Is it necessary to learn every library of python before proceeding to Machine Learning?
Like libraries of python numpy, pandas, sci-py.
Is it necessary to learn all above stated libraries before proceeding to sci-kit learn?
Not really. You could start off by installing "Anaconda Python" - this is a popular package for data analysis and machine learning. Next, you could experiment with the "Numpy" library through which you could do pretty much all the matrix operations. Once you are comfortable with these, you could then delve into specific machine learning libraries such as "scikit-learn" and so on.
Not really. You could start off by installing "Anaconda Python" - this is a popular package for data analysis and machine learning. Next, you could experiment with the "Numpy" library through which you could do pretty much all the matrix operations. Once you are comfortable with these, you could then delve into specific machine learning libraries such as "scikit-learn" and so on.
You should learn only the ones what you need. Otherwise, it will not help you to know everything in python. Good luck
It is not necessary to learn every library of a particular language. First, you should find out what you are going to achieve through the problem in hand, formulate it and apply specific libraries.
It is not humanly possible that way and you may want to take a top down approach. So you start with a problem in hand may be real estate pricing and now you try linear regression, may be then try logistic regression and other non linear methods. The subject area is so vast, you can not possibly complete learning and start applying. However the catch is if you do not apply, you will hardly learn. HTH
Dear Rahul,
I think what would really get you going in the field of machine learning in the shortest possible time is concurrent learning of machine learning basics (foundations/theory) and hands-on experiments. As stated by others, start with very basics such as preparing datasets, loading datasets, data normalization, etc; this you can achieve using Python libraries such as Numpy, Scipy, etc. Then begin to try out machine learning models with Scikit-learn. And of course, don't need to have mastered everything on Numpy and Scipy before proceeding to Scikit-learn. Your knowledge based on Python libraries for machine learning would expand over time with more hands-on experiments.
Hope this helps.
Alot of machine learning can be done and understood using any programing language without lots of libraries. As many folks said python, bumpy, are all your need to do most anything. Other libraries will really help but it's better to start with rolling your own algorithms just in Python at first to learn then leverage SciPy, PsyKit learn, etc when you know what's up
Thanks everyone for your valuable answers. I'm using anaconda package and Jupyter notebook for execution of codes. I'm going to use this ML for pedestrians risk taking behavior identification and prediction.
You should also consider checking out online courses like Coursera's Machine Learning courses. Very good overview of ML techniques and algorithms and you can take all the classes for free.
Not all, but below are some important libraries for Machine Learning:
- Theano
- Scikit-Learn
- TensorFlow
- Keras
- PyTorch
Read here more about these Python libraries - how and where to use them - https://data-flair.training/blogs/python-library/
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