The 7 Key Steps To Build Your Machine Learning Model

Several types of industries are executing projects based on artificial intelligence and machine learning for various applications. These applications include pattern recognition, conversational systems, predictive analytics, personalization systems, and autonomous systems. All these projects execute with the machine learning models. Building and developing a machine learning model is just like developing any product but at a high level. Machine learning training will provide you with deep knowledge and understanding of the ML domain. In this blog, we will discuss the steps to develop your machine learning model.

Introduction

A Machine learning model is a mathematical depiction of real-word. You have to provide data training to build machine learning models. Since data is a fundamental concept of machine learning. So, the data layer will be at the top of the development process. So let's dive in and understand the seven key steps of machine learning model development.

Steps for machine learning model development

There are seven steps for the development of machine learning models. You can’t ignore these key steps of machine learning development if you wish to be certified for machine learning certification. 

1. Identification of the business problem

The first step of any ML-based project is to understand the requirements of the business. You need to develop an understanding of the problem before attempting to decode it. Firstly, understand the requirements and objectives of a project. Then, reshape this knowledge into a business problem definition. After that formulate an opening plan for attaining the objectives of the project.

2. Identification of data

Once you identify the business problems the next phase is to identify data. Firstly, you have to understand how the model will work on real-world data. A machine learning model is generated by learning from train data and applying that understanding to new data. The data needs to be in good shape. This step involves data identification, initial requirements, collection, quality, and data insights. The main focus of this step is to manage the quality and quantity of data. 

3. Collect and prepare the data

The collection of data starts after the identification of data. This step involves the investigation of data. In this phase, you need to shape your business data so that it further can be utilized to train your business model. The quality of data will directly impact how your business model will operate. You can use web scraping to gather information from several sources. After gathering information the next step is to prepare and visualize the data. This step involves the pre-processing of data by eliminating, normalizing, error corrections, and removal of duplicacy. The preparation of data consists of data cleansing, augmentation, normalization, aggregation, transformation, and labeling of data. 

4. Choose and train your machine model

At this stage, you develop an understanding of your problem which you are trying to solve. Now your data is also in its usable shape. Now it's time to select and train your machine model. There are many models that you can select according to your business objectives. The step of selection of models includes algorithms of prediction, classification, clustering, deep learning, linear regression, and so forth. Now you will be required to train datasets to operate smoothly. The step of training your machine model involves several algorithms and techniques. The outcome machine model can be used for evaluation to check whether it meets the operational and business requirements. 

5. Evaluation

This step involves the evaluation of the machine models using a model metric approach, quality measurements, datasets, and matrix calculations. This phase is the quality assurance of a machine learning approach. 

6. Experiment and adjustment of the model

After evaluation, the adjustments of the machine model comes. Now, it's time to see how it works in the real world. This stage is also known as model operationalizing. It includes the deployment and monitoring of the ML model.

7. Interference or Prediction

Now, it's time to utilize machine learning models in real-life scenarios. 

Final Thoughts

Once you get a direction and blueprint of your ML model then you can test the prototype of your solution. You should continuously look for advancements and improvements to attain success in the machine learning development model.