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Bismark Adomako
This course gives Microsoft Azure Data Scientists a road map on how to build, train, and validate machine learning models in Azure.
Building machine learning models in Microsoft Azure can appear intimidiating. This course, Building, Training, and Validating Models in Microsoft Azure, will help you decide which model to choose and why by building a model which will try to predict if a flight would be delayed more than 15 mins with given data. First, you will go through a real world problem to see how Azure ML can solve this problem, helping you form a hypothesis on which the model performance can be judged.
Read more
This course gives Microsoft Azure Data Scientists a road map on how to build, train, and validate machine learning models in Azure.
Building machine learning models in Microsoft Azure can appear intimidiating. This course, Building, Training, and Validating Models in Microsoft Azure, will help you decide which model to choose and why by building a model which will try to predict if a flight would be delayed more than 15 mins with given data. First, you will go through a real world problem to see how Azure ML can solve this problem, helping you form a hypothesis on which the model performance can be judged.
Read more
This course gives Microsoft Azure Data Scientists a road map on how to build, train, and validate machine learning models in Azure.
Building machine learning models in Microsoft Azure can appear intimidiating. This course, Building, Training, and Validating Models in Microsoft Azure, will help you decide which model to choose and why by building a model which will try to predict if a flight would be delayed more than 15 mins with given data. First, you will go through a real world problem to see how Azure ML can solve this problem, helping you form a hypothesis on which the model performance can be judged.
Next, you will quickly get Azure ML set up and learn why you need to split data for training and testing the models.
Then, you will explore the dependent and independent variables, which independent variables should be picked, why they should be picked, as well as feature data conversion such as label encoding and feature scaling.
Finally, you will discover which models to choose and why before obtaining the score of the model which will show how we can optimize the model and re-test.
When you are finished with this course, you will be ready to put your own model into production and monitor and retrain that model when necessary.
Register for this course and see more details by visiting:
OpenCourser.com/course/r7zsjc/building
What's inside
Syllabus
Course Overview
Creating a Hypothesis
Sourcing and Transforming Data Relevant to a Hypothesis
Identifying Features from Raw Data
Read more
Syllabus
Course Overview
Creating a Hypothesis
Sourcing and Transforming Data Relevant to a Hypothesis
Identifying Features from Raw Data
Read more
Building the Model
Monitoring and Managing the Performance of a Model
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Acquaints data scientists with one of Microsoft's flagship services for developing machine learning models
Provides step-by-step guidance throughout the process of building, training, and evaluating machine learning models
Applies theory to a practical problem by predicting flight delays
Empowers data scientists to make informed decisions about model selection and optimization
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Save Building, Training, and Validating Models in Microsoft Azure 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 Building, Training, and Validating Models in Microsoft Azure with these
activities:
Attend Azure ML Study Group
Show steps
Enhance understanding through peer support, knowledge sharing, and collaborative problem-solving.
Show steps
-
Join or create an Azure ML study group
-
Participate in group discussions and share knowledge
Explore Data Preprocessing Techniques
Show steps
Gain familiarity with best practices and techniques for data preprocessing in machine learning.
Browse courses on
Data Preprocessing
Show steps
-
Review tutorials on data preprocessing methods
-
Apply preprocessing techniques to real-world datasets
Review Model Evaluation Concepts
Show steps
Gain familiarity with key model evaluation concepts and metrics to assess model performance.
Browse courses on
Model Evaluation
Show steps
-
Identify and review resources on model evaluation
-
Practice calculating and interpreting model evaluation metrics
Ten other activities
Expand to see all activities and additional details
Show all 13 activities
Follow Microsoft Azure Machine Learning Tutorials
Show steps
Enhance understanding of Azure ML concepts and capabilities through guided tutorials and hands-on exercises.
Browse courses on
Microsoft Azure Machine Learning
Show steps
-
Access Microsoft Azure Machine Learning tutorials
-
Complete selected tutorials related to model building
Practice Leetcode problems for binary search
Show steps
This activity targets the development of foundational skills for working with binary search trees and their applications.
Browse courses on
Binary Search
Show steps
-
Select a Leetcode problem related to binary search
-
Solve the problem using a binary search algorithm
-
Repeat steps 1 and 2 for multiple problems
Practice Data Preprocessing Techniques
Show steps
Develop proficiency in data preprocessing techniques, such as data cleaning, normalization, and feature scaling, to ensure clean and consistent data for model training.
Show steps
-
Implement data cleaning routines to handle missing values, outliers, and duplicates
-
Normalize data using techniques like min-max scaling and standardization
-
Apply feature scaling methods, such as normalization or standardization, to ensure features are on the same scale
Explore Machine Learning Libraries and Platforms
Show steps
Gain hands-on experience with Azure ML and other relevant machine learning libraries to enhance practical understanding.
Show steps
-
Follow guided tutorials to build and train machine learning models using Azure ML
-
Experiment with different libraries and compare their functionalities
Solve Machine Learning Model Assessment Exercises
Show steps
Reinforce understanding of model evaluation techniques and improve problem-solving skills.
Show steps
-
Identify relevant model assessment exercises and problems
-
Attempt to solve the exercises and evaluate the results
-
Review the solutions and identify areas for improvement
Classification Model Optimization
Show steps
Sharpen your abilities by optimizing classification models through parameter tuning.
Browse courses on
Machine Learning Models
Show steps
-
Understand the impact of parameters
-
Apply grid search for parameter optimization
-
Evaluate model performance
Develop a Flight Delay Prediction Model
Show steps
Develop a machine learning model to accurately predict flight delays based on various input data.
Show steps
-
Gather and preprocess flight data
-
Select and train a machine learning algorithm
-
Evaluate and optimize the model's performance
Exponential Smoothing for Increased Accuracy
Show steps
Acquire the skills to use exponential smoothing for improved accuracy in machine learning models.
Browse courses on
Exponential Smoothing
Show steps
-
Explore different exponential smoothing techniques
-
Apply exponential smoothing to data
-
Evaluate the performance of exponential smoothing models
Interactive Data Visualization Dashboard
Show steps
Enhance your skills by creating an interactive dashboard to visualize machine learning model outputs.
Browse courses on
Data Visualization
Show steps
-
Identify data visualization techniques
-
Select appropriate data sources
-
Design and implement interactive dashboard
-
Publish and share dashboard
Kaggle Machine Learning Competition
Show steps
Gain real-world experience by participating in a Kaggle machine learning competition.
Show steps
-
Choose a competition
-
Prepare data and build models
-
Submit solutions
Attend Azure ML Study Group
Show steps
Enhance understanding through peer support, knowledge sharing, and collaborative problem-solving.
Show steps
Show steps
Show steps
- Join or create an Azure ML study group
- Participate in group discussions and share knowledge
Explore Data Preprocessing Techniques
Show steps
Gain familiarity with best practices and techniques for data preprocessing in machine learning.
Browse courses on
Data Preprocessing
Show steps
Show steps
Show steps
- Review tutorials on data preprocessing methods
- Apply preprocessing techniques to real-world datasets
Review Model Evaluation Concepts
Show steps
Gain familiarity with key model evaluation concepts and metrics to assess model performance.
Browse courses on
Model Evaluation
Show steps
Show steps
Show steps
- Identify and review resources on model evaluation
- Practice calculating and interpreting model evaluation metrics
Ten other activities
Expand to see all activities and additional details
Show all 13 activities
Follow Microsoft Azure Machine Learning Tutorials
Show steps
Enhance understanding of Azure ML concepts and capabilities through guided tutorials and hands-on exercises.
Browse courses on
Microsoft Azure Machine Learning
Show steps
Show steps
Show steps
- Access Microsoft Azure Machine Learning tutorials
- Complete selected tutorials related to model building
Practice Leetcode problems for binary search
Show steps
This activity targets the development of foundational skills for working with binary search trees and their applications.
Browse courses on
Binary Search
Show steps
Show steps
Show steps
- Select a Leetcode problem related to binary search
- Solve the problem using a binary search algorithm
- Repeat steps 1 and 2 for multiple problems
Practice Data Preprocessing Techniques
Show steps
Develop proficiency in data preprocessing techniques, such as data cleaning, normalization, and feature scaling, to ensure clean and consistent data for model training.
Show steps
Show steps
Show steps
- Implement data cleaning routines to handle missing values, outliers, and duplicates
- Normalize data using techniques like min-max scaling and standardization
- Apply feature scaling methods, such as normalization or standardization, to ensure features are on the same scale
Explore Machine Learning Libraries and Platforms
Show steps
Gain hands-on experience with Azure ML and other relevant machine learning libraries to enhance practical understanding.
Show steps
Show steps
Show steps
- Follow guided tutorials to build and train machine learning models using Azure ML
- Experiment with different libraries and compare their functionalities
Solve Machine Learning Model Assessment Exercises
Show steps
Reinforce understanding of model evaluation techniques and improve problem-solving skills.
Show steps
Show steps
Show steps
- Identify relevant model assessment exercises and problems
- Attempt to solve the exercises and evaluate the results
- Review the solutions and identify areas for improvement
Classification Model Optimization
Show steps
Sharpen your abilities by optimizing classification models through parameter tuning.
Browse courses on
Machine Learning Models
Show steps
Show steps
Show steps
- Understand the impact of parameters
- Apply grid search for parameter optimization
- Evaluate model performance
Develop a Flight Delay Prediction Model
Show steps
Develop a machine learning model to accurately predict flight delays based on various input data.
Show steps
Show steps
Show steps
- Gather and preprocess flight data
- Select and train a machine learning algorithm
- Evaluate and optimize the model's performance
Exponential Smoothing for Increased Accuracy
Show steps
Acquire the skills to use exponential smoothing for improved accuracy in machine learning models.
Browse courses on
Exponential Smoothing
Show steps
Show steps
Show steps
- Explore different exponential smoothing techniques
- Apply exponential smoothing to data
- Evaluate the performance of exponential smoothing models
Interactive Data Visualization Dashboard
Show steps
Enhance your skills by creating an interactive dashboard to visualize machine learning model outputs.
Browse courses on
Data Visualization
Show steps
Show steps
Show steps
- Identify data visualization techniques
- Select appropriate data sources
- Design and implement interactive dashboard
- Publish and share dashboard
Kaggle Machine Learning Competition
Show steps
Gain real-world experience by participating in a Kaggle machine learning competition.
Show steps
Show steps
Show steps
- Choose a competition
- Prepare data and build models
- Submit solutions
Career center
Learners who complete Building, Training, and Validating Models in Microsoft Azure will develop knowledge and skills
that may be useful to these careers:
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers are responsible for the development and deployment of machine learning models. This course provides a solid foundation in the principles and practices of machine learning, and will help you develop the skills you need to be successful in this role. You will learn how to build, train, and validate machine learning models, and how to deploy them to production. This course is essential for anyone who wants to pursue a career in machine learning engineering.
Data Scientist
Data Scientist
Data Scientists use machine learning and other statistical techniques to extract insights from data. This course will help you develop the skills you need to be successful in this role. You will learn how to build, train, and validate machine learning models, and how to use them to solve real-world problems. This course is ideal for anyone who wants to pursue a career in data science.
Software Engineer
Software Engineer
Software Engineers design, develop, and maintain software systems. This course will help you develop the skills you need to be successful in this role. You will learn how to build, train, and validate machine learning models, and how to integrate them into software systems. This course is ideal for anyone who wants to pursue a career in software engineering.
Business Analyst
Business Analyst
Business Analysts use data to identify and solve business problems. This course will help you develop the skills you need to be successful in this role. You will learn how to build, train, and validate machine learning models, and how to use them to solve real-world business problems. This course is ideal for anyone who wants to pursue a career in business analysis.
Product Manager
Product Manager
Product Managers are responsible for the development and launch of new products. This course will help you develop the skills you need to be successful in this role. You will learn how to build, train, and validate machine learning models, and how to use them to create innovative new products. This course is ideal for anyone who wants to pursue a career in product management.
Quantitative Analyst
Quantitative Analyst
Quantitative Analysts use mathematical and statistical techniques to analyze financial data. This course will help you develop the skills you need to be successful in this role. You will learn how to build, train, and validate machine learning models, and how to use them to solve real-world financial problems. This course is ideal for anyone who wants to pursue a career in quantitative analysis.
Operations Research Analyst
Operations Research Analyst
Operations Research Analysts use mathematical and statistical techniques to solve operational problems. This course will help you develop the skills you need to be successful in this role. You will learn how to build, train, and validate machine learning models, and how to use them to solve real-world operational problems. This course is ideal for anyone who wants to pursue a career in operations research.
Data Architect
Data Architect
Data Architects design and implement data architectures. This course will help you develop the skills you need to be successful in this role. You will learn how to build, train, and validate machine learning models, and how to integrate them into data architectures. This course is ideal for anyone who wants to pursue a career in data architecture.
Machine Learning Researcher
Machine Learning Researcher
Machine Learning Researchers develop new machine learning algorithms and techniques. This course will help you develop the skills you need to be successful in this role. You will learn how to build, train, and validate machine learning models, and how to design and conduct experiments to evaluate their performance. This course is ideal for anyone who wants to pursue a career in machine learning research.
Statistician
Statistician
Statisticians use statistical methods to analyze data. This course will help you develop the skills you need to be successful in this role. You will learn how to build, train, and validate machine learning models, and how to use them to solve real-world statistical problems. This course is ideal for anyone who wants to pursue a career in statistics.
Financial Analyst
Financial Analyst
Financial Analysts use financial data to make investment decisions. This course will help you develop the skills you need to be successful in this role. You will learn how to build, train, and validate machine learning models, and how to use them to analyze financial data and make investment decisions. This course is ideal for anyone who wants to pursue a career in financial analysis.
Actuary
Actuary
Actuaries use mathematical and statistical techniques to assess risk. This course will help you develop the skills you need to be successful in this role. You will learn how to build, train, and validate machine learning models, and how to use them to assess risk and make decisions. This course is ideal for anyone who wants to pursue a career in actuarial science.
Data Analyst
Data Analyst
Data Analysts use data to solve business problems. This course will help you develop the skills you need to be successful in this role. You will learn how to build, train, and validate machine learning models, and how to use them to solve real-world business problems. This course is ideal for anyone who wants to pursue a career in data analysis.
Business Intelligence Analyst
Business Intelligence Analyst
Business Intelligence Analysts use data to make informed business decisions. This course will help you develop the skills you need to be successful in this role. You will learn how to build, train, and validate machine learning models, and how to use them to analyze data and make informed business decisions. This course is ideal for anyone who wants to pursue a career in business intelligence.
Market Researcher
Market Researcher
Market Researchers use data to understand customer behavior. This course will help you develop the skills you need to be successful in this role. You will learn how to build, train, and validate machine learning models, and how to use them to analyze customer behavior and make informed marketing decisions. This course is ideal for anyone who wants to pursue a career in market research.
For more career information including salaries, visit:
OpenCourser.com/course/r7zsjc/building
Reading list
We've selected 11 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
Building, Training, and Validating Models in Microsoft Azure.
Provides a comprehensive overview of machine learning from a probabilistic perspective. It covers a wide range of topics, from supervised learning to unsupervised learning, and it provides a solid theoretical foundation for understanding machine learning algorithms.
Classic introduction to reinforcement learning. It provides a comprehensive overview of the field, and it valuable resource for anyone who wants to learn more about reinforcement learning.
Provides a comprehensive overview of the foundations of machine learning. It covers a wide range of topics, from the basics of learning theory to advanced topics such as online learning and reinforcement learning.
Classic introduction to statistical learning. It provides a comprehensive overview of the field, and it valuable resource for anyone who wants to learn more about statistical learning.
Covers all the essential concepts of machine learning, from data preparation to model evaluation, and it uses the R programming language to provide practical examples. It valuable resource for anyone who wants to learn more about machine learning and how to apply it to real-world problems.
Comprehensive guide to deep learning using the Python programming language. It covers a wide range of topics, from the basics of deep learning to advanced topics such as convolutional neural networks and recurrent neural networks.
Provides a comprehensive overview of statistical learning with sparsity. It covers a wide range of topics, from the basics of sparsity to advanced topics such as the lasso and other regularization methods.
Provides a comprehensive overview of kernel methods for machine learning. It covers a wide range of topics, from the basics of kernel methods to advanced topics such as support vector machines and Gaussian processes.
Comprehensive guide to machine learning using the Python programming language. It covers a wide range of topics, from the basics of machine learning to advanced topics such as deep learning and reinforcement learning.
Practical guide to machine learning using the Scikit-Learn, Keras, and TensorFlow libraries. It covers a wide range of topics, from the basics of machine learning to advanced topics such as deep learning and reinforcement learning.
Practical guide to machine learning for hackers. It covers a wide range of topics, from the basics of machine learning to advanced topics such as deep learning and reinforcement learning.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/r7zsjc/building
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Time
103 hours
Level
Advanced
Via
Pluralsight
Instructor
Bismark Adomako
Language
English
Good to know
Acquaints data scientists with one of Microsoft's flagship services for developing machine learning models
Provides step-by-step guidance throughout the process of building, training, and evaluating machine learning models
Applies theory to a practical problem by predicting flight delays
Empowers data scientists to make informed decisions about model selection and optimization
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