A roadmap must be followed to produce a well functioning Machine Learning model. The strength of the algorithm determines the results it will gauge. Machine learning can be classified into many types such as supervised learning, unsupervised learning, reinforced learning etc. However almost all the machine learning techniques follow the same basic ideology discussed here.
Python has grown to be the most common programming language used for developing a machine learning model. The reason for its popularity is its dynamic nature of coding. Pre-processing of the data-set is the foremost step in devising a model after which the others follow. The steps discussed below outlines the machine learning process in a gradual manner.
Step 1- Preprocessing of Data and installation of packages
The basic ideology of this step is to get the data into a shape that is palatable with the next stages of machine learning. The pip command is used to install any package (available online) required in your program. The general syntax of this command is:
>pip install PkgName
The available dataset is divided into 3 crucial segments i.e. Training Data, Validation Data and Testing Data. Sometimes the data might be highly correlated, for which dimensionality reduction must be done prior to pre processing. Dimensionality reduction is also useful for the algorithms to run faster due to low storage space occupancy.
Data cleaning and exploration is also done in this step. The data obtained in this step must be free from any noise or abnormalities. Libraries such as NumPy and Pandas can be used for data preprocessing. A data frame is a 2 dimensional data structure with two axes that can be used to store data into an aligned format. The recommended division of a dataset is shown below:
Step 2- Training the data for Machine Learning
For training a model the training data is used on which the predictions will be carried out by inputting some data values. Training is similar to a child learning to ride a bicycle. With regular practice (training here) the child begins to learn and finally becomes skilled after a particular amount of effort. Same is the case with a machine learning model. A linear model can be used for applications such as classification problems. Training involves the initialization of random variables for the sake of developing models.
Step 3-Selection of Predictive Model
A model can be selected from a vast set of available options. Selection of variables and relationships must be formulated beforehand and the criteria must be set to use the most optimal predictive model.
The predictive model is also crucially dependent on the results to be achieved by applying the model to the data-set. Accuracy is the most common metric in a model. The final review and analysis must be in accordance with the thresholds chosen for the model. This can be performed by evaluating the performance of the model after its successful selection.
Step 4- Evaluate model and prediction of data
This is the final step in which the predictions are made on the new input data that is unknown to the existing model. In this step the testing data is fed into the model to return the predicted outcomes such as percentage, classification results etc. based on its implementation. In this step, the machine learning is realized.
Some of the most commonly used datasets are MNIST hand written dataset of digits, IRIS dataset, CIFAR-10 Dataset etc. on which the machine learning models are popularly applied for developing various interesting applications. Machine Learning is a vast field in computer science that requires plenty of statistical as well as mathematical knowledge to fully understand the machine leaning process. Many dynamic techniques such as Transfer Learning provide faster and efficient machine learning ways of machine learning development.