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7 Steps to Successfully Implement Machine Learning in your Company

The pace at which technology is evolving is mind boggling. With technology becoming an integral part of our personal and professional lives, businesses must keep pace with this tech evolution to survive and thrive in today’s competitive business world.  One of the hottest new trends in technology is of machine learning.

Yes, it has its downsides too, but the pros outweigh the cons by a significant margin. Machine learning algorithms are susceptible to errors and requires a lot of time and resources. More importantly, you will have to follow some machine learning ethics when using these algorithms to get the best results. All these minor gripes pale in comparison to advantages machine learning offers.

That is why many businesses are already using AI and machine learning to make smart decisions and automate their processes. This gives them a competitive advantage over their competitors and keep their employees happy as some of the mundane daily tasks are automated. It let you act based on user behavior. Want to implement machine learning in your organization? Here is how to do it.

In this article, your will learn about the step by step process of implementing machine learning in your organization.

1.    Collect the Data

Machine learning algorithms require large amount of data which are called data sets to work. The quality and quantity of data will impact the effectiveness of machine learning algorithm. The first thing you need to do is to collect all the data from different sources. The prediction you make using all the data will be only be accurate if the data quality and quantity is high.

2.    Prepare the Data

Once you have gathered all your data, the next step is to prepare it. During data preparation stage, you should normalize data and remove errors, bias and duplicated data from data set. Next, load all your data into a place and prepare it so it can be used for machine learning training. To check whether you have collected the right data or not, you can use visualization. It will tell whether your data is complete or some part of it is missing.

3.    Select a Model

During the next stage, you will have to choose the right machine learning model. Each model serves a different purpose therefore, it is important to choose a model based on business needs. Here are some of the most important things you should consider when choosing a machine learning model:

  • Accuracy
  • Preparation
  • Scalability
  • Complexity

Remember the more complex a model is, the better results it will deliver. Some of the commonly used machine models are as follows:

  • Linear Regression
  • Principal Component Analysis
  • K-means
  • Decision Trees
  • Logistic Regression

4.    Train Your Model

Training your model is at the heart of machine learning. The main objective of training your model is to iteratively improve the prediction of model. Each cycle updates the biases and weight and is considered a single training step. There are two ways to train your machine learning model.

  • Supervised
  • Unsupervised

In supervised machine learning, the model is created using labelled data while in unsupervised machine learning, model uses non labelled data to draw inferences from. Once you have trained your machine learning model, then it is ready to identify patterns and trends from massive data sets.

5.    Evaluate the Model

After training your machine learning model, it is now time to test it. For this, you might have to define and set certain parameters which acts as baseline. The more training cycles you run the more accurate will be your result. Always keep that in mind.

Once you have set the bar with parameters, you can easily evaluate how your machine learning model is performing.  Is it meeting your needs or is it underperforming? It is it the latter, then you should keep tweaking your machine learning model until it starts performing at a level you want it to. Evaluation is an on-going experimental process.

6.    Tune the Parameters

When you are done with evaluation process, it is time to check the parameters. Have you set the right parameters for your machine learning model? If not, you can also fine tune certain parameters or replace old parameters with newer ones to see if that works. Some important parameters that you should focus on are:

  • Frequency
  • Learning rate

These two factors directly impact how long your training process will take and how accurate your machine learning model will be. Digital marketing agencies can benefit from this and choose the right KPIs and improve their campaigns. Once you are fully sure that you have chosen the right parameters, or your parameters are fine tuned to deliver the desired outcome.

7.    Prediction

Machine learning is all about using data to answer questions. It is about analyzing data and identifying patterns and trends. More importantly, machine learning help you make accurate predication based on data. All the processes lead up to this last stage. You can make all types of prediction using machine learning such as predictive analytics, image recognition and sematic analysis. The accuracy of prediction made by your machine learning model heavily depends on outcome of all the previous stages. This means that if you want to get the best output, you will have to run all the phases efficiently.

Conclusion

Machine learning has its advantages, but you can only reap rich rewards from it if you understand how it works. Without developing an in depth understanding of machine learning models, processes and phases, you can never be able to get the best out of it. Invest your time and efforts in all those stages and it will pay off in long run. After reading this article, you might have understood which steps are involved in machine learning so you can easily implement it in your organization.

Do you use machine learning? If you do, how do you implement it in your organization? Feel free to share it with us in the comments section below.

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