Machine learning life cycle

1. Machine Learning Life
Cycle
- Dr. M. Balamurugan

2. Machine learning is the subfield of Artificial Intelligence (AI).
Machine learning is the science of teaching machines to learn by themselves.
Types of ML
● Supervised
● Unsupervised
● Reinforcement

3. There are seven steps involves in the ML life cycle:
● Data collection
● Data preparation
● Data processing
● Data analysis
● Model training
● Test the model
● Deployment

4. 1. Data Collection
Data can be collected from different sources such as files, databases, the
internet, or mobile devices. It is one of the most important steps of the life cycle.
The quantity and quality of the data-collected will determine the efficiency of the
output.
Primary Data - Collected by researcher from first-hand source.
Secondary Data - Collected by someone else and already been passed
through the statistical process.

5. This step includes several points:
● Identify different data sources
● collect data
● Combine data obtained from different sources
● By performing the above task, we get a coherent set of data, also called a
data set.

6. 2. Data Preparation
It refines the data by understanding whether this is the required data or not,
and whether it is appropriate or not, and it comes directly after the data collection
process.
This process can be put into two steps:
Explore the Data - It is used to understand the nature of the data we have to
work with. We need to understand the characteristics, format, and quality of data.
Data Preprocess - to pre-process the data for analysis.

7. 3. Data Wrangling
It is the process of converting raw data into clean data.
The data we have collected does not always have to be useful because some data
may not be useful. In real-world applications, the collected data may encounter
various issues, including:
● missing values
● Duplicate data
● invalid data
● Noise
Therefore, we use different filtering techniques to clean the data.

8. 4. Data Analysis
The cleaned and prepared data is now transferred to the analysis step. This step
includes:
● Data selection
● building models
● See the result

9. • The objective of this step is to build a machine learning model to analyze the
data using different analytical techniques and review the result.
• It starts with defining the type of problems, where we choose machine learning
techniques such as classification, regression, cluster analysis, correlation, etc.,
then build the model using the prepared data, and evaluate the model.
• Hence, in this step, we take the data and use machine learning algorithms to build
the model.

10. 5. Train the Model
Here we come to the step that we are in now, which is training the model until we get
the goal that we worked for. where we using data sets to train the model using
various machine learning algorithms.
Model training is required so that it can understand different patterns, rules, and
features.

12. 6. Test the Model
Once our machine learning model has been trained we have to test the model. In this
step, we check the accuracy of our model by providing it with a test dataset.
Test the model determines the percentage of accuracy of the model depending on the
requirements of the project or problem.

13. 7. Deployment
Hence, this step is the last step in the machine learning lifecycle in deployment, where
we deploy the model in the real-world system.
If the above-prepared form produces an accurate result according to our requirements
at an acceptable speed, we publish the form in the real system.

14. Thank You