The 7 Key Steps To Build Your Machine Learning Model

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• 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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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.

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Steps for machine learning model development

• There are seven steps for the development of
  machine learning models. You cant ignore these
  key steps of machine learning development if you
  wish to be certified for machine learning
  certification.

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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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