Learn Data Science using Python

                                          Announcing The Release of My New book Another post starts with you beautiful people! Today I am very happy and going to share with you all about my happiness. It was almost 6 months of work, dedication and motivation to accumulate my knowledge and experience in a book. When I have started to learn Data Science with Python, there were rare resources who can teach me all the basics of Data Science so that I can get my first job in this field. Data Science is a vast field, no one can be a master in all fields but one can master in basics and that is asked to everyone whether you are an aspiring data scientist or an experienced one. Keeping this lesson in my mind I determined to make my own notes based on my previous experience and various resources available in internet so that whenever I need to brush up my mind I will use my notes; I will not use Google search or use internet for this. Believe me my friends! If you decide

Another post starts with you beautiful people! It was quite overwhelming to see almost 10,000 views on my last post where we learned to build a simple  Keras + Deep Learning Rest API . That post is quite important if you want to deploy your model as a Rest API in development environment so that you can easily demonstrate it to your tester team or business team. One drawback with this approach is that it was intended for single threaded use only with no concurrent requests but in production environment your model will be used by many users at the same time so it is important to efficiently batch process incoming inference requests. Many thanks to  Adrian   who has shared his knowledge to use  Redis   to solve this problem and make our simple keras Rest Api scalable. If you are macos/linux user then you can follow  Adrian's post   but if you are a Windows user like me then you must follow this post because officially Redis does not support Windows. Here we have chosen Redis al