Online Course
Machine Learning for Data Analysis
- Provider Coursera
- Cost Free Online Course (Audit)
- Session In progress
- Language English
- Certificate Paid Certificate Available
- Duration 4 weeks long
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Overview
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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.
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.
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.
-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.
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.
Taught by
Jen Rose
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Caio T
4 years ago
by
is taking this course right now, spending 2 hours a week on it and found the course difficulty to be very easy.
Very shallow course.
I had just finished Andrew Ng's Machine Learning class and was looking for something to continue to build my knowledge in ML when I bumped into this course. It looked very interesting, as it covered algorithms that were not explored in Ng's course, like decision tree and random forests.
However, I was very disappointed when I found out that the actual class material expands very little on what is already written on the syllabus. It's an almost complete blackbox approach to the algorithms, with just some explanation of what is going on.
I had just finished Andrew Ng's Machine Learning class and was looking for something to continue to build my knowledge in ML when I bumped into this course. It looked very interesting, as it covered algorithms that were not explored in Ng's course, like decision tree and random forests.
However, I was very disappointed when I found out that the actual class material expands very little on what is already written on the syllabus. It's an almost complete blackbox approach to the algorithms, with just some explanation of what is going on.
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Anonymous
3 years ago
completed this course.
I thought the course was a fantastic way to introduce these concepts, which are becoming more widely used for data analysis
Was this review helpful to you?
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Colin K
Alvaro C
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