six-major-steps-in-the-machine-learning-process

Introduction

Machine Learning is a subset of artificial intelligence. It focuses mainly on the designing of systems, thereby allowing them to learn and make predictions based on some experience which is data in the case of machines.

In simpler words, machine learning is a subset of AI techniques that use statistical methods to enable machines to improve with experience.

 

Steps in the Machine Learning Process

Let’s discuss six major steps in the machine learning process:

Step 1: Data Collection

The first step in the machine learning process is data collection. During the collection of data, we need to assure that data we collected is complete and precise.

For supervised learning, this is the labeled historical data that we intend to use to train and evaluate our model.

For unsupervised learning, this is the unlabeled data with unknown patterns that we intend to discover.

For reinforcement learning, this is the data that helps our agent learn which actions yield the most reward

 

Step 2: Data Exploration

Data exploration is a process of describing, visualizing, and analyzing data in order to better understand it.

With data exploration, we can answer questions such as, how many rows and columns are in the data? What type of values are stored in the columns of the data? Are there missing, inconsistent, or duplicate values in the data? And are there outliers in the data?

 

Step 3: Data Preparation

The third step in the machine learning process is data preparation. Data preparation is the process of making sure that our data is suitable for the machine learning approach that we intend to use.

It involves resolving data quality issues, such as missing data, noisy data, outlier data, and class imbalance. Data preparation also involves modifying or transforming the structure of our data in order to make it easier to work with. This includes normalizing the data and reducing the number of rows and columns in the data.

Successful data science relies on good data. The data doesn’t have to be perfect, but it should be good. The saying garbage in, garbage out is especially important when it comes to machine learning. Because of how important good data is, it is not unusual to spend up to 80% of our time collecting, exploring, and preparing data.

 

Step 4: Modeling

Modeling is the process of choosing and applying the right machine learning approach that works well with the data we have and solves a problem at hand. It is the most well-known stage in the machine learning process.

In order to apply the right type of model, we must be clear about our objective. Knowing what type of machine learning we intend to do and what machine learning approach is capable or incapable of will go a long way in helping us be successful in this stage.

 

Step 5: Evaluation

The fifth step in the machine learning process is evaluation. As the name suggests, our objective in this stage is to assess how well the machine learning approach we chose worked. There are several ways to do this.

In supervised learning, where our goal is to predict a label or value, we evaluate a model by measuring how well it does in predicting labels for previously unseen data.

In unsupervised learning, we usually take a more subjective approach. A good unsupervised learning model is one that provides us with results that make sense to us.

Depending on how well a model performs, we may need to build it again with slightly different data or with different settings. The idea here is to make a change that has a meaningful positive impact on the performance of our model. This is usually an iterative process.

 

Step 6: Actionable Insight

When we feel confident that the model we prepared is performing well on the new data regardless of the data we provided initially, we move on to the last step of the machine learning process, actionable insight. This means identifying a potential course of action based on the result of the machine learning model.

For supervised learning and reinforcement learning, this is the stage where we decide whether or not to deploy our model to production.

In unsupervised learning, this is the stage where we decide what to do with the patterns identified by our model.

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