2. Generalized AIML Life cycle
2
1. Problem Identification
2. Gathering Data
3. Data Pre-processing
4. Exploratory Data Analysis
5. Model Selection
6. Train Model
7. Hyperparameter Tunning
8. Test Model
9. Deployment
3. AIML Life cycle
1. Problem Identification
Machine learning project typically begins with the problem
definition.
3
4. AIML Life cycle
4
2. Gathering Data:
This step includes the below tasks:
• Identify various data sources
• Collect data
• Integrate the data obtained from different sources
5. ML Life cycle
5
3. Data Pre-processing
Data Pre-processing is a technique that is used to convert the raw data
into a clean data set. In other words, whenever the data is gathered
from different sources it is collected in raw format which is not feasible
for the analysis.
In real-world applications, collected data may have various issues,
including:
• Missing Values
• Duplicate data
• Invalid data
• Noise
6. AIML Life cycle
6
4. Exploratory Data Analysis:
The aim of this step is to analyze the data.
5. Model Selection
Here we select the machine learning techniques such
as Classification, Regression and Cluster analysis, etc. then build
the model using prepared data, and evaluate the model.
7. AIML Life cycle
7
6. Train Model:
In this step we train our model to improve its performance for
better outcome of the problem.
We use datasets to train the model using various machine learning
algorithms.
Training a model is required so that it can understand the various
patterns, rules, and, features.
9. AIML Life cycle
9
8. Test Model:
Once our machine learning model has been trained on a given
dataset, then we test the model.
In this step, we check for the accuracy of our model by providing a
test dataset to it.
Testing the model determines the percentage accuracy of the
model as per the requirement of project or problem.
10. AIML Life cycle
10
9. Deployment:
The last step of machine learning life cycle is deployment, where we
deploy the model in the real-world system.
