How to categorize machine learning problems, techniques, solutions and algorithms in one super flowchart, consisting of all possible categories? [on hold]AWS machine learning prediction schema problemsResearch in high-dimensional statistics vs. machine learning?How do AI's learn to act when the problem space is too bigAlgorithmic approach to model blendingDeciding the number of clusters in K-means clustering of descriptorsAlgorithms and techniques for spell checkingDifference between machine learning and artificial intelligenceUsing Machine Learning techniques for text-analysisHow to handle “unknown” category in machine learning classification problems?What are the common properties of all machine learning algorithms?
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How to categorize machine learning problems, techniques, solutions and algorithms in one super flowchart, consisting of all possible categories? [on hold]
AWS machine learning prediction schema problemsResearch in high-dimensional statistics vs. machine learning?How do AI's learn to act when the problem space is too bigAlgorithmic approach to model blendingDeciding the number of clusters in K-means clustering of descriptorsAlgorithms and techniques for spell checkingDifference between machine learning and artificial intelligenceUsing Machine Learning techniques for text-analysisHow to handle “unknown” category in machine learning classification problems?What are the common properties of all machine learning algorithms?
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I am trying to create a flowchart that categorizes everything under the sun that exists in machine learning. From the different techniques to the evaluation metrics to miscellaneous techniques used to assist machine learning (like stratified sampling, bias, overfitting etc) to categorization of techniques by the problem it solves versus the algorithm it uses. The problem itself seems so vast that iam unable to comprehend the solution. My tries have been pretty basic and resulted in the following :-
Machine Learning Auxillary Techniques (Stratified Sampling, Deciling, Dimensionality Reduction)
Machine Learning Evaluation Metrics (R Square, ROC Curve, Confusion Matrix, Log Loss etc)
Machine Learning Real World Problems (Game Theory, CustomerSegementation/Profiling, Pre Post Campaign Assessment, Genetics etc)
Machine Learning Techniques ( CLustering, Regression, Neural, Bayesian etc)
Machine Learning Problem Types (Supervised/Unsupervised/SemiSupervised/Reinforcement/Continuous/Discrete)
Do you think that these 5 consist of adequate number of categories to entrap the entirety of machine learning in its bounds (of course, with enough number of values for each category, i have listed only a fraction of the total number of techniques). If yes, then where can i get a good guide for helping me in this endeavor? Even a good answer will act as a good guide for me.
Thanks, Abhay
machine-learning data theory
asked Mar 19 at 6:39
Abhay SainiAbhay Saini
1
New contributor
Abhay Saini is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
$endgroup$
put on hold as primarily opinion-based by Mark.F, Toros91, bradS, Esmailian, Siong Thye Goh Mar 19 at 12:41
Many good questions generate some degree of opinion based on expert experience, but answers to this question will tend to be almost entirely based on opinions, rather than facts, references, or specific expertise. If this question can be reworded to fit the rules in the help center, please edit the question.
add a comment |
$begingroup$
I am trying to create a flowchart that categorizes everything under the sun that exists in machine learning. From the different techniques to the evaluation metrics to miscellaneous techniques used to assist machine learning (like stratified sampling, bias, overfitting etc) to categorization of techniques by the problem it solves versus the algorithm it uses. The problem itself seems so vast that iam unable to comprehend the solution. My tries have been pretty basic and resulted in the following :-
Machine Learning Auxillary Techniques (Stratified Sampling, Deciling, Dimensionality Reduction)
Machine Learning Evaluation Metrics (R Square, ROC Curve, Confusion Matrix, Log Loss etc)
Machine Learning Real World Problems (Game Theory, CustomerSegementation/Profiling, Pre Post Campaign Assessment, Genetics etc)
Machine Learning Techniques ( CLustering, Regression, Neural, Bayesian etc)
Machine Learning Problem Types (Supervised/Unsupervised/SemiSupervised/Reinforcement/Continuous/Discrete)
Do you think that these 5 consist of adequate number of categories to entrap the entirety of machine learning in its bounds (of course, with enough number of values for each category, i have listed only a fraction of the total number of techniques). If yes, then where can i get a good guide for helping me in this endeavor? Even a good answer will act as a good guide for me.
Thanks, Abhay
machine-learning data theory
asked Mar 19 at 6:39
Abhay SainiAbhay Saini
1
New contributor
Abhay Saini is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
$endgroup$
put on hold as primarily opinion-based by Mark.F, Toros91, bradS, Esmailian, Siong Thye Goh Mar 19 at 12:41
Many good questions generate some degree of opinion based on expert experience, but answers to this question will tend to be almost entirely based on opinions, rather than facts, references, or specific expertise. If this question can be reworded to fit the rules in the help center, please edit the question.
add a comment |
$begingroup$
I am trying to create a flowchart that categorizes everything under the sun that exists in machine learning. From the different techniques to the evaluation metrics to miscellaneous techniques used to assist machine learning (like stratified sampling, bias, overfitting etc) to categorization of techniques by the problem it solves versus the algorithm it uses. The problem itself seems so vast that iam unable to comprehend the solution. My tries have been pretty basic and resulted in the following :-
Machine Learning Auxillary Techniques (Stratified Sampling, Deciling, Dimensionality Reduction)
Machine Learning Evaluation Metrics (R Square, ROC Curve, Confusion Matrix, Log Loss etc)
Machine Learning Real World Problems (Game Theory, CustomerSegementation/Profiling, Pre Post Campaign Assessment, Genetics etc)
Machine Learning Techniques ( CLustering, Regression, Neural, Bayesian etc)
Machine Learning Problem Types (Supervised/Unsupervised/SemiSupervised/Reinforcement/Continuous/Discrete)
Do you think that these 5 consist of adequate number of categories to entrap the entirety of machine learning in its bounds (of course, with enough number of values for each category, i have listed only a fraction of the total number of techniques). If yes, then where can i get a good guide for helping me in this endeavor? Even a good answer will act as a good guide for me.
Thanks, Abhay
machine-learning data theory
asked Mar 19 at 6:39
Abhay SainiAbhay Saini
1
New contributor
Abhay Saini is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
$endgroup$
I am trying to create a flowchart that categorizes everything under the sun that exists in machine learning. From the different techniques to the evaluation metrics to miscellaneous techniques used to assist machine learning (like stratified sampling, bias, overfitting etc) to categorization of techniques by the problem it solves versus the algorithm it uses. The problem itself seems so vast that iam unable to comprehend the solution. My tries have been pretty basic and resulted in the following :-
Machine Learning Auxillary Techniques (Stratified Sampling, Deciling, Dimensionality Reduction)
Machine Learning Evaluation Metrics (R Square, ROC Curve, Confusion Matrix, Log Loss etc)
Machine Learning Real World Problems (Game Theory, CustomerSegementation/Profiling, Pre Post Campaign Assessment, Genetics etc)
Machine Learning Techniques ( CLustering, Regression, Neural, Bayesian etc)
Machine Learning Problem Types (Supervised/Unsupervised/SemiSupervised/Reinforcement/Continuous/Discrete)
Do you think that these 5 consist of adequate number of categories to entrap the entirety of machine learning in its bounds (of course, with enough number of values for each category, i have listed only a fraction of the total number of techniques). If yes, then where can i get a good guide for helping me in this endeavor? Even a good answer will act as a good guide for me.
Thanks, Abhay
machine-learning data theory
machine-learning data theory
asked Mar 19 at 6:39
Abhay SainiAbhay Saini
1
New contributor
Abhay Saini is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
asked Mar 19 at 6:39
Abhay SainiAbhay Saini
1
New contributor
Abhay Saini is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
asked Mar 19 at 6:39
Abhay SainiAbhay Saini
1
New contributor
Abhay Saini is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
asked Mar 19 at 6:39
Abhay SainiAbhay Saini
1
asked Mar 19 at 6:39
Abhay SainiAbhay Saini
1
1
New contributor
Abhay Saini is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
New contributor
Abhay Saini is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
Abhay Saini is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
put on hold as primarily opinion-based by Mark.F, Toros91, bradS, Esmailian, Siong Thye Goh Mar 19 at 12:41
Many good questions generate some degree of opinion based on expert experience, but answers to this question will tend to be almost entirely based on opinions, rather than facts, references, or specific expertise. If this question can be reworded to fit the rules in the help center, please edit the question.
put on hold as primarily opinion-based by Mark.F, Toros91, bradS, Esmailian, Siong Thye Goh Mar 19 at 12:41
Many good questions generate some degree of opinion based on expert experience, but answers to this question will tend to be almost entirely based on opinions, rather than facts, references, or specific expertise. If this question can be reworded to fit the rules in the help center, please edit the question.
add a comment |
add a comment |
1 Answer
1
active
oldest
votes
$begingroup$
This categorization misses Deep Learning techniques that are mix of multiple approaches (Such as Word Vectors and GAN )
Sci-kit has a pretty good flow chart for common algo :
answered Mar 19 at 7:10
Shamit VermaShamit Verma
90929
$endgroup$
add a comment |
1 Answer
1
active
oldest
votes
1 Answer
1
active
oldest
votes
active
oldest
votes
active
oldest
votes
$begingroup$
This categorization misses Deep Learning techniques that are mix of multiple approaches (Such as Word Vectors and GAN )
Sci-kit has a pretty good flow chart for common algo :
answered Mar 19 at 7:10
Shamit VermaShamit Verma
90929
$endgroup$
add a comment |
$begingroup$
This categorization misses Deep Learning techniques that are mix of multiple approaches (Such as Word Vectors and GAN )
Sci-kit has a pretty good flow chart for common algo :
answered Mar 19 at 7:10
Shamit VermaShamit Verma
90929
$endgroup$
add a comment |
$begingroup$
This categorization misses Deep Learning techniques that are mix of multiple approaches (Such as Word Vectors and GAN )
Sci-kit has a pretty good flow chart for common algo :
answered Mar 19 at 7:10
Shamit VermaShamit Verma
90929
$endgroup$
This categorization misses Deep Learning techniques that are mix of multiple approaches (Such as Word Vectors and GAN )
Sci-kit has a pretty good flow chart for common algo :
answered Mar 19 at 7:10
Shamit VermaShamit Verma
90929
answered Mar 19 at 7:10
Shamit VermaShamit Verma
90929
answered Mar 19 at 7:10
Shamit VermaShamit Verma
90929
answered Mar 19 at 7:10
Shamit VermaShamit Verma
90929
90929
add a comment |
add a comment |
