The document outlines the seven key steps for developing a machine learning model, starting with identifying the business problem and ending with making predictions using the model. Key steps include understanding data requirements, preparing and cleansing data, selecting and training machine learning models, evaluating their performance, and making necessary adjustments before deploying the model. A continuous improvement approach is encouraged to achieve success in machine learning projects.

1. Session 1
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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.

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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.
Introduction
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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.
Steps for machine learning model
development
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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.
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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.
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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.
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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.
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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.
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7. Interference or Prediction
Now, it's time to utilize machine learning models in real-life scenarios
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Conclusion
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.
If you are a beginner and want to explore machine learning for beginners, then
you can check out our website Global Tech Council.

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