For a new comer like me in the field of computer science, I have just superficial knowledge of Soft-computing (Like ANN, Machine learning, Fuzzy logic etc.)
I want to be familiar with these topics but before to explore, I have many questions.
How to choose the best field among them for good and sustainable career ?
What are the future scopes ?
For the new comer or beginners, refer to the basic book of soft computing. For example, Soft Computing by D.K.Pratihar. You may also follow (a little advanced step):
1. Padhy, N. P., & Simon, S. P. (2015). Soft Computing: With MATLAB Programming. Oxford university press.
2. Karray, F. O., & De Silva, C. W. (2004). Soft computing and intelligent systems design: theory, tools, and applications. Pearson Education.
3. Burke, E. K., & Kendall, G. (2005). Search methodologies (pp. 1-17). Springer Science+ Business Media, Incorporated.
4. Deb, K. (2014). Multi-objective optimization. In Search methodologies (pp. 403-449). Springer, Boston, MA.
5. Marler, R. T., & Arora, J. S. (2004). Survey of multi-objective optimization methods for engineering. Structural and multidisciplinary optimization, 26(6), 369-395.
For the new comer or beginners, refer to the basic book of soft computing. For example, Soft Computing by D.K.Pratihar. You may also follow (a little advanced step):
1. Padhy, N. P., & Simon, S. P. (2015). Soft Computing: With MATLAB Programming. Oxford university press.
2. Karray, F. O., & De Silva, C. W. (2004). Soft computing and intelligent systems design: theory, tools, and applications. Pearson Education.
3. Burke, E. K., & Kendall, G. (2005). Search methodologies (pp. 1-17). Springer Science+ Business Media, Incorporated.
4. Deb, K. (2014). Multi-objective optimization. In Search methodologies (pp. 403-449). Springer, Boston, MA.
5. Marler, R. T., & Arora, J. S. (2004). Survey of multi-objective optimization methods for engineering. Structural and multidisciplinary optimization, 26(6), 369-395.
It will be necessary to develop the basic skills: data management (computer science foundation: algorithms, database, etc.) the elements of numerical analysis: matrix algebra and optimization; data analysis: basic statistics, factorial techniques, test theory, operational research, fuzzy set theory and applications, metaheuristics, ... pattern recognition, ... Descion systems, Data mining (Machine learning, NN, ...)
...........
sustainable career = ( Knowledge + Skills + Ability ) on Big data Analytics --> Data Science :)
For totally new comers I recommend to learn some easy and open high-level programming language. Python is the most popular option (maybe also the easier) in Machine Learning in general. Nowadays, the boom is mainly in Machine Learning, specially in Deep Learning. You can find good libraries with excellent documentation as:
http://scikit-learn.org/
https://www.tensorflow.org/
https://keras.io/
I also recommend an easy to follow book as:
Marsland, S. (2015). Machine learning: an algorithmic perspective. CRC press.
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