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Janani Ravi
This course covers important techniques in data preparation, data cleaning and feature selection that are needed to set your machine learning model up for success. You will also learn how to use imputation to deal with missing data and strategies for identifying and coping with outliers.
As Machine Learning explodes in popularity, it is becoming ever more important to know precisely how to prepare the data going into the model in a manner appropriate to the problem we are trying to solve.
Read more
This course covers important techniques in data preparation, data cleaning and feature selection that are needed to set your machine learning model up for success. You will also learn how to use imputation to deal with missing data and strategies for identifying and coping with outliers.
As Machine Learning explodes in popularity, it is becoming ever more important to know precisely how to prepare the data going into the model in a manner appropriate to the problem we are trying to solve.
Read more
This course covers important techniques in data preparation, data cleaning and feature selection that are needed to set your machine learning model up for success. You will also learn how to use imputation to deal with missing data and strategies for identifying and coping with outliers.
As Machine Learning explodes in popularity, it is becoming ever more important to know precisely how to prepare the data going into the model in a manner appropriate to the problem we are trying to solve.
In this course, Preparing Data for Machine Learning* you will gain the ability to explore, clean, and structure your data in ways that get the best out of your machine learning model.
First, you will learn why data cleaning and data preparation are so important, and how missing data, outliers, and other data-related problems can be solved. Next, you will discover how models that read too much into data suffer from a problem called overfitting, in which models perform well under test conditions but struggle in live deployments. You will also understand how models that are trained with insufficient or unrepresentative data suffer from a different set of problems, and how these problems can be mitigated.
Finally, you will round out your knowledge by applying different methods for feature selection, dealing with missing data using imputation, and building your models using the most relevant features.
When you’re finished with this course, you will have the skills and knowledge to identify the right data procedures for data cleaning and data preparation to set your model up for success.
Register for this course and see more details by visiting:
OpenCourser.com/course/7tq0q0/preparing
What's inside
Syllabus
Course Overview
Understanding the Need for Data Preparation
Implementing Data Cleaning and Transformation
Transforming Continuous and Categorical Data
Read more
Syllabus
Course Overview
Understanding the Need for Data Preparation
Implementing Data Cleaning and Transformation
Transforming Continuous and Categorical Data
Read more
Understanding Feature Selection
Implementing Feature Selection
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Helps learners gain a strong foundation in data cleaning, preparation, and feature selection for machine learning models
Provides practical techniques for dealing with missing data and outliers, which are common challenges in data preparation
Teaches learners how to identify and mitigate problems that arise from overfitting and underfitting models
Covers various methods for feature selection, including techniques for categorical and continuous data
Helps learners build models using the most relevant features, which can improve model performance and efficiency
Save this course
Save Preparing Data for Machine Learning 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 Preparing Data for Machine Learning with these
activities:
Review data manipulation and cleaning techniques.
Show steps
Ensure foundational understanding of data manipulation techniques is strong before the class begins.
Browse courses on
Data Manipulation
Show steps
-
Review your notes on data manipulation techniques.
-
Complete practice problems on data manipulation.
-
Review online tutorials on data cleaning.
Read "Data Preparation for Machine Learning" by Jason Brownlee.
Show steps
Gain a comprehensive understanding of data preparation techniques and best practices.
Show steps
-
Read the book.
-
Take notes on the key concepts.
-
Complete the exercises in the book.
Complete the Pluralsight tutorial on data imputation.
Show steps
Deepen understanding of data imputation techniques and how to apply them in real-world scenarios.
Show steps
-
Watch the Pluralsight tutorial on data imputation.
-
Complete the exercises in the tutorial.
Three other activities
Expand to see all activities and additional details
Show all six activities
Form a study group with other students in the course.
Show steps
Collaborate with classmates to improve understanding and retention.
Show steps
-
Find other students in the course who are interested in forming a study group.
-
Decide on a meeting time and place.
-
Meet regularly to discuss the course material.
Practice feature selection techniques on real-world datasets.
Show steps
Develop proficiency in applying feature selection techniques to improve model performance.
Browse courses on
Feature Selection
Show steps
-
Find a real-world dataset to work with.
-
Apply different feature selection techniques to the dataset.
-
Evaluate the performance of your models with and without feature selection.
Build a machine learning model using the techniques learned in the course.
Show steps
Apply the concepts learned in the course to build a complete machine learning model.
Browse courses on
Machine Learning Model
Show steps
-
Choose a dataset to work with.
-
Preprocess the data using the techniques learned in the course.
-
Build a machine learning model.
-
Evaluate the performance of the model.
Review data manipulation and cleaning techniques.
Show steps
Ensure foundational understanding of data manipulation techniques is strong before the class begins.
Browse courses on
Data Manipulation
Show steps
Show steps
Show steps
- Review your notes on data manipulation techniques.
- Complete practice problems on data manipulation.
- Review online tutorials on data cleaning.
Read "Data Preparation for Machine Learning" by Jason Brownlee.
Show steps
Gain a comprehensive understanding of data preparation techniques and best practices.
Show steps
Show steps
Show steps
- Read the book.
- Take notes on the key concepts.
- Complete the exercises in the book.
Complete the Pluralsight tutorial on data imputation.
Show steps
Deepen understanding of data imputation techniques and how to apply them in real-world scenarios.
Show steps
Show steps
Show steps
- Watch the Pluralsight tutorial on data imputation.
- Complete the exercises in the tutorial.
Three other activities
Expand to see all activities and additional details
Show all six activities
Form a study group with other students in the course.
Show steps
Collaborate with classmates to improve understanding and retention.
Show steps
Show steps
Show steps
- Find other students in the course who are interested in forming a study group.
- Decide on a meeting time and place.
- Meet regularly to discuss the course material.
Practice feature selection techniques on real-world datasets.
Show steps
Develop proficiency in applying feature selection techniques to improve model performance.
Browse courses on
Feature Selection
Show steps
Show steps
Show steps
- Find a real-world dataset to work with.
- Apply different feature selection techniques to the dataset.
- Evaluate the performance of your models with and without feature selection.
Build a machine learning model using the techniques learned in the course.
Show steps
Apply the concepts learned in the course to build a complete machine learning model.
Browse courses on
Machine Learning Model
Show steps
Show steps
Show steps
- Choose a dataset to work with.
- Preprocess the data using the techniques learned in the course.
- Build a machine learning model.
- Evaluate the performance of the model.
Career center
Learners who complete Preparing Data for Machine Learning will develop knowledge and skills
that may be useful to these careers:
Data Scientist
Data Scientist
A Data Scientist is a professional who uses data to solve problems. They may work in a variety of industries, including finance, healthcare, and technology. Data Scientists use a variety of techniques to clean, analyze, and interpret data. They may also develop machine learning models to help them make predictions or recommendations. The Preparing Data for Machine Learning course can help you build the skills you need to be a successful Data Scientist. You will learn how to clean and prepare data, as well as how to select the right features for your machine learning models.
Machine Learning Engineer
Machine Learning Engineer
A Machine Learning Engineer is a professional who designs, builds, and deploys machine learning models. They work with data scientists to identify the right problems to solve with machine learning, and they develop the models that will solve those problems. Machine Learning Engineers also work with software engineers to deploy machine learning models into production. The Preparing Data for Machine Learning course can help you build the skills you need to be a successful Machine Learning Engineer. You will learn how to clean and prepare data, as well as how to select the right features for your machine learning models.
Data Analyst
Data Analyst
A Data Analyst is a professional who collects, analyzes, and interprets data. They may work in a variety of industries, including finance, healthcare, and technology. Data Analysts use a variety of techniques to clean, analyze, and interpret data. They may also develop visualizations to help them communicate their findings. The Preparing Data for Machine Learning course can help you build the skills you need to be a successful Data Analyst. You will learn how to clean and prepare data, as well as how to select the right features for your machine learning models.
Business Analyst
Business Analyst
A Business Analyst is a professional who helps organizations improve their business processes. They work with stakeholders to identify problems, develop solutions, and implement those solutions. Business Analysts often use data to support their work. The Preparing Data for Machine Learning course can help you build the skills you need to be a successful Business Analyst. You will learn how to clean and prepare data, as well as how to select the right features for your machine learning models.
Statistician
Statistician
A Statistician is a professional who collects, analyzes, and interprets data. They may work in a variety of industries, including finance, healthcare, and government. Statisticians use a variety of techniques to clean, analyze, and interpret data. They may also develop statistical models to help them make predictions or recommendations. The Preparing Data for Machine Learning course can help you build the skills you need to be a successful Statistician. You will learn how to clean and prepare data, as well as how to select the right features for your machine learning models.
Software Engineer
Software Engineer
A Software Engineer is a professional who designs, builds, and tests software. They may work in a variety of industries, including technology, finance, and healthcare. Software Engineers use a variety of programming languages and technologies to develop software. The Preparing Data for Machine Learning course may be useful for Software Engineers who want to learn more about how to clean and prepare data for machine learning models.
Data Engineer
Data Engineer
A Data Engineer is a professional who builds and maintains data pipelines. They work with data scientists and machine learning engineers to ensure that data is available in a timely and reliable manner. Data Engineers also work with software engineers to develop the infrastructure needed to support data pipelines. The Preparing Data for Machine Learning course may be useful for Data Engineers who want to learn more about how to clean and prepare data for machine learning models.
Database Administrator
Database Administrator
A Database Administrator is a professional who manages and maintains databases. They work with data engineers and software engineers to ensure that databases are available and performant. Database Administrators also work with security professionals to ensure that databases are secure. The Preparing Data for Machine Learning course may be useful for Database Administrators who want to learn more about how to clean and prepare data for machine learning models.
Product Manager
Product Manager
A Product Manager is a professional who is responsible for the development and launch of new products. They work with engineers, designers, and marketing professionals to ensure that products meet the needs of customers. Product Managers also work with data analysts to track the performance of products and identify areas for improvement. The Preparing Data for Machine Learning course may be useful for Product Managers who want to learn more about how to clean and prepare data for machine learning models.
Marketing Analyst
Marketing Analyst
A Marketing Analyst is a professional who analyzes data to understand customer behavior. They work with marketing professionals to develop and implement marketing campaigns. Marketing Analysts also work with data scientists to develop machine learning models to help them predict customer behavior. The Preparing Data for Machine Learning course may be useful for Marketing Analysts who want to learn more about how to clean and prepare data for machine learning models.
Financial Analyst
Financial Analyst
A Financial Analyst is a professional who analyzes financial data to make investment recommendations. They work with portfolio managers and other financial professionals to develop and implement investment strategies. Financial Analysts also work with data scientists to develop machine learning models to help them make investment decisions. The Preparing Data for Machine Learning course may be useful for Financial Analysts who want to learn more about how to clean and prepare data for machine learning models.
Operations Research Analyst
Operations Research Analyst
An Operations Research Analyst is a professional who uses mathematical models to solve business problems. They work with businesses to identify problems, develop solutions, and implement those solutions. Operations Research Analysts also work with data scientists to develop machine learning models to help them make decisions. The Preparing Data for Machine Learning course may be useful for Operations Research Analysts who want to learn more about how to clean and prepare data for machine learning models.
Risk Manager
Risk Manager
A Risk Manager is a professional who identifies and manages risks. They work with businesses to identify risks, develop mitigation plans, and implement those plans. Risk Managers also work with data scientists to develop machine learning models to help them identify and manage risks. The Preparing Data for Machine Learning course may be useful for Risk Managers who want to learn more about how to clean and prepare data for machine learning models.
Compliance Officer
Compliance Officer
A Compliance Officer is a professional who ensures that a business complies with laws and regulations. They work with businesses to identify and mitigate compliance risks. Compliance Officers also work with data scientists to develop machine learning models to help them identify and manage compliance risks. The Preparing Data for Machine Learning course may be useful for Compliance Officers who want to learn more about how to clean and prepare data for machine learning models.
Auditor
Auditor
An Auditor is a professional who examines financial records to ensure that they are accurate and complete. They work with businesses to identify and mitigate financial risks. Auditors also work with data scientists to develop machine learning models to help them identify and manage financial risks. The Preparing Data for Machine Learning course may be useful for Auditors who want to learn more about how to clean and prepare data for machine learning models.
For more career information including salaries, visit:
OpenCourser.com/course/7tq0q0/preparing
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
Preparing Data for Machine Learning.
Provides a practical introduction to machine learning using Python. It covers a wide range of topics, from data preparation to model evaluation. It good resource for anyone who wants to learn how to build and deploy machine learning models.
Provides a practical introduction to deep learning using Python. It covers topics such as neural networks, convolutional neural networks, and recurrent neural networks. It good resource for anyone who wants to learn how to build and deploy deep learning models.
Provides a comprehensive overview of Keras, a popular high-level neural networks API, written in Python. It covers topics such as building and training neural networks, data preprocessing, and model evaluation. It good resource for anyone who wants to learn how to build and deploy deep learning models using Keras.
Provides a comprehensive overview of machine learning using R, a popular programming language for statistical computing. It covers topics such as data preparation, model evaluation, and model deployment. It good resource for anyone who wants to learn how to build and deploy machine learning models using R.
Provides a practical introduction to data science for business professionals. It covers topics such as data visualization, data mining, and machine learning. It good resource for anyone who wants to learn how to use data to make better business decisions.
Provides a comprehensive overview of statistical learning methods. It covers topics such as supervised learning, unsupervised learning, and model selection. It valuable resource for anyone who wants to learn about the different ways to use statistics to make predictions and decisions.
Provides a comprehensive overview of statistical learning methods, with a focus on applications in data mining and bioinformatics. It valuable resource for anyone who wants to learn about the latest advances in statistical learning.
Provides a practical introduction to machine learning for hackers. It covers topics such as data cleaning, feature engineering, and model evaluation. It good resource for anyone who wants to learn how to build and deploy machine learning models without getting bogged down in the details.
Save
Provides a comprehensive overview of deep learning, covering topics such as neural networks, convolutional neural networks, and recurrent neural networks. It valuable resource for anyone who wants to learn about the latest advances in deep learning.
Save
Provides a comprehensive overview of data mining concepts and techniques. It valuable resource for anyone who wants to learn about the different ways to extract knowledge from data.
Provides a probabilistic perspective on machine learning. It covers topics such as Bayesian inference, graphical models, and reinforcement learning. It valuable resource for anyone who wants to understand the theoretical foundations of machine learning.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/7tq0q0/preparing
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Time
204 hours
Level
Introductory
Via
Pluralsight
Instructor
Janani Ravi
Language
English
This course belongs to a
collection
called
Machine Learning Literacy. It's comprised of five courses:
- Preparing Data for Machine Learning (viewing)
- Designing a Machine Learning Model
- Creating Machine Learning Models
- Deploying Machine Learning Solutions
- Building Your First Machine Learning Solution
Good to know
Helps learners gain a strong foundation in data cleaning, preparation, and feature selection for machine learning models
Provides practical techniques for dealing with missing data and outliers, which are common challenges in data preparation
Teaches learners how to identify and mitigate problems that arise from overfitting and underfitting models
Covers various methods for feature selection, including techniques for categorical and continuous data
Helps learners build models using the most relevant features, which can improve model performance and efficiency
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