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Janani Ravi
This course covers both the why and how of using scikit-learn. You'll delve into scikit-learn’s niche in the ever-growing taxonomy of machine learning libraries, and important aspects of working with scikit-learn estimators and pipelines.
Read more
This course covers both the why and how of using scikit-learn. You'll delve into scikit-learn’s niche in the ever-growing taxonomy of machine learning libraries, and important aspects of working with scikit-learn estimators and pipelines.
Read more
This course covers both the why and how of using scikit-learn. You'll delve into scikit-learn’s niche in the ever-growing taxonomy of machine learning libraries, and important aspects of working with scikit-learn estimators and pipelines.
Even as the number of machine learning frameworks and libraries increases on a daily basis, 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, Building Your First scikit-learn Solution, you'll gain the ability to identify the situations where scikit-learn is exactly the tool you are looking for, and also those situations where you need something else. First, you'll learn how scikit-learn’s niche is traditional machine learning, as opposed to deep learning or building neural networks. Next, you'll discover how seamlessly it integrates with core Python libraries. Then, you'll explore the typical set of steps needed to work with models in scikit-learn. Finally, you'll round out your knowledge by building your first scikit-learn regression and classification models. When you’re finished with this course, you'll have the skills and knowledge to identify precisely the situations when scikit-learn ought to be your tool of choice, and also how best to leverage the formidable capabilities of scikit-learn.
Register for this course and see more details by visiting:
OpenCourser.com/course/zty93e/building
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What's inside
Syllabus
Course Overview
Exploring scikit-learn for Machine Learning
Understanding the Machine Learning Workflow with scikit-learn
Building a Simple Machine Learning Model with scikit-learn
Read more
Syllabus
Course Overview
Exploring scikit-learn for Machine Learning
Understanding the Machine Learning Workflow with scikit-learn
Building a Simple Machine Learning Model with scikit-learn
Read more
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Meant for students who want to build their initial machine learning solution with scikit-learn
Starts with basic concepts then builds incrementally on more complex topics
Exposes students to feature engineering and making predictions with a trained model
Save this course
Save Building Your First scikit-learn Solution 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 Your First scikit-learn Solution with these
activities:
Review Machine Learning Concepts
Show steps
Reinforce your understanding of core machine learning concepts before diving into Scikit-learn.
Browse courses on
Machine Learning Fundamentals
Show steps
-
Review your notes or textbooks
-
Take practice quizzes or review online resources
Introduction to Machine Learning with Python
Show steps
Gain a foundational understanding of machine learning concepts and Scikit-learn's role.
View
Introduction to Machine Learning with Python: A...
on Amazon
Show steps
-
Read chapters on supervised and unsupervised learning
-
Review sections on model evaluation and selection
Show all two activities
Review Machine Learning Concepts
Show steps
Reinforce your understanding of core machine learning concepts before diving into Scikit-learn.
Browse courses on
Machine Learning Fundamentals
Show steps
Show steps
Show steps
- Review your notes or textbooks
- Take practice quizzes or review online resources
Introduction to Machine Learning with Python
Show steps
Gain a foundational understanding of machine learning concepts and Scikit-learn's role.
View
Introduction to Machine Learning with Python: A...
on Amazon
Show steps
Show steps
Show steps
- Read chapters on supervised and unsupervised learning
- Review sections on model evaluation and selection
Show all two activities
Career center
Learners who complete Building Your First scikit-learn Solution will develop knowledge and skills
that may be useful to these careers:
Data Scientist
Data Scientist
Data Scientists combine expertise in machine learning, statistics, and programming to extract meaningful insights from data. They use machine learning libraries such as scikit-learn to build models that can make predictions or classifications. This course provides a foundation in scikit-learn, making it a helpful resource for aspiring Data Scientists.
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers design and implement machine learning solutions. They use libraries like scikit-learn to build, deploy, and maintain machine learning models. This course covers the basics of scikit-learn, making it a useful resource for aspiring Machine Learning Engineers.
Business Analyst
Business Analyst
Business Analysts use data to understand and improve business processes. They use machine learning techniques to build models that can make predictions or classifications. This course can be useful for Business Analysts who want to use scikit-learn to build machine learning models for their projects.
Operations Research Analyst
Operations Research Analyst
Operations Research Analysts use mathematical and statistical models to solve complex business problems. They use machine learning techniques to build models that can optimize processes or decisions. This course can be useful for Operations Research Analysts who want to use scikit-learn to build machine learning models for their projects.
Statistician
Statistician
Statisticians use statistical methods to collect, analyze, and interpret data. They use machine learning techniques to build models that can make predictions or classifications. This course can be useful for Statisticians who want to use scikit-learn to build machine learning models for their projects.
Software Engineer
Software Engineer
Software Engineers use programming languages and tools to develop and maintain software applications. While not specific to machine learning, this course can be helpful for Software Engineers who want to incorporate machine learning into their projects. It provides a foundation in scikit-learn, which can be used for building machine learning models.
Data Analyst
Data Analyst
Data Analysts use data to solve business problems. They use machine learning techniques to identify patterns and trends in data. This course can be useful for Data Analysts who want to use scikit-learn to build machine learning models for their projects.
Financial Analyst
Financial Analyst
Financial Analysts use data to make investment decisions. They use machine learning techniques to build models that can predict the future performance of stocks or other financial instruments. This course can be useful for Financial Analysts who want to use scikit-learn to build machine learning models for their investment decisions.
Risk Analyst
Risk Analyst
Risk Analysts use data to identify and assess risks. They use machine learning techniques to build models that can predict the likelihood of future events. This course can be useful for Risk Analysts who want to use scikit-learn to build machine learning models for their projects.
Product Manager
Product Manager
Product Managers are responsible for managing the development and launch of new products. They use data to understand customer needs and market trends. This course can be useful for Product Managers who want to use scikit-learn to build machine learning models to inform their product decisions.
Consultant
Consultant
Consultants provide advice and guidance to businesses on a variety of topics, including machine learning. This course can be useful for Consultants who want to use scikit-learn to build machine learning models for their clients.
Quantitative Analyst
Quantitative Analyst
Quantitative Analysts use mathematical and statistical models to analyze financial data. They use machine learning techniques to build models that can make predictions or classifications. This course can be useful for Quantitative Analysts who want to use scikit-learn to build machine learning models for their projects.
Actuary
Actuary
Actuaries use mathematical and statistical models to assess risk. They use machine learning techniques to build models that can predict the likelihood of future events. This course can be useful for Actuaries who want to use scikit-learn to build machine learning models for their risk assessments.
Researcher
Researcher
Researchers conduct studies to investigate new ideas and theories. They use machine learning techniques to build models that can make predictions or classifications. This course can be useful for Researchers who want to use scikit-learn to build machine learning models for their research projects.
Educator
Educator
Educators teach students about a variety of subjects, including machine learning. This course can be useful for Educators who want to use scikit-learn to teach their students about machine learning.
For more career information including salaries, visit:
OpenCourser.com/course/zty93e/building
Reading list
We've selected ten 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 Your First scikit-learn Solution.
Provides a comprehensive overview of scikit-learn, covering all the major concepts and algorithms. It valuable resource for both beginners and experienced users.
Provides a comprehensive overview of machine learning with Python. It valuable resource for both beginners and experienced users.
Provides a gentle introduction to machine learning with Python. It valuable resource for those who are new to machine learning.
Provides a comprehensive overview of machine learning with R. It valuable resource for both beginners and experienced users.
Provides a comprehensive overview of deep learning with Python. It valuable resource for those who want to learn how to use deep learning to build and deploy deep learning models.
Provides a comprehensive overview of natural language processing with Python. It valuable resource for those who want to learn how to use natural language processing to build and deploy natural language processing models.
Provides a comprehensive overview of reinforcement learning with Python. It valuable resource for those who want to learn how to use reinforcement learning to build and deploy reinforcement learning models.
Provides a comprehensive overview of machine learning for finance. It valuable resource for those who want to learn how to use machine learning to build and deploy machine learning models for finance.
Provides a comprehensive overview of machine learning for healthcare. It valuable resource for those who want to learn how to use machine learning to build and deploy machine learning models for healthcare.
Save
Provides a comprehensive overview of machine learning for marketing. It valuable resource for those who want to learn how to use machine learning to build and deploy machine learning models for marketing.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/zty93e/building
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Time
127 hours
Level
Introductory
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 (viewing)
- 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
Good to know
Meant for students who want to build their initial machine learning solution with scikit-learn
Starts with basic concepts then builds incrementally on more complex topics
Exposes students to feature engineering and making predictions with a trained model
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