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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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Random Forests
In this session, you will learn about random forests, a type of data mining algorithm that can select from among a large number of variables those that are most important in determining the target or response variable to be explained. Unlike decision trees, the results of random forests generalize well to new data.
Lasso Regression
Lasso regression analysis is a shrinkage and variable selection method for linear regression models. The goal of lasso regression is to obtain the subset of predictors that minimizes prediction error for a quantitative response variable. The lasso does this by imposing a constraint on the model parameters that causes regression coefficients for some variables to shrink toward zero. Variables with a regression coefficient equal to zero after the shrinkage process are excluded from the model. Variables with non-zero regression coefficients variables are most strongly associated with the response variable. Explanatory variables can be either quantitative, categorical or both. In this session, you will apply and interpret a lasso regression analysis. You will also develop experience using k-fold cross validation to select the best fitting model and obtain a more accurate estimate of your model’s test error rate. To test a lasso regression model, you will need to identify a quantitative response variable from your data set if you haven’t already done so, and choose a few additional quantitative and categorical predictor (i.e. explanatory) variables to develop a larger pool of predictors. Having a larger pool of predictors to test will maximize your experience with lasso regression analysis. Remember that lasso regression is a machine learning method, so your choice of additional predictors does not necessarily need to depend on a research hypothesis or theory. Take some chances, and try some new variables. The lasso regression analysis will help you determine which of your predictors are most important. Note also that if you are working with a relatively small data set, you do not need to split your data into training and test data sets. The cross-validation method you apply is designed to eliminate the need to split your data when you have a limited number of observations.
K-Means Cluster Analysis
Cluster analysis is an unsupervised machine learning method that partitions the observations in a data set into a smaller set of clusters where each observation belongs to only one cluster. The goal of cluster analysis is to group, or cluster, observations into subsets based on their similarity of responses on multiple variables. Clustering variables should be primarily quantitative variables, but binary variables may also be included. In this session, we will show you how to use k-means cluster analysis to identify clusters of observations in your data set. You will gain experience in interpreting cluster analysis results by using graphing methods to help you determine the number of clusters to interpret, and examining clustering variable means to evaluate the cluster profiles. Finally, you will get the opportunity to validate your cluster solution by examining differences between clusters on a variable not included in your cluster analysis. You can use the same variables that you have used in past weeks as clustering variables. If most or all of your previous explanatory variables are categorical, you should identify some additional quantitative clustering variables from your data set. Ideally, most of your clustering variables will be quantitative, although you may also include some binary variables. In addition, you will need to identify a quantitative or binary response variable from your data set that you will not include in your cluster analysis. You will use this variable to validate your clusters by evaluating whether your clusters differ significantly on this response variable using statistical methods, such as analysis of variance or chi-square analysis, which you learned about in Course 2 of the specialization (Data Analysis Tools). Note also that if you are working with a relatively small data set, you do not need to split your data into training and test data sets.

Good to know

Know what's good
, what to watch for
, 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

Well-received machine learning course

Learners say this well-received machine learning course is engaging and insightful. According to students, the course teaches core concepts and provides hands-on experience. However, some learners note that assignments could be improved.
Learners complete projects to apply their learning.
"Nice"
"EXCELLENT"
"very good"
Course covers the basics of machine learning.
"Great course about machine learning methods"
"I thought the course was a fantastic way to introduce these concepts"
"Good Course"
Coursework keeps learners engaged.
"Clear and explanatory approach to the object."
"Great Learning"
"Good to learn "
Some learners have concerns about assignment quality.
"One of the worst online courses I have taken"
"the quality of assignments needs to be massively improved"

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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.

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.
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.
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.
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.
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.
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.
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.
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.
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.
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.

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