Machine Learning is the ability of computers to automatically learn and improve from experience without given a set of rules. Machine Learning uses the data for themselves to be more smarter and efficient. It is a subset of Artificial Intelligence (AI) which provides machines the ability to learn automatically & improve from experience without being explicitly programmed.
The main difference between Machine Learning and Traditional programming is that Traditional Programming uses a set of rules and data to give an output but Machine learning uses Answers and data to give a set of rules.
There are a number of machine learning process that we should consider to build a successful Machine learning model. They are:
Know the objective of the Problem.
You must be well known about your main concept behind building the model. You must know what should be your output along with the what features to be taken into account. The type of input data you will be using must be defined clearly. And the main part, What kind of problem are you solving i.e. Classification or Clustering.
Collecting data is the most important and difficult phase of the project. If you have your own data than skip this step. If not you must search the data from different platforms like Kaggle, UCI Machine Learning Repository.
Now the data you need to collect must be Processed. There might be unwanted columns of the data and most of the datasets are corrupted containing null values which must be filtered. You must also transform the data into desired format and must do necessary resizing.
You must be familiar with the datasets to know which module will works best for your data. If the datasets are new you must visualize the data in order ti understand the correlations between the variables. At this stage all useful insights are drawn. Here, where the main dimensional reduction and features scaling enters.
Building a Machine Learning Model
Now you need to split the datasets between train, test, and validation set. Then you must apply the necessary model suitable for your dateset that will give an appropriate output. If your datasets analysis is good you will be able to select which model to apply in the dataset.
Model Evaluation & Optimization
Now you have seen the output and accuracy of your module. You must see that the accuracy might be low or your data may perform differently in the test data. So, you need to do necessary tuning of the hyper-parameters like weight, learning rate, epochs and many more.
The outcome is predicted after performing parameter tuning and improving the accuracy of the model. This gives your final output and you can repeat above steps if desired accuracy is not achieved.
Building a Model Package for Production
To put your machine learning model in production, you’ll build a Package file. Simply, then import that package, and reuse by inputting the data and getting the answer. Congratulation you have made to the end.
Do check out the next blog Types of Machine Learning to make your learning effective.