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Janani Ravi
This course covers the important considerations for scikit-learn models in improving prediction latency and throughput; specific feature representation and partial learning techniques, as well as implementations of incremental learning, out-of-core learning, and multicore parallelism.
Even as the number of machine learning frameworks and libraries increases rapidly, scikit-learn is retaining its popularity with ease. scikit-learn makes the common use-cases in machine learning - clustering, classification, dimensionality reduction and regression - incredibly easy.
Read more
This course covers the important considerations for scikit-learn models in improving prediction latency and throughput; specific feature representation and partial learning techniques, as well as implementations of incremental learning, out-of-core learning, and multicore parallelism.
Even as the number of machine learning frameworks and libraries increases rapidly, scikit-learn is retaining its popularity with ease. scikit-learn makes the common use-cases in machine learning - clustering, classification, dimensionality reduction and regression - incredibly easy.
Read more
This course covers the important considerations for scikit-learn models in improving prediction latency and throughput; specific feature representation and partial learning techniques, as well as implementations of incremental learning, out-of-core learning, and multicore parallelism.
Even as the number of machine learning frameworks and libraries increases rapidly, scikit-learn is retaining its popularity with ease. scikit-learn makes the common use-cases in machine learning - clustering, classification, dimensionality reduction and regression - incredibly easy.
In this course, Scaling scikit-learn Solutions you will gain the ability to leverage out-of-core learning and multicore parallelism in scikit-learn.
First, you will learn considerations that affect latency and throughput in prediction, including the number of features, feature complexity, and model complexity.
Next, you will discover how smart choices in feature representation and in how you model sparse data can improve the scalability of your models. You will then understand what incremental learning is, and how to use scikit-learn estimators that support this key enabler of out-of-core learning.
Finally, you will round out your knowledge by parallelizing key tasks such as cross-validation, hyperparameter tuning, and ensemble learning.
When you’re finished with this course, you will have the skills and knowledge to identify key techniques to help make your model scalable and implement them appropriately for your use-case.
Register for this course and see more details by visiting:
OpenCourser.com/course/3pxmt5/scaling
What's inside
Syllabus
Course Overview
Understanding Strategies for Computational Scaling
Observing the Factors Affecting Prediction Latency
Implementing Scaling of Instances Using Out-of-core Learning
Read more
Syllabus
Course Overview
Understanding Strategies for Computational Scaling
Observing the Factors Affecting Prediction Latency
Implementing Scaling of Instances Using Out-of-core Learning
Read more
Implementing Multicore Parallelism in scikit-learn
Autoscaling of scikit-learn with Apache Spark
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Explores out-of-core learning and multicore parallelism, which are highly relevant to scaling machine learning solutions in industry
Taught by Janani Ravi, who are recognized for their work in the field of machine learning
Develops skills and knowledge that are useful for personal growth and development in machine learning
Builds a strong foundation for beginners in scaling scikit-learn solutions for machine learning
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Save Scaling scikit-learn Solutions 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 Scaling scikit-learn Solutions with these
activities:
Review scikit-learn Foundation Concepts
Show steps
Solidify the understanding of fundamental scikit-learn concepts and machine learning principles.
Browse courses on
scikit-learn
Show steps
-
Review scikit-learn documentation and tutorials.
-
Revisit key machine learning algorithms and their implementation in scikit-learn.
Collaborate on a Scikit-Learn Scaling Project
Show steps
Enhance understanding and practical application of scikit-learn scaling techniques by collaborating with peers on a hands-on project.
Browse courses on
Collaboration
Show steps
-
Form a study group with peers.
-
Choose a real-world dataset and define scaling goals.
-
Apply scikit-learn scaling techniques and evaluate results.
-
Discuss findings, compare approaches, and refine solutions.
Classify Prediction Latency and Throughput Factors
Show steps
Identify and categorize different aspects that impact the speed of prediction and data processing within scikit-learn models.
Show steps
-
Create a list of factors influencing prediction latency and throughput.
-
Define and provide examples for each factor.
-
Categorize the factors based on their impact.
Four other activities
Expand to see all activities and additional details
Show all seven activities
Showcase Scikit-Learn Scaling Techniques in a Presentation
Show steps
Develop presentation skills while reinforcing scikit-learn scaling knowledge by creating and delivering a presentation on the techniques learned.
Browse courses on
Presentation Skills
Show steps
-
Choose a topic related to scikit-learn scaling.
-
Research and gather relevant information about the topic.
-
Create presentation slides using visuals, examples, and clear explanations.
-
Rehearse and deliver the presentation to an audience.
Boost Model Scalability via Feature Representation
Show steps
Explore techniques for representing features and handling sparse data to enhance the scalability of scikit-learn models.
Show steps
-
Discuss different feature representation methods.
-
Explain the impact of sparse data on model performance.
-
Implement feature representation techniques to improve scalability.
Parallelize Key Tasks in scikit-learn
Show steps
Implement multicore parallelism techniques to speed up key tasks in scikit-learn, such as cross-validation, hyperparameter tuning, and ensemble learning.
Show steps
-
Identify the key tasks to parallelize in scikit-learn.
-
Research and choose appropriate multicore parallelism techniques.
-
Implement multicore parallelism for the identified tasks.
-
Evaluate the performance improvements achieved through parallelization.
Develop an Incremental Learning Implementation
Show steps
Implement incremental learning algorithms using scikit-learn estimators to enable out-of-core learning and handle large datasets.
Browse courses on
Incremental Learning
Show steps
-
Research incremental learning algorithms and their benefits.
-
Select appropriate scikit-learn estimators for incremental learning.
-
Develop an implementation using the selected estimators.
-
Test and evaluate the implementation on a real-world dataset.
Review scikit-learn Foundation Concepts
Show steps
Solidify the understanding of fundamental scikit-learn concepts and machine learning principles.
Browse courses on
scikit-learn
Show steps
Show steps
Show steps
- Review scikit-learn documentation and tutorials.
- Revisit key machine learning algorithms and their implementation in scikit-learn.
Collaborate on a Scikit-Learn Scaling Project
Show steps
Enhance understanding and practical application of scikit-learn scaling techniques by collaborating with peers on a hands-on project.
Browse courses on
Collaboration
Show steps
Show steps
Show steps
- Form a study group with peers.
- Choose a real-world dataset and define scaling goals.
- Apply scikit-learn scaling techniques and evaluate results.
- Discuss findings, compare approaches, and refine solutions.
Classify Prediction Latency and Throughput Factors
Show steps
Identify and categorize different aspects that impact the speed of prediction and data processing within scikit-learn models.
Show steps
Show steps
Show steps
- Create a list of factors influencing prediction latency and throughput.
- Define and provide examples for each factor.
- Categorize the factors based on their impact.
Four other activities
Expand to see all activities and additional details
Show all seven activities
Showcase Scikit-Learn Scaling Techniques in a Presentation
Show steps
Develop presentation skills while reinforcing scikit-learn scaling knowledge by creating and delivering a presentation on the techniques learned.
Browse courses on
Presentation Skills
Show steps
Show steps
Show steps
- Choose a topic related to scikit-learn scaling.
- Research and gather relevant information about the topic.
- Create presentation slides using visuals, examples, and clear explanations.
- Rehearse and deliver the presentation to an audience.
Boost Model Scalability via Feature Representation
Show steps
Explore techniques for representing features and handling sparse data to enhance the scalability of scikit-learn models.
Show steps
Show steps
Show steps
- Discuss different feature representation methods.
- Explain the impact of sparse data on model performance.
- Implement feature representation techniques to improve scalability.
Parallelize Key Tasks in scikit-learn
Show steps
Implement multicore parallelism techniques to speed up key tasks in scikit-learn, such as cross-validation, hyperparameter tuning, and ensemble learning.
Show steps
Show steps
Show steps
- Identify the key tasks to parallelize in scikit-learn.
- Research and choose appropriate multicore parallelism techniques.
- Implement multicore parallelism for the identified tasks.
- Evaluate the performance improvements achieved through parallelization.
Develop an Incremental Learning Implementation
Show steps
Implement incremental learning algorithms using scikit-learn estimators to enable out-of-core learning and handle large datasets.
Browse courses on
Incremental Learning
Show steps
Show steps
Show steps
- Research incremental learning algorithms and their benefits.
- Select appropriate scikit-learn estimators for incremental learning.
- Develop an implementation using the selected estimators.
- Test and evaluate the implementation on a real-world dataset.
Career center
Learners who complete Scaling scikit-learn Solutions will develop knowledge and skills
that may be useful to these careers:
Data Scientist
Data Scientist
Data Scientists are responsible for collecting, analyzing, and interpreting data to uncover patterns and trends. With the skills and knowledge gained from this course, you'll be well-equipped to implement scalable machine learning models to handle large datasets efficiently, a valuable skill for Data Scientists. Understanding how to scale models can help you build more efficient and effective solutions, leading to success in this role.
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers design, develop, and deploy machine learning models. By taking this course, you'll gain the expertise to scale machine learning models effectively, enabling you to handle large-scale data and improve model performance. The course covers techniques like out-of-core learning and multicore parallelism, which are essential for Machine Learning Engineers.
Software Engineer
Software Engineer
Software Engineers design, develop, and maintain software systems. This course provides valuable knowledge for Software Engineers who want to specialize in building scalable machine learning applications. You'll learn how to optimize model performance and handle large datasets, making you a more competitive candidate in this field.
Data Analyst
Data Analyst
Data Analysts gather, clean, and analyze data to identify trends and patterns. This course can enhance your skills by providing techniques to handle large datasets and improve model performance, making you a more valuable asset as a Data Analyst.
Research Scientist
Research Scientist
Research Scientists conduct research to advance scientific knowledge. This course provides a foundation for Research Scientists who want to specialize in machine learning. You'll gain the skills to scale models to handle large datasets and improve performance, which is crucial in research.
Business Analyst
Business Analyst
Business Analysts identify and solve business problems using data analysis and modeling. This course can be useful by providing techniques to handle large datasets and improve model performance, enabling you to make more informed decisions and deliver better solutions as a Business Analyst.
Quantitative Analyst
Quantitative Analyst
Quantitative Analysts use mathematical and statistical models to analyze financial data. This course can be helpful by providing techniques to handle large datasets and improve model performance, allowing you to build more accurate and reliable financial models.
Statistician
Statistician
Statisticians collect, analyze, and interpret data to provide insights. This course can be useful by providing techniques to handle large datasets and improve model performance, enabling you to conduct more comprehensive and accurate statistical analyses.
Data Engineer
Data Engineer
Data Engineers design and build data pipelines and infrastructure. This course can be useful by providing techniques to handle large datasets and improve model performance, enabling you to build more efficient and scalable data pipelines.
Operations Research Analyst
Operations Research Analyst
Operations Research Analysts use mathematical and analytical methods to solve complex business problems. This course can be useful by providing techniques to handle large datasets and improve model performance, enabling you to develop more effective solutions.
Risk Analyst
Risk Analyst
Risk Analysts identify and assess risks for organizations. This course can be useful by providing techniques to handle large datasets and improve model performance, allowing you to build more accurate and comprehensive risk models.
Actuary
Actuary
Actuaries use mathematical and statistical methods to assess and manage financial risks. This course can be useful by providing techniques to handle large datasets and improve model performance, enabling you to build more accurate and reliable actuarial models.
Financial Analyst
Financial Analyst
Financial Analysts analyze financial data to make investment recommendations. This course can be useful by providing techniques to handle large datasets and improve model performance, enabling you to build more accurate and reliable financial models.
Economist
Economist
Economists study the production, distribution, and consumption of goods and services. This course can be useful by providing techniques to handle large datasets and improve model performance, enabling you to conduct more comprehensive and accurate economic analyses.
Market Researcher
Market Researcher
Market Researchers study consumer behavior to understand market trends. This course can be useful by providing techniques to handle large datasets and improve model performance, enabling you to conduct more comprehensive and accurate market research studies.
For more career information including salaries, visit:
OpenCourser.com/course/3pxmt5/scaling
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
Scaling scikit-learn Solutions.
Save
Comprehensive introduction to pattern recognition and machine learning. It covers the basics of pattern recognition and machine learning, as well as how to use Python to build and train pattern recognition and machine learning models.
Comprehensive introduction to machine learning from a probabilistic perspective. It covers the basics of machine learning, as well as how to use Python to build and train probabilistic machine learning models.
Comprehensive introduction to reinforcement learning. It covers the basics of reinforcement learning, as well as how to build and train reinforcement learning models.
Classic introduction to artificial intelligence. It covers the basics of artificial intelligence, as well as how to build and train artificial intelligence models.
Comprehensive introduction to neural networks and deep learning. It covers the basics of neural networks and deep learning, as well as how to build and train neural networks and deep learning models.
Save
Comprehensive introduction to data mining. It covers the basics of data mining, as well as how to use Python to build and train data mining models.
Save
Comprehensive introduction to deep learning. It covers the basics of deep learning, as well as how to build and train deep learning models.
Comprehensive guide to machine learning with scikit-learn, Keras, and TensorFlow. It covers all the steps of the machine learning process, from data preprocessing to model evaluation.
Comprehensive introduction to big data analytics. It covers the basics of big data analytics, as well as how to use Python to build and train big data analytics models.
Comprehensive introduction to ensemble methods in machine learning. It covers the basics of ensemble methods, as well as how to use Python to build and train ensemble machine learning models.
Comprehensive introduction to data-intensive text processing with MapReduce. It covers the basics of data-intensive text processing, as well as how to use Hadoop to build and train data-intensive text processing models.
Gentle introduction to deep learning with Python. It covers the basics of deep learning, as well as how to build and train deep learning models.
Practical introduction to data science. It covers the basics of data science, as well as how to use Python to build and train data science models.
Very basic introduction to machine learning. It covers the basics of machine learning, as well as how to use Python to build and train machine learning models.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/3pxmt5/scaling
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Time
174 hours
Level
Advanced
Via
Pluralsight
Instructor
Janani Ravi
Language
English
This course belongs to a
collection
called
Building Machine Learning Solutions with scikit-learn. It's comprised of nine courses:
- Building Your First scikit-learn Solution
- Building Classification Models with scikit-learn
- Building Regression Models with scikit-learn
- Building Clustering Models with scikit-learn
- Building Neural Networks with scikit-learn
- Reducing Dimensions in Data with scikit-learn
- Employing Ensemble Methods with scikit-learn
- Preparing Data for Modeling with scikit-learn
- Scaling scikit-learn Solutions (viewing)
Good to know
Explores out-of-core learning and multicore parallelism, which are highly relevant to scaling machine learning solutions in industry
Taught by Janani Ravi, who are recognized for their work in the field of machine learning
Develops skills and knowledge that are useful for personal growth and development in machine learning
Builds a strong foundation for beginners in scaling scikit-learn solutions for machine learning
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