For what it is worth, as I see it, there at lest three major components to knowing what you are doing in applying machine learning:

• Understanding the mathematics behind it.
• Understanding the tools for implementing it.
• Understanding the domain in which you are applying it.

If you are mathematically mature, I would recommend working through The Elements of Statistical Machine Learning and learning R, if you don't already know it. If you are not, you might want to check-out the Handbook of Statistical Analysis and Data Mining Applications, instead of TEoSML. That will get you two-thirds of the way to a basic skill-set, fast.

There are some caveats, though. if you are interested in being some areas of commercial work, you might want to learn SAS programming, or some other language/platform, instead.

For what it is worth, as I see it, there at lest least three major components to knowing what you are doing in applying machine learning:

• Understanding the mathematics behind it. the methods.
• Understanding the tools for implementing it. them.
• Understanding the domain in which you are applying it.

If you are mathematically mature, I would recommend working through The Elements of Statistical Machine Learning and learning R, if you don't already know it. If you are not, you might want to check-out the Handbook of Statistical Analysis and Data Mining Applications, instead of TEoSML. That will get you two-thirds of the way to a basic skill-set, fast.

There are some caveats, though. if you are interested in being some areas of commercial work, you might want to learn SAS programming, or some other language/platform, instead.

For what it is worth, as I see it, there at least three major components to knowing what you are doing in applying machine learning:

• Understanding the mathematics behind the methods.
• Understanding the tools for implementing them.
• Understanding the domain in which you are applying it.

If you are mathematically mature, I would recommend working through The Elements of Statistical Machine Learning and learning R, if you don't already know it. If you are not, you might want to check-out the Handbook of Statistical Analysis and Data Mining Applications, instead of TEoSML. That will get you two-thirds of the way to a basic skill-set, fast.

There are some caveats, though. if you are interested in being some areas of commercial work, you might want to learn SAS programming, or some other language/platform, instead.instead of R. While R seems valued by high-end, analytically-oriented companies, many companies less focused on the bleeding-edge use SAS, MS SQL SSAS, etc.

For what it is worth, as I see it, there at least three major components to knowing what you are doing in applying machine learning:

• Understanding the mathematics behind the methods.
• Understanding the tools for implementing them.
• Understanding the domain in which you are applying it.your tools and methods.

If you are mathematically mature, I would recommend working through The Elements of Statistical Machine Learning and learning R, if you don't already know it. If you are not, you might want to check-out the Handbook of Statistical Analysis and Data Mining Applications, instead of TEoSML. That will get you two-thirds of the way to a basic skill-set, fast.

There are some caveats, though. if you are interested in being some areas of commercial work, you might want to learn SAS programming, or some other language/platform, instead of R. While R seems valued by high-end, analytically-oriented companies, many companies less focused on the bleeding-edge use SAS, MS SQL SSAS, etc.

For what it is worth, as I see it, there at least three major components to knowing what you are doing in applying machine learning:

• Understanding the mathematics behind the methods.
• Understanding the tools for implementing them.
• Understanding the domain in which you are applying your tools and methods.

If you are mathematically mature, I would recommend working through The Elements of Statistical Machine Learning and learning R, if you don't already know it. If you are not, you might want to check-out start with StatSoft's Electronic Statistics Textbook and/or purchase the Handbook of Statistical Analysis and Data Mining Applications, instead of TEoSML. That will get you two-thirds of the way to a basic skill-set, fast.

There are some caveats, though. if you are interested in being some areas of commercial work, you might want to learn SAS programming, or some other language/platform, instead of R. While R seems valued by high-end, analytically-oriented companies, many companies less focused on the bleeding-edge use SAS, MS SQL SSAS, etc.

For what it is worth, as I see it, there at least three major components to knowing what you are doing in applying machine learning:

• Understanding the mathematics behind the methods.
• Understanding the tools for implementing them.
• Understanding the domain in which you are applying your tools and methods.

If you are mathematically mature, I would recommend working through The Elements of Statistical Machine Learning and learning R, if you don't already know it. If you are not, you might want to start with StatSoft's Electronic Statistics Textbook and/or purchase the Handbook of Statistical Analysis and Data Mining Applications, instead of TEoSML. That will get you two-thirds of the way to a basic skill-set, fast.

There are some caveats, though. if you are interested in being some areas of commercial work, you might want to learn SAS programming, or some other language/platform, instead of R. While R seems valued by high-end, analytically-oriented companies, many companies less focused on the bleeding-edge use SAS, MS SQL SSAS, etc.