Introduction of machine learning, Need and Important Terms of machine learning.

Machine Learning is a tool for turning information into knowledge. Machine learning techniques are used to automatically find the valuable underlying patterns within complex data that we would otherwise struggle to discover. The hidden patterns and knowledge about a problem can be used to predict future events and perform all kinds of complex decision making.

Tom Mitchell gave a "well-posed" mathematical and relational definition that " A computer program is said to learn from experience E with respect to some task T and some performance measure P, if  its performance on T, as measured by P, improves with experience E ".

For example:- A checkers learning problem:

                       Task(T): Playing checkers.

                        Performance measures (P): Performance of games won.

                        Training Experience (E): Playing practice games against itself.

Need for Machine Learning:-

• Ever since the technical revolution, we've been generating an immeasurable amount of data.
• With the availability of so much data, it is finally possible to build predictive models that can study and complex data to find useful insights and deliver more accurate results.
• Top tier companies such as Netflix and Amazon build such machine learning models by using tons of data in order to identify profitable opportunities and avoid unwanted risks.

Important Terms of Machine Learning:-

• Algorithm:- Machine Learning algorithm is a set of rules and statistical techniques used to learn patterns from data and draw significant information from it. it is the logic behind a machine learning model. An example of a machine learning algorithm is the linear regression algorithm.
• Model:- A model  is the main component of machine learning. A model is trained by using a machine learning algorithm. An algorithm maps all the decision that a model is supposed to take based on the given input, in order to get the correct output.
• Predictor Variable:- It is a feature(s) of the data that can be used to predict the output.
• Response Variable:- It is the feature or the output that needs to be predicted by using the predictor variable(s).
• Training Data:- The machine learning model is build using the training data. the training data helps the model to identity key trends and patterns essential to predict the output.
• Testing Data:- After the model is trained, it must be tested to evaluate how accurately it can predict an outcome. this is done by the testing data set.