation in the field can be beneficial, as machine learning models are increasingly being used to assess risk. MLBox can help you build a foundation in automated machine learning model creation, which will give you the knowledge you need to succeed as a Risk Analyst.
Actuary
Actuary
Actuaries use mathematical and statistical models to assess risk and uncertainty. While a background in machine learning is not required, having a foundation in the field can be beneficial, as machine learning models are increasingly being used to assess risk and uncertainty. MLBox can help you build a foundation in automated machine learning model creation, which will give you the knowledge you need to succeed as an Actuary.
Financial Analyst
Financial Analyst
Financial Analysts forecast financial performance and make recommendations to businesses and investors. While a background in machine learning is not required, having a foundation in the field can be beneficial, as machine learning models are increasingly being used to forecast financial performance. MLBox can help you build a foundation in automated machine learning model creation, which will give you the knowledge you need to succeed as a Financial Analyst.
Business Analyst
Business Analyst
Business Analysts gather and analyze data to improve business processes. While a background in machine learning is not required, having a foundation in the field can be beneficial, as machine learning models are increasingly being used to analyze business data. MLBox can help you build a foundation in automated machine learning model creation, which will give you the knowledge you need to succeed as a Business Analyst.
Marketing Analyst
Marketing Analyst
Marketing Analysts analyze marketing data to improve marketing campaigns. While a background in machine learning is not required, having a foundation in the field can be beneficial, as machine learning models are increasingly being used to analyze marketing data. MLBox can help you build a foundation in automated machine learning model creation, which will give you the knowledge you need to succeed as a Marketing Analyst.
Operations Research Analyst
Operations Research Analyst
Operations Research Analysts use mathematical and statistical models to solve business problems. While a background in machine learning is not required, having a foundation in the field can be beneficial, as machine learning models are increasingly being used to solve business problems. MLBox can help you build a foundation in automated machine learning model creation, which will give you the knowledge you need to succeed as an Operations Research Analyst.
For more career information including salaries, visit:
OpenCourser.com/course/u8pls7/getting
Reading list
We've selected nine 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
Getting Started with MLBox.
Save
Although this book doesn't cover automated machine learning, it provides a comprehensive guide to TensorFlow, which is the underlying library used by MLBox.
Although this book focuses on traditional machine learning techniques rather than automated machine learning, it provides a comprehensive guide to essential concepts and algorithms, complementing MLBox's automated approach.
Offers a practical introduction to machine learning using R, providing valuable insights for understanding the algorithms and concepts behind MLBox.
While MLBox automates feature preprocessing, this book delves into the principles and techniques of feature engineering, providing valuable insights into how data preparation impacts model performance.
This recipe-based guide provides practical solutions to common machine learning challenges, complementing the theoretical foundations provided by the course.
Explores the techniques for making machine learning models more interpretable, which is particularly relevant when working with automated machine learning systems like MLBox.
As an introductory guide to machine learning in Python, this book offers a solid foundation for understanding the concepts that underpin MLBox's automated approach.
This non-technical guide provides a clear and accessible introduction to machine learning, making it a valuable resource for learners who are new to the field.
As an introductory text, this book offers a clear and accessible overview of machine learning concepts, providing a good starting point for learners who are new to the field.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/u8pls7/getting
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Time
67 hours
Level
Introductory
Via
Pluralsight
Instructor
Trent McMillan
Language
English
Good to know
Covers a niche topic that may not immediately stand out to learners but will be highly valuable for those who work in that area
Taught by Trent McMillan, who are recognized for their work in the field of machine learning and data science
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