Machine Learning is a field of Artificial Intelligence.
Through algorithms, statistical models, and a “known” dataset, Machine Learning trains a computer system to make predictions, find anomalies, and group similar data samples, to create predictive models that will be used with a set of “unknown” data.
With the help of machine learning algorithms, Netflix has trained its system to recognise your viewing habits and suggest movies you might enjoy.
What is the training process of a machine learning system?
How do we train machine learning models?
A machine learning system’s process comprises the following phrases of machine learning models:
- iterative preparation
- creation
- testingÂ
- implementationÂ
The process is sequential and iterative, so we can go back and forth, jumping from one step to another. The process requires adaptation, evaluation and finetuning.
We can outline the process in the following steps:
- The Question
- The Dataset: Gathering data, Data preparation, Data Exploration
- Evaluation
- The Deployment
Let’s suppose we want to work on a Data science project.
The project usually starts with a question.
- We must use historical or active data collection or combined data to answer that question.Â
- The next step is gathering datarelevant to the question we are working on.Â
The goal is to use data to prove or disprove a theory about what factors may impact the outcomes you want to understand.
Once we have the data, we must prepare thembefore interpreting it.
It is essentialto understand the deep of the problemand the dataset we must handle.
Then we can explore the informationcontained therein.
Data Preparation and Data Exploration phrases are significant factors in data science projects.
3. The next step of the process is creating modelsand evaluating performance.
Machine learning algorithms map the relationships between variables. If their relationships describe reality, we can use them to accurately predict the values of the target variables and answerthe question we started with.
Some algorithms perform better than others for certain kinds of problems.
For the best result, we need to try several algorithms and compare the outcome of those models to historical values, then pick the best performer for the project we are working on.
4. The final step of the process is model interpretationand deployment.
This phrase consists of the following:
- Using the model to simulate situations
- Understand the relationships in your data
You can make decisions using your new insights. As soon as we answer a question, we may have to ask a new one, and we may have to repeat the process.Or the relationships modelled change requires another iteration of the process with new data that reflects the new reality.