Machine Learning

6 Key Steps In Machine Learning Project

A fantastic new field of science called machine learning is gradually taking over daily life. Machine learning is used in everything, from targeted advertising to even identifying cancer cells. How is machine learning carried out? It is a natural question in light of the high-level tasks simple code blocks can carry out.

What Is Machine Learning

Making systems capable of self-learning and improvement through carefully designed programming is known as machine learning. 

The ultimate goal of machine learning is to design algorithms that automatically help a system gather data and use that data to learn more. Systems are anticipated to examine patterns in the data gathered and use them to autonomously make important decisions.

In general, machine learning aims to give systems a brain, human-like intelligence, and the ability to think and behave like people. In the real world, there are existing machine learning models capable of tasks like :

  • Separating spam from actual emails, as seen in Gmail
  • Correcting grammar and spelling mistakes, as seen in autocorrect 

Thanks to machine learning, the world has also seen design systems capable of exhibiting uncanny human-like thinking, which performs tasks like: 

  • Object and image recognition
  • Detecting fake news
  • Understanding written or spoken words
  • Bots on websites that interact with humans, like humans
  • Self-driven cars                     

Steps Of Machine Learning

Data Collection

Preparing customer data for meaningful Due to the enormous variety of disparate data sources and data silos that exist in organizations, ML projects can be a difficult task. Selecting data that is likely to be predictive of the target—the outcome you hope the model will predict based on other input data—is crucial for creating an accurate model.

Machine Learning

Data Normalization

Cleaning and normalizing dirty data is the next stage in the ML process, which is where analysts and data scientists typically spend the majority of their time on analysis projects. Data scientists frequently have to decide what to do with missing data, incomplete data, and outliers, which often requires them to make decisions on data they may not fully understand.

The customer, the right unit of analysis, may not be easily correlated with this data. Siloed data from various sources cannot be trusted, for instance, to determine whether a specific customer will churn.

All of the information from those sources will be prepared and combined by a data scientist into a form that ML models can understand. Before any ML even happens, this could turn out to be a drawn-out process that demands a lot of work.

Data Modeling

Modeling the data that will be used for prediction is the next stage of an ML project. Part of modeling data for a prediction about customers is to combine disparate data sets to paint a proper picture of a single customer. This includes combining and gathering disparate data silos like web, mobile app, and offline data.


The machine that you created will need to be compared to your evaluation data set, which contains inputs that the trained model is unaware of, in order to assess the accuracy of your model.

That model won’t be useful if the accuracy is less than or equal to 50% because making decisions using that model would be equivalent to flipping a coin. You can be confident in the model’s predictions if you get a 90% or higher success rate.

Parameter Tuning

You may have overfitting or underfitting issues if, during the evaluation, you did not obtain good predictions and your precision was not at the minimum desired level. In this case, you must go back to the training phase before changing any model parameters.

You can increase the number of epochs—or iterations—that you perform on your training data. The “learning rate” parameter, which is typically a multiplier of the gradient to gradually bring it closer to the global or local minimum to minimize the cost of the function, is another crucial variable.

You should 0.0 increase your values.1 units from 0.001 is not the same as this can significantly affect the model execution time. The maximum error permitted for your model can also be stated. The training process for your machine can take just a few minutes or it can take hours or even days.

These parameters are frequently referred to as hyperparameters. As you experiment, your “tuning” will become more precise. It is still more of an art than a science. There are typically a lot of variables to change, and their combination can activate all of your options. The parameters to be changed for each algorithm are different.

To name a few more, you must specify in the architecture of Artificial Neural Networks (ANNs) how many hidden layers there will be and gradually test with more or fewer and with how many neurons per layer. To yield good results, this work will require a lot of patience and effort.

Deploying Models To Production

All work to this point culminates in the final step of deploying a model to production where the ability to predict outcomes in the real world is tested. By this time, models ought to have reached a level of accuracy that justifies their use in production.

To determine what level of risk is acceptable for inaccuracy, it’s crucial to interpret model performance with stakeholders. It’s possible that some customer behaviors won’t be predictable enough for a model to ever become accurate enough to be used in production.


At the end of the day, machine learning won’t take the place of a digital marketing strategy; rather, it will support and enhance it. Successful brands will put their customer at the center of what they do and machine learning is one tool (among many) to optimize decision-making as part of that larger initiative.

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