Here are some of the scenarios in which machine learning findsapplications in real world:
Ecommerce: Understanding the customer churn, deploying targetedadvertising, remarketing.
Search engine: Ranking pages depending on the personal preferences of thesearcher
Finance: Evaluating investment opportunities & risks, detecting fraudulenttransactions
Medicare: Designing drugs depending on the patient’s history and needs
Robotics: Machine learning for handling situations that are out of theordinary
Social media: Understanding relationships and recommending connectionsExtraction of information: framing questions for getting answers fromdatabases over the web.

Domain knowledge:
This is the first step wherein we need to understand how to extract the variousfeatures from the data and learn more about the data that we are dealing with.It has got more to do with the type of domain that we are dealing with andfamiliarizing the system to learn more about it.
Feature Selection:
This step has got more to do with the feature that we are selecting from the setof features that we have. Sometimes it happens that there are a lot of featuresand we have to make an intelligent decision regarding the type of feature thatwe want to select to go ahead with our machine learning endeavor.
Algorithm:
This is a vital step since the algorithms that we choose will have a very majorimpact on the entire process of machine learning. You can choose between thelinear and nonlinear algorithm. Some of the algorithms used are SupportVector Machines, Decision Trees, Naïve Bayes, K-Means Clustering, etc.
Training:
This is the most important part of the machine learning technique and this iswhere it differs from the traditional programming. The training is done basedon the data that we have and providing more real world experiences. Witheach consequent training step the machine gets better and smarter and able totake improved decisions.
Evaluation:
In this step we actually evaluate the decisions taken by the machine in orderto decide whether it is up to the mark or not. There are various metrics that areinvolved in this process and we have to closed deploy each of these to decideon the efficacy of the whole machine learning endeavor.
Optimization:
This process involves improving the performance of the machine learningprocess using various optimization techniques. Optimization of machinelearning is one of the most vital components wherein the performance of thealgorithm is vastly improved. The best part of optimization techniques is thatmachine learning is not just a consumer of optimization techniques but it alsoprovides new ideas for optimization too.
Testing:
Here various tests are carried out and some these are unseen set of test cases.The data is partitioned into test and training set. There are various testingtechniques like cross-validation in order to deal with multiple situations.

It is a set of continuous variable spread across a normal curve or in the shapeof a bell curve. It can be considered as a continuous probability distributionand is useful in statistics. It is the most common distribution curve and itbecomes very useful to analyze the variables and their relationships when wehave the normal distribution curve.
The normal distribution curve is symmetrical. The non-normal distributionapproaches the normal distribution as the size of the samples increases. It isalso very easy to deploy the Central Limit Theorem. This method helps tomake sense of data that is random by creating an order and interpreting theresults using a bell-shaped graph.

Determining and analyzing the correlation and direction of the dataDeploying the estimation of the modelEnsuring the usefulness and validity of the modelIt is extensively used in scenarios where the cause effect model comes intoplay. For example you want to know the effect of a certain action in order todetermine the various outcomes and extent of effect the cause has indetermining the final outcome.