The topic of machine learning is quite familiar in computer science community. Long back in early 2000, computer science enthusiasts were not payed much importance to this massive term. In-fact it was existing in computer science community in the form of mathematical formulas, equations and algorithms. During that time some even mistook the term of machine learning as some jargon related to machines, which deals with mechanical enthusiasts. It demanded a great somersault to explain the exact meaning of the term to a computer enthusiast that there are some methods to train your computing machine. The obvious thought comes to his mind that why should I train my computing machine. Hence the definition again changed which try to exploit the natural taste of human mind. It changed to the following, there are some methods to train your computing machine there by it can take the decisions by its own and enter into the world of predictions and conclusions by its own. This is interesting since at this point of time human mind got a pellet to climb up with its imaginations. The imaginations climbed great heights. Eminent scientists and researchers made this imaginations a reality. There came massive machines, techniques, software organizations, products, social media, analytical domain, and so forth. That urge is continuing in full dimensions, by producing mind stunning results, products every day. That is the power of machine learning. There are various ways to master machine learning techniques. The difficulty exists in selecting which resource should be selected or from where to begin. The decision to take which course or which book to follow is immaterial in this fast developing world. It may vary according to persons and their interests. Mostly the ML enthusiasts starts with linear regression which will gives us an intuition about prediction of values based on the observed values in the real world. Most of the explanations starts with the categories of machine learning algorithms which will gives a birds eye-view about the methods available in machine learning. Some of the concepts are worth to be known before stepping into specific algorithms in machine learning. Supervised learning, Unsupervised learning, Clustering, Classification, Prediction, Performance measures, Basic statistical formulas, Conditional Probability, Matrix operations and transformations, Dataset, Training and Test Data etc are very few of them. The next quest of enthusiasts is about the platform or programming language which helps in mastering machine learning. Python can be one of the best candidate for machine learning friendly programming language. It includes various package like NumPy and SciPy which will make our job easy. The only draw back of such package usage is that the enthusiasts will be habituated to use the function call with out knowing the internal wheels of algorithm. Hence it will be appreciable if the enthusiasts try to know the algorithm with small examples and then heading to use of such utility function in the package for their problem solving in machine learning. One of the good resources to start with machine learning approaches is listed below