The Life Cycle of Machine Learning
Machine Learning enables the computer to learn without being explicitly programmed. Let’s know about the working of a machine learning model. This blog will give you a brief explanation of “The Life Cycle of Machine Learning Model”. The following are the major stages of machine learning.
1. Problem Definition
2. Data Collection
3. Data Pre-Processing
4. Model Training
5. Model Testing
6. Model Deployment
Problem Definition:
In the context of machine learning, Problem definition is the statement that determines the aim of our model. It should be more specific, clear, and well-defined. For a successful machine learning model, it should be very essential as it guides in the selection of algorithms, data pre-processing techniques, and evaluation metrics.
Data Collection:
Data is very necessary while working on a machine learning project. It refers to the process of acquiring and organizing the data to train, test, and validate the machine learning model.
The data we collected should be relevant to the problem definition. But what type of data do we need to collect?
Generally, We are implementing the machine learning model with a dataset that is in the format of CSV i.e. Comma-Separated Values. We can download it from the websites like Kaggle, and UCI Machine Learning Repository, or else we can create our dataset.
Data Pre-processing:
It is the process of preparing and cleaning the data before it is used to train a model. We cannot feed the data directly to the model as it contains uncleaned data.
To use the data in our machine learning model, it should be clean. So, we need to pre-process the data to transform it into a suitable form. Data Pre-processing involves several steps like
Data cleaning: This step involves the removal of any irrelevant and missing data which includes duplicate data, noisy data, and inconsistent data.
Data Transformation: This step involves the conversion of data into a suitable format which includes techniques like encoding, normalization, and scaling the data
Data Augmentation: This step involves creating more diverse data by applying random transformations to existing data. It includes rotating images or flipping images horizontally.
Feature Engineering: This step involves creating new features by combining or transforming existing features in a dataset. The main aim of feature engineering is to improve the efficiency of the machine learning model by providing it with more informative and relevant input data.\
Model Training:
Model Training refers to the process of learning from the dataset to create a model which predicts new data. The model understands various patterns, rules, and features while training.
The main aim of model training is to find the best set of parameters for a given model that will minimize the error between the predictions made by the model and the Actual values in the training dataset. This process involves several steps as
Selecting the model: The goal is to find the accurate model that best fits the data and can make accurate predictions on new, unseen data.
Data Splitting: In this step, we need to spilt the data into training and testing sets in which the training set used for training and testing set is used to evaluate the performance and then model training Begins.
Model Testing:
Once the training has been done then we need to test the model. Basically, It is the process of evaluating the performance of a trained model and it is also used to identify any underfitting or overfitting in the training data. The concept of testing is to find the model efficiency which is the error between predicted and actual values.
We use performance metrics like accuracy, precision, recall, F1-score, etc.
Model Deployment:
This is the last step of machine learning cycle, where we deploy our machine learning model in real-world systems. It can be deployed on the cloud and local server, web browser and then you can use plugins, and APIs to access the predictions. For deployment, we can use Heroku, Amazon AWS, Google cloud platform, etc.
