Why it is taking more training time on one machine comparatively to similar another? I have a HP15-R203TX notebook with CPU i5-5200U 2.20GHzx4 / RAM 4Gb /2 Gb NVIDIA GeForce 820M. My friend has the same notebook. Another one has a dell notebook but same graphic card.

When i ran same jupyter notebook on same dataset to train the same number of layers, it takes 20 minutes on mine but 6-8 minutes on both of my friend's.

I tried resetting the whole environment, but didn't helped.
In machine learning, while your choice of inputs and hyper-parameters do matter, most of the time convergence ultimately comes down to how lucky you are. If you're lucky you might reach optimum in a very short period of time, whereas if you're unlucky you might get stuck in a local optimum with a huge error and get stuck in a very long loop. 

That's one reason; another is possibly that if you're using TensorFlow and you installed the version for non-GPU computers, and your friends installed the version for computers with GPUs.