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Machine Learning for Data Analysis

Jen Rose and Lisa Dierker

Are you interested in predicting future outcomes using your data? This course helps you do just that! Machine learning is the process of developing, testing, and applying predictive algorithms to achieve this goal. Make sure to familiarize yourself with course 3 of this specialization before diving into these machine learning concepts. Building on Course 3, which introduces students to integral supervised machine learning concepts, this course will provide an overview of many additional concepts, techniques, and algorithms in machine learning, from basic classification to decision trees and clustering. By completing this course, you will learn how to apply, test, and interpret machine learning algorithms as alternative methods for addressing your research questions.

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What's inside

Syllabus

Decision Trees
In this session, you will learn about decision trees, a type of data mining algorithm that can select from among a large number of variables those and their interactions that are most important in predicting the target or response variable to be explained. Decision trees create segmentations or subgroups in the data, by applying a series of simple rules or criteria over and over again, which choose variable constellations that best predict the target variable.
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Traffic lights

Read about what's good
what should give you pause
and possible dealbreakers
Builds a strong foundation for developing predictive algorithms
Taught by Jen Rose and Lisa Dierker, who are recognized for their work in machine learning
Develops skills and knowledge that are highly relevant to industry
Provides a comprehensive study of machine learning concepts
Covers unique perspectives and ideas that may add color to other topics and subjects
Requires prerequisite knowledge of supervised machine learning concepts

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Reviews summary

Practical machine learning for data analysis

According to learners, this course provides an excellent overview of machine learning methods specifically for data analysis, serving as a valuable follow-up to prior courses in the specialization. Students particularly praise the practical assignments and labs, which help solidify understanding and allow them to apply techniques to real data. Explanations are generally found to be clear, making complex topics approachable. However, a significant number of reviews highlight the need for a very strong prior statistical and data analysis background, stressing that the course assumes considerable prerequisite knowledge. Some also note the course is more conceptual than deeply coding-focused and that lectures can occasionally feel dry or less engaging.
Provides an overview, not a deep theoretical dive.
"Excellent overview of machine learning methods for data analysis!"
"If you're looking for deep theoretical ML, this isn't it, but for applying it in analysis, it's perfect."
"Good practical focus overall."
More conceptual than focused on deep coding.
"...while they mentioned R in the labs, the connection between the theory and actual coding wasn't strong enough for me."
"Expected more practical coding guidance."
"...it's more conceptual than coding-focused."
Concepts are mostly explained clearly.
"The lectures were clear and concise, covering decision trees, random forests, lasso regression, and k-means clustering effectively."
"The explanations were mostly good..."
"The way they explained complex topics like random forests and clustering made them seem approachable."
"The modules on decision trees and k-means were particularly clear and useful."
"The course covers the promised topics, but the delivery could be more engaging."
"Some lectures felt a bit dry."
Focus on applying ML techniques is strong.
"The assignments were practical and helped solidify the understanding of how to apply these techniques."
"I particularly appreciated the focus on interpreting the results and applying the models to real-world-like scenarios."
"The hands-on labs were invaluable."
"Useful course for understanding how to apply common ML algorithms..."
"The practical exercises were key."
Strong prior background is definitely needed.
"Building on Course 3 is definitely necessary, as the course assumes familiarity with basic concepts and tools."
"As others have said, make sure you've completed the prerequisite courses..."
"Assumed more prior statistical knowledge than just completing Course 3 might provide."
"It assumes a level of statistical and mathematical maturity..."
"you REALLY need a solid data analysis background..."
"prerequisite knowledge from Course 3 and earlier is crucial. Don't skip them or you will struggle."

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 Machine Learning for Data Analysis with these activities:
Solve practice problems on machine learning concepts
Solve practice problems on machine learning concepts to reinforce your understanding and improve your problem-solving skills.
Show steps
  • Find a set of practice problems on machine learning concepts.
  • Attempt to solve the problems without looking at any solutions.
  • Check your solutions against the provided answers.
Follow a tutorial on how to use machine learning algorithms in a programming language
Follow a tutorial on how to use machine learning algorithms in a programming language to gain practical experience in implementing and applying machine learning models.
Browse courses on Machine Learning Algorithms
Show steps
  • Find a tutorial on how to use machine learning algorithms in a programming language.
  • Follow the steps outlined in the tutorial.
  • Apply the machine learning algorithm to a real-world dataset.
Follow a tutorial on how to perform lasso regression analysis
Follow a tutorial on how to perform lasso regression analysis to gain practical experience and enhance your understanding of its applications.
Browse courses on Lasso Regression
Show steps
  • Find a tutorial on lasso regression analysis.
  • Follow the steps outlined in the tutorial.
  • Apply the lasso regression model to a real-world dataset.
Four other activities
Expand to see all activities and additional details
Show all seven activities
Practice using decision trees on real-world data
Practice using decision trees on real-world data to gain hands-on experience and improve your understanding of their applications.
Browse courses on Decision Trees
Show steps
  • Find a dataset that is suitable for decision tree analysis.
  • Load the dataset into a programming environment.
  • Create a decision tree model using the dataset.
  • Evaluate the performance of the decision tree model.
Create a concept map of the different machine learning algorithms
Create a concept map of the different machine learning algorithms to visualize their relationships, understand their strengths and weaknesses, and improve your overall comprehension.
Browse courses on Machine Learning Algorithms
Show steps
  • Gather information about the different machine learning algorithms.
  • Organize the information into a logical structure.
  • Create a visual representation of the concept map.
Create a presentation on the benefits of using random forests
Create a presentation on the benefits of using random forests to enhance your understanding of their advantages and how to communicate their value.
Browse courses on Random Forests
Show steps
  • Gather information about the benefits of using random forests.
  • Organize the information into a logical flow.
  • Create visual aids to support your presentation.
  • Practice delivering your presentation.
Develop a k-means cluster analysis project
Develop a k-means cluster analysis project to apply your knowledge, gain hands-on experience, and improve your understanding of clustering techniques.
Show steps
  • Define the research question or problem you want to address.
  • Gather a dataset that is suitable for cluster analysis.
  • Choose the number of clusters to create.
  • Apply the k-means cluster analysis algorithm to the dataset.
  • Evaluate the results of the cluster analysis.

Career center

Learners who complete Machine Learning for Data Analysis will develop knowledge and skills that may be useful to these careers:
Machine Learning Engineer
Machine Learning Engineers design, build, and deploy machine learning models. This specialization provides you with the machine learning algorithms, statistical techniques, and software engineering skills you need to become a successful machine learning engineer.
See salaries and explore the career path for Machine Learning Engineer
Statistician
Statisticians use statistical models and methods to analyze data and draw conclusions. This specialization provides you with the statistical models, machine learning algorithms, and software skills you need to become a successful statistician.
See salaries and explore the career path for Statistician
Quantitative Analyst
Quantitative Analysts use mathematical and statistical models to analyze financial data and make investment decisions. This specialization provides you with the statistical models, machine learning algorithms, and software skills you need to become a successful quantitative analyst.
See salaries and explore the career path for Quantitative Analyst
Biostatistician
Biostatisticians use statistical models and methods to analyze data from biological and medical studies. This specialization provides you with the statistical models, machine learning algorithms, and software skills you need to become a successful biostatistician.
See salaries and explore the career path for Biostatistician
Actuary
Actuaries use statistical models and methods to assess risk and make financial decisions. This specialization provides you with the statistical models, machine learning algorithms, and software skills you need to become a successful actuary.
See salaries and explore the career path for Actuary
Data Scientist
Data Scientists use advanced statistical models and machine learning algorithms to solve complex business problems. This specialization provides you with not only machine learning algorithms, but also the statistical models you need to become a successful data scientist.
See salaries and explore the career path for Data Scientist
Market Researcher
Market Researchers use data and analysis to help organizations understand their customers and make better marketing decisions. This specialization provides you with the statistical models, machine learning algorithms, and software skills you need to become a successful market researcher.
See salaries and explore the career path for Market Researcher
Business Analyst
Business Analysts use data and analysis to help organizations make better decisions. This specialization provides you with the statistical models, machine learning algorithms, and software skills you need to become a successful business analyst.
See salaries and explore the career path for Business Analyst
Financial Analyst
Financial Analysts use data and analysis to help organizations make better financial decisions. This specialization provides you with the statistical models, machine learning algorithms, and software skills you need to become a successful financial analyst.
See salaries and explore the career path for Financial Analyst
Data Analyst
Data Analysts use statistical models to help organizations make informed decisions. This course teaches not only statistical models, but also the latest machine learning algorithms. By completing this specialization, you will be well-equipped to get started in your data analyst career.
See salaries and explore the career path for Data Analyst
Operations Research Analyst
Operations Research Analysts use data and analysis to help organizations make better decisions about their operations. This specialization provides you with the statistical models, machine learning algorithms, and software skills you need to become a successful operations research analyst.
See salaries and explore the career path for Operations Research Analyst
Database Administrator
Database Administrators design, build, and maintain databases. This specialization provides you with the statistical models, machine learning algorithms, and software engineering skills you need to become a successful database administrator.
See salaries and explore the career path for Database Administrator
Data Engineer
Data Engineers design, build, and maintain data infrastructure. This specialization provides you with the statistical models, machine learning algorithms, and software engineering skills you need to become a successful data engineer.
See salaries and explore the career path for Data Engineer
Data Architect
Data Architects design, build, and maintain data systems. This specialization provides you with the statistical models, machine learning algorithms, and software engineering skills you need to become a successful data architect.
See salaries and explore the career path for Data Architect
Software Engineer
Software Engineers design, build, and maintain software applications. This specialization provides you with the machine learning algorithms and software engineering skills you need to become a successful software engineer.
See salaries and explore the career path for Software Engineer

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 Machine Learning for Data Analysis.
Cover image
Hands-On Machine Learning with Scikit-Learn, Keras,...
Save
Provides practical guidance on implementing machine learning algorithms using popular Python libraries such as Scikit-Learn, Keras, and TensorFlow. It valuable resource for students who want to gain hands-on experience with machine learning.
Hands-On Machine Learning with Scikit-Learn, Keras,...
Paperback
$$
Hands-On Machine Learning with Scikit-Learn, Keras,...
Paperback
$$$
Hands-On Machine Learning with Scikit-Learn, Keras,...
Kindle Edition
$$
Cover image
An Introduction to Statistical Learning
Save
Provides a comprehensive introduction to statistical learning methods, including supervised and unsupervised learning. It valuable resource for students who want to gain a deeper understanding of the statistical foundations of machine learning.
An Introduction to Statistical Learning
Paperback
Check
An Introduction to Statistical Learning
Kindle Edition
Check
Cover image
Machine Learning with R
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Provides a comprehensive introduction to machine learning using R, including supervised and unsupervised learning. It valuable resource for students who want to learn how to use R for machine learning.
Machine Learning with R - Fourth Edition: Learn...
Paperback
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Machine Learning with R - Second Edition: Expert...
Kindle Edition
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Machine Learning with R - Third Edition: Expert...
Paperback
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(Italiano) Machine Learning con R: Conoscere le tecniche per...
Kindle Edition
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R: Unleash Machine Learning Techniques
Kindle Edition
$$$
Machine Learning with R: Learn How to Use R to...
Paperback
$$
(中文) Machine learning and R language (3rd edition of the...
Paperback
$$
Cover image
Deep Learning
Save
Provides a comprehensive overview of deep learning, including convolutional neural networks, recurrent neural networks, and generative adversarial networks. It valuable resource for students who want to gain a deeper understanding of deep learning.
Deep Learning
Paperback
$$$
Deep Learning (Adaptive Computation and Machine...
Hardcover
$$$
Deep Learning (Adaptive Computation and Machine...
Kindle Edition
$$$
Cover image
Reinforcement Learning, second edition
Save
Provides a comprehensive introduction to reinforcement learning, including value functions, policy gradients, and deep reinforcement learning. It valuable resource for students who want to gain a deeper understanding of reinforcement learning.
Reinforcement Learning, second edition: An...
Hardcover
$$$
Reinforcement Learning, second edition: An...
Kindle Edition
$$$
Cover image
Bayesian Data Analysis, Third Edition
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Provides a comprehensive introduction to Bayesian data analysis, including Bayesian inference, model checking, and Bayesian computation. It valuable resource for students who want to learn how to use Bayesian methods for data analysis.
Bayesian Data Analysis (Chapman & Hall/CRC Texts in...
Kindle Edition
$$$$
Cover image
Learning with Kernels
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Provides a comprehensive introduction to kernel methods for machine learning, including support vector machines, kernel principal component analysis, and Gaussian processes. It valuable resource for students who want to gain a deeper understanding of kernel methods.
Learning with Kernels: Support Vector Machines,...
Paperback
$$$
Learning Theory and Kernel Machines: 16th Annual...
Paperback
$$$$
Cover image
Probabilistic Graphical Models
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Provides a comprehensive introduction to graphical models for machine learning and artificial intelligence, including Bayesian networks, Markov random fields, and Gaussian graphical models. It valuable resource for students who want to gain a deeper understanding of graphical models.
Probabilistic Graphical Models: Principles and...
Hardcover
$$$$
Probabilistic Graphical Models: Principles and...
Kindle Edition
$$$
Pattern Recognition and Machine Learning
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Provides a comprehensive introduction to pattern recognition and machine learning, including supervised and unsupervised learning, as well as more advanced topics such as neural networks and graphical models. It valuable resource for students who want to gain a deeper understanding of pattern recognition and machine learning.
Pattern Recognition and Machine Learning...
Hardcover
$$$
Pattern Recognition and Machine Learning...
Paperback
$$$
Pattern Recognition and Machine Learning (text...
Unknown Binding
$$$$
Cover image
Machine Learning in Action
Save
Provides a practical guide to machine learning, including supervised and unsupervised learning, feature engineering, and model evaluation. It valuable resource for students who want to learn how to use machine learning for practical applications.
Machine Learning in Action
Paperback
$$
(中文) 机器学习实战 (图灵程序设计丛书 72) (Chinese Edition)
Kindle Edition
$

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Similar courses

Similar courses are unavailable at this time. Please try again later.
Via
Coursera
Institution
Wesleyan University
Instructors
Jen Rose
Lisa Dierker
Language
English
Transcripts
Español Français Português Deutsch Italiano 中文 العربية 한국어 日本語 हिंदी Nederlands Svenska Pусский Türkçe Polski Ελληνικά українська мова magyar nyelv Bahasa Indonesia ไทย қазақша اُردُو, فارسی বাংলা tiếng Việt پښتو آذربایجان دیلی
This course belongs to a collection called Data Analysis and Interpretation. It's comprised of five courses:
  1. Data Analysis and Interpretation Capstone
  2. Data Analysis Tools
  3. Data Management and Visualization
  4. Regression Modeling in Practice
  5. Machine Learning for Data Analysis (viewing)
Traffic lights
Read about what's good
what should give you pause
and possible dealbreakers
Builds a strong foundation for developing predictive algorithms
Taught by Jen Rose and Lisa Dierker, who are recognized for their work in machine learning
Develops skills and knowledge that are highly relevant to industry
Provides a comprehensive study of machine learning concepts
Covers unique perspectives and ideas that may add color to other topics and subjects
Requires prerequisite knowledge of supervised machine learning concepts
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