machine learning with python basics we are living in the 'age of datathat is enriched with better computational power and more storage resourcesthis data or information is increasing day by daybut the real challenge is to make sense of all the data businesses organizations are trying to deal with it by building intelligent systems using the concepts and methodologies from data sciencedata mining and machine learning among themmachine learning is the most exciting field of computer science it would not be wrong if we call machine learning the application and science of algorithms that provides sense to the data what is machine learningmachine learning (mlis that field of computer science with the help of which computer systems can provide sense to data in much the same way as human beings do in simple wordsml is type of artificial intelligence that extract patterns out of raw data by using an algorithm or method the main focus of ml is to allow computer systems learn from experience without being explicitly programmed or human intervention need for machine learning human beingsat this momentare the most intelligent and advanced species on earth because they can thinkevaluate and solve complex problems on the other sideai is still in its initial stage and haven' surpassed human intelligence in many aspects then the question is that what is the need to make machine learnthe most suitable reason for doing this is"to make decisionsbased on datawith efficiency and scalelatelyorganizations are investing heavily in newer technologies like artificial intelligencemachine learning and deep learning to get the key information from data to perform several real-world tasks and solve problems we can call it data-driven decisions taken by machinesparticularly to automate the process these data-driven decisions can be usedinstead of using programing logicin the problems that cannot be programmed inherently the fact is that we can' do without human intelligencebut other aspect is that we all need to solve real-world problems with efficiency at huge scale that is why the need for machine learning arises why when to make machines learnwe have already discussed the need for machine learningbut another question arises that in what scenarios we must make the machine learnthere can be several circumstances where we need machines to take data-driven decisions with efficiency and at huge scale the followings are some of such circumstances where making machines learn would be more effectivelack of human expertise the very first scenario in which we want machine to learn and take data-driven decisionscan be the domain where there is lack of human expertise the examples can be navigations in unknown territories or spatial planets