Which is the correct order of first five steps of machine learning?

These 5 steps of machine learning can be applied to solve other problems as well:

• Data collection and preparation.
• Choosing a model.
• Training.
• Evaluation and Parameter Tuning.
• Prediction.

What is the correct order in machine learning?

The first step is correct, you need to gather data. Then, you perform exploratory data analysis. This may include but not limited to data ingestion, cleaning, transformation etc. And you don’t always delete duplicates.

What are the 5 stages of AI project cycle in correct order?

The five stages of the project life cycle

• Initiating. This process helps in the visualisation of what is to be accomplished.
• Planning. This is a crucial process in project management.
• Executing.
• Monitoring and control.
• Closing.

Which of the following are key steps for machine learning project?

The Machine Learning Project Checklist

• Frame the problem. This first step is where the objective is defined.
• Get the data.
• Explore the data.
• Prepare the data.
• Model the data.
• Fine-tune the models.
• Present the solution.
• Launch the ML system.

What are steps in AI project cycle?

Generally, every AI or data project lifecycle encompasses three main stages: project scoping, design or build phase, and deployment in production.

What are the steps of the AI lifecycle?

How to solve machine learning problems?

The first step to solving any machine learni n g problem is to gather relevant data. It could be from different sources and in different formats like plain text, categorical or numerical. Data Gathering is important as the outcome of this step directly affects the nature of our problem.

How to minimize the training data set error in machine learning?

Gather the data set: This is one of the most important step where the objective is to as much large volume of data set as possible. Given that features have been selected appropriately, large data set helps to minimize the training data set error and also, enable cross-validation and training data set error to converge to the minimum value.

How to choose the right machine learning algorithm for your project?

One could adopt the 60-20-20\% split for training, cross-validation and test data set. Choose the most appropriate algorithm. There are guidelines based on which one could select a particular machine learning algorithm based on the problem at hand.