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Dr. Nikunj Maheshwari
By the end of this project, you will learn how to create machine learning pipelines using Python and Spark, free, open-source programs that you can download. You will learn how to load your dataset in Spark and learn how to perform basic cleaning techniques such as removing columns with high missing values and removing rows with missing values. You will then create a machine learning pipeline with a random forest regression model. You will use cross validation and parameter tuning to select the best model from the pipeline. Lastly, you will evaluate your model’s performance using various metrics.
A pipeline in Spark combines...
Read more
By the end of this project, you will learn how to create machine learning pipelines using Python and Spark, free, open-source programs that you can download. You will learn how to load your dataset in Spark and learn how to perform basic cleaning techniques such as removing columns with high missing values and removing rows with missing values. You will then create a machine learning pipeline with a random forest regression model. You will use cross validation and parameter tuning to select the best model from the pipeline. Lastly, you will evaluate your model’s performance using various metrics.
A pipeline in Spark combines...
Read more
By the end of this project, you will learn how to create machine learning pipelines using Python and Spark, free, open-source programs that you can download. You will learn how to load your dataset in Spark and learn how to perform basic cleaning techniques such as removing columns with high missing values and removing rows with missing values. You will then create a machine learning pipeline with a random forest regression model. You will use cross validation and parameter tuning to select the best model from the pipeline. Lastly, you will evaluate your model’s performance using various metrics.
A pipeline in Spark combines multiple execution steps in the order of their execution. So rather than executing the steps individually, one can put them in a pipeline to streamline the machine learning process. You can save this pipeline, share it with your colleagues, and load it back again effortlessly.
Note: You should have a Gmail account which you will use to sign into Google Colab.
Note: This course works best for learners who are based in the North America region. We’re currently working on providing the same experience in other regions.
Register for this course and see more details by visiting:
OpenCourser.com/course/f497f4/building
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Teaches the fundamentals of machine learning pipelines with Python and Spark, industry-standard tools
Offers hands-on practice with data cleaning, model building, and evaluation techniques
Provides a solid foundation for beginners in machine learning pipeline development
Emphasizes cross-validation and parameter tuning to optimize model performance
Requires basic familiarity with Python and Spark, which may be a barrier for complete beginners
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Reviews summary
Pyspark mllib pipeline basics
This course teaches you how to create machine learning pipelines using Python and Spark. You will learn how to load your dataset in Spark, perform basic cleaning techniques, and create a machine learning pipeline with a random forest regression model. You will use cross validation and parameter tuning to select the best model from the pipeline, and evaluate your model’s performance using various metrics.
Practical, hands-on project.
"Good project to get you started..."
"very short and hands on"
"....basically replicate the code the instructor provides which is very clear and concise"
Potential for technical issues with dataset and installation.
"First cell gives error..."
"following fix on discussion forum did not resolve the error"
"The dataset provided was wrong..."
"...It's a shame that people with high level of education are trying to scam people"
Focus on code replication, less on concepts.
"Instructor doesn't really talk about the library or how things work"
"....I did not feel like I learned a lot from it because of the very simple guided nature"
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 Machine Learning Pipelines in PySpark MLlib with these
activities:
Review Big Data Analytics
Show steps
Reviewing this text will provide context for the course by giving foundational concepts, terms, and theory of data analytics.
View
Big Data, Big Analytics: Emerging Business...
on Amazon
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-
Read the first three chapters of the book.
-
Make notes on the key points in each chapter.
-
Create a mind map of the concepts covered in the chapters.
Compile a Resource List
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Creating a compilation of resources will provide you with easy access to helpful materials throughout the course.
Browse courses on
Resources
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-
Gather relevant articles, tutorials, and videos.
-
Organize the resources into a document or spreadsheet.
-
Share the compilation with classmates.
Join a Study Group
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Joining a study group will provide opportunities to discuss the course material with peers and enhance your understanding.
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Collaboration
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-
Find a study group or create one with classmates.
-
Meet regularly to discuss the course material.
-
Work together on practice problems.
Five other activities
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Show all eight activities
Practice Data Cleaning in Spark
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Practicing data cleaning will strengthen your understanding of data preprocessing techniques.
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Data Cleaning
Show steps
-
Load a dataset into Spark.
-
Identify and remove columns with missing values.
-
Identify and replace outliers.
Create a Machine Learning Model Using Spark
Show steps
Building a model will help you apply the concepts and techniques learned in the course.
Browse courses on
Machine Learning
Show steps
-
Load a dataset into Spark.
-
Preprocess the data.
-
Create a machine learning model.
-
Evaluate the model.
Follow a Tutorial on Hyperparameter Tuning
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Following a tutorial will provide practical guidance on optimizing your machine learning models.
Browse courses on
Hyperparameter Tuning
Show steps
-
Find a tutorial on hyperparameter tuning.
-
Follow the steps in the tutorial.
-
Apply the techniques to your own machine learning model.
Participate in a Kaggle Competition
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Participating in a Kaggle competition will challenge you to apply your skills and learn from others.
Browse courses on
Kaggle
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-
Find a Kaggle competition that aligns with your interests.
-
Download the competition data.
-
Build a machine learning model to solve the competition problem.
-
Submit your model to the competition.
Mentor a Junior Student
Show steps
Mentoring a junior student can help reinforce your understanding of the course material while also providing support to others.
Browse courses on
Mentoring
Show steps
-
Identify a junior student who needs support.
-
Meet with the student regularly to discuss the course material.
-
Provide feedback on the student's work.
Review Big Data Analytics
Show steps
Reviewing this text will provide context for the course by giving foundational concepts, terms, and theory of data analytics.
View
Big Data, Big Analytics: Emerging Business...
on Amazon
Show steps
Show steps
Show steps
- Read the first three chapters of the book.
- Make notes on the key points in each chapter.
- Create a mind map of the concepts covered in the chapters.
Compile a Resource List
Show steps
Creating a compilation of resources will provide you with easy access to helpful materials throughout the course.
Browse courses on
Resources
Show steps
Show steps
Show steps
- Gather relevant articles, tutorials, and videos.
- Organize the resources into a document or spreadsheet.
- Share the compilation with classmates.
Join a Study Group
Show steps
Joining a study group will provide opportunities to discuss the course material with peers and enhance your understanding.
Browse courses on
Collaboration
Show steps
Show steps
Show steps
- Find a study group or create one with classmates.
- Meet regularly to discuss the course material.
- Work together on practice problems.
Five other activities
Expand to see all activities and additional details
Show all eight activities
Practice Data Cleaning in Spark
Show steps
Practicing data cleaning will strengthen your understanding of data preprocessing techniques.
Browse courses on
Data Cleaning
Show steps
Show steps
Show steps
- Load a dataset into Spark.
- Identify and remove columns with missing values.
- Identify and replace outliers.
Create a Machine Learning Model Using Spark
Show steps
Building a model will help you apply the concepts and techniques learned in the course.
Browse courses on
Machine Learning
Show steps
Show steps
Show steps
- Load a dataset into Spark.
- Preprocess the data.
- Create a machine learning model.
- Evaluate the model.
Follow a Tutorial on Hyperparameter Tuning
Show steps
Following a tutorial will provide practical guidance on optimizing your machine learning models.
Browse courses on
Hyperparameter Tuning
Show steps
Show steps
Show steps
- Find a tutorial on hyperparameter tuning.
- Follow the steps in the tutorial.
- Apply the techniques to your own machine learning model.
Participate in a Kaggle Competition
Show steps
Participating in a Kaggle competition will challenge you to apply your skills and learn from others.
Browse courses on
Kaggle
Show steps
Show steps
Show steps
- Find a Kaggle competition that aligns with your interests.
- Download the competition data.
- Build a machine learning model to solve the competition problem.
- Submit your model to the competition.
Mentor a Junior Student
Show steps
Mentoring a junior student can help reinforce your understanding of the course material while also providing support to others.
Browse courses on
Mentoring
Show steps
Show steps
Show steps
- Identify a junior student who needs support.
- Meet with the student regularly to discuss the course material.
- Provide feedback on the student's work.
Career center
Learners who complete Building Machine Learning Pipelines in PySpark MLlib will develop knowledge and skills
that may be useful to these careers:
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers build, deploy, and maintain machine learning models. This course teaches you how to create machine learning pipelines, which is a valuable skill for Machine Learning Engineers. You will also learn how to tune the parameters of machine learning models, which can help you improve the performance of your models. This course can help you build a strong foundation for a career as a Machine Learning Engineer.
Data Scientist
Data Scientist
Data Scientists use data to solve business problems. This course teaches you how to load, clean, and analyze data using Spark, which is a valuable skill for Data Scientists. You will also learn how to create machine learning pipelines, which can be used to automate the process of building and deploying machine learning models. This course can help you build a strong foundation for a career as a Data Scientist.
Data Analyst
Data Analyst
Data Analysts collect, clean, and analyze data to help businesses make informed decisions. This course teaches you how to load, clean, and analyze data using Spark, which is a valuable skill for Data Analysts. You will also learn how to create machine learning pipelines, which can be used to automate the process of building and deploying machine learning models. This course can help you build a strong foundation for a career as a Data Analyst.
Data Engineer
Data Engineer
Data Engineers build and maintain data infrastructure. This course teaches you how to load, clean, and analyze data using Spark, which is a valuable skill for Data Engineers. You will also learn how to create machine learning pipelines, which can be used to automate the process of building and deploying machine learning models. This course can help you build a strong foundation for a career as a Data Engineer.
Business Analyst
Business Analyst
Business Analysts use data to help businesses make better decisions. This course teaches you how to load, clean, and analyze data using Spark, which is a valuable skill for Business Analysts. You will also learn how to create machine learning pipelines, which can be used to automate the process of building and deploying machine learning models. This course can help you build a strong foundation for a career as a Business Analyst.
Data Architect
Data Architect
Data Architects design and build data architectures. This course teaches you how to load, clean, and analyze data using Spark, which is a valuable skill for Data Architects. You will also learn how to create machine learning pipelines, which can be used to automate the process of building and deploying machine learning models. This course can help you build a strong foundation for a career as a Data Architect.
Software Engineer
Software Engineer
Software Engineers design, develop, and maintain software applications. This course teaches you how to create machine learning pipelines, which can be used to add machine learning functionality to software applications. You will also learn how to tune the parameters of machine learning models, which can help you improve the performance of your applications. This course can help you build a strong foundation for a career as a Software Engineer.
Big Data Engineer
Big Data Engineer
Big Data Engineers design and build big data systems. This course teaches you how to load, clean, and analyze big data using Spark, which is a valuable skill for Big Data Engineers. You will also learn how to create machine learning pipelines, which can be used to automate the process of building and deploying machine learning models. This course can help you build a strong foundation for a career as a Big Data Engineer.
Database Administrator
Database Administrator
Database Administrators manage and maintain databases. This course teaches you how to load, clean, and analyze data using Spark, which is a valuable skill for Database Administrators. You will also learn how to create machine learning pipelines, which can be used to automate the process of building and deploying machine learning models. This course can help you build a strong foundation for a career as a Database Administrator.
Operations Research Analyst
Operations Research Analyst
Operations Research Analysts use mathematical and statistical techniques to solve business problems. This course teaches you how to load, clean, and analyze data using Spark, which is a valuable skill for Operations Research Analysts. You will also learn how to create machine learning pipelines, which can be used to automate the process of building and deploying machine learning models. This course can help you build a strong foundation for a career as an Operations Research Analyst.
Risk Analyst
Risk Analyst
Risk Analysts assess and manage risk. This course teaches you how to load, clean, and analyze data using Spark, which is a valuable skill for Risk Analysts. You will also learn how to create machine learning pipelines, which can be used to automate the process of building and deploying machine learning models. This course can help you build a strong foundation for a career as a Risk Analyst.
Quantitative Analyst
Quantitative Analyst
Quantitative Analysts use data to make investment decisions. This course teaches you how to load, clean, and analyze data using Spark, which is a valuable skill for Quantitative Analysts. You will also learn how to create machine learning pipelines, which can be used to automate the process of building and deploying machine learning models. This course can help you build a strong foundation for a career as a Quantitative Analyst.
Actuary
Actuary
Actuaries use mathematical and statistical techniques to assess risk. This course teaches you how to load, clean, and analyze data using Spark, which is a valuable skill for Actuaries. You will also learn how to create machine learning pipelines, which can be used to automate the process of building and deploying machine learning models. This course can help you build a strong foundation for a career as an Actuary.
Financial Analyst
Financial Analyst
Financial Analysts analyze financial data to make investment decisions. This course teaches you how to load, clean, and analyze data using Spark, which is a valuable skill for Financial Analysts. You will also learn how to create machine learning pipelines, which can be used to automate the process of building and deploying machine learning models. This course can help you build a strong foundation for a career as a Financial Analyst.
Statistician
Statistician
Statisticians collect, analyze, and interpret data. This course teaches you how to load, clean, and analyze data using Spark, which is a valuable skill for Statisticians. You will also learn how to create machine learning pipelines, which can be used to automate the process of building and deploying machine learning models. This course can help you build a strong foundation for a career as a Statistician.
For more career information including salaries, visit:
OpenCourser.com/course/f497f4/building
Reading list
We've selected 14 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 Machine Learning Pipelines in PySpark MLlib.
Save
Provides a comprehensive introduction to machine learning with PySpark, including topics such as data preparation, feature engineering, model training, and model evaluation. It valuable reference for anyone who wants to learn how to use PySpark for machine learning.
Save
Provides a comprehensive introduction to Apache Spark, including topics such as dataframes, transformations, actions, and machine learning. It valuable reference for anyone who wants to learn how to use Spark for data processing and machine learning.
Provides a comprehensive introduction to Python for data analysis, including topics such as dataframes, data manipulation, and data visualization. It valuable reference for anyone who wants to learn how to use Python for data analysis.
Save
Provides a gentle introduction to machine learning, including topics such as supervised learning, unsupervised learning, and deep learning. It valuable reference for anyone who wants to learn the basics of machine learning.
Provides a comprehensive introduction to deep learning with Python, including topics such as convolutional neural networks, recurrent neural networks, and generative adversarial networks. It valuable reference for anyone who wants to learn how to use deep learning for computer vision, natural language processing, and other applications.
Provides a comprehensive introduction to data science, including topics such as data collection, data cleaning, data analysis, and data visualization. It valuable reference for anyone who wants to learn the basics of data science.
Provides a comprehensive introduction to machine learning with Python, including topics such as supervised learning, unsupervised learning, and deep learning. It valuable reference for anyone who wants to learn how to use machine learning for computer vision, natural language processing, and other applications.
Provides a comprehensive introduction to machine learning, including topics such as supervised learning, unsupervised learning, and deep learning. It valuable reference for anyone who wants to learn the basics of machine learning.
Provides a comprehensive introduction to deep learning with Python, including topics such as convolutional neural networks, recurrent neural networks, and generative adversarial networks. It valuable reference for anyone who wants to learn how to use deep learning for computer vision, natural language processing, and other applications.
Provides a comprehensive introduction to artificial intelligence, including topics such as machine learning, natural language processing, and computer vision. It valuable reference for anyone who wants to learn the basics of artificial intelligence.
Save
Provides a comprehensive introduction to data mining, including topics such as data preprocessing, data mining algorithms, and data visualization. It valuable reference for anyone who wants to learn the basics of data mining.
Provides a comprehensive introduction to statistical learning, including topics such as supervised learning, unsupervised learning, and deep learning. It valuable reference for anyone who wants to learn the basics of statistical learning.
Provides a comprehensive introduction to statistical learning, including topics such as supervised learning, unsupervised learning, and deep learning. It valuable reference for anyone who wants to learn the basics of statistical learning.
Provides a comprehensive introduction to machine learning with R, including topics such as supervised learning, unsupervised learning, and deep learning. It valuable reference for anyone who wants to learn how to use R for machine learning.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/f497f4/building
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Time
1 hours
Via
Coursera
Institution
Coursera Project Network
Instructor
Dr. Nikunj Maheshwari
Good to know
Teaches the fundamentals of machine learning pipelines with Python and Spark, industry-standard tools
Offers hands-on practice with data cleaning, model building, and evaluation techniques
Provides a solid foundation for beginners in machine learning pipeline development
Emphasizes cross-validation and parameter tuning to optimize model performance
Requires basic familiarity with Python and Spark, which may be a barrier for complete beginners
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