Wrapping your head around something so complex as an ML model can appear daunting, let alone developing one from scratch. The road from idea to a working ML model can be a bumpy ride, especially if you are an absolute beginner.
However, using data to answer questions is not an overly complex task once you understand the basics. Since you are new to all things ML, we are going to introduce you to it slowly. Let’s start by defining ML and then work our way through all the stages you need to complete to develop an ML model.
What is ML?
Many people use Machine Learning and Artificial Intelligence interchangeably. Keep in mind that these two are different. ML is what enables AI. So what is machine learning? We already referred to ML as “using data to answer questions.” It is by far the most straightforward way to define ML.
But what enables us to use the data and get answers to our questions? The answer is the ML model.
What is an ML model?
You provide training inputs to an ML model, and it outputs the prediction. A training input is just a fancy way of calling data. It can be text, numerical or categorical data, or image data.
An ML model is matrix multiplication. The training inputs are features, and they become matrices. An ML model matrix also contains weights and bias matrices.
At the beginning of ML model training, weights and bias matrices are of random values. As you train the ML model, you will find the optimal values for bias and weights, and your ML model can deliver accurate predictions.
How do you develop an ML model then? It all starts with ideation.
From an Idea to Problem Definition
The problem definition is a crucial stage in building ML models. Before you start doing anything ML-related, you need to have a basic idea of what you want to achieve. What is the problem you want to solve? Is there a data set available to help you solve it?
Once you have a general idea of what you want your ML model to do, you can define the problem. In technical terms, you need to define your training inputs and outputs.
You need to define features and determine which are the most important ones. Then, you have to check whether you have access to the input data and how you will represent it as a matrix.
You’ll Need to Collect the Data!
Collecting data is not a big deal anymore. The Google Cloud Platform, for instance, stores interesting data sets on BigQuery, which you can use to develop and train ML models. However, if you need a specific set of data, you will need to collect it yourself.
The most common data collection harvesting technique is web scraping. But in some instances, you might need to use the data on the go as it runs through the pipeline. In that case, you will need to use online machine learning such as PandioML.
Determine How You’ll Measure Success
Your ML model has to know what is considered a success. This has everything to do with your problem definition. For example, let’s say you have a regression problem. In this case, you will have to use appropriate evaluation metrics, more precisely, mean squared error.
However, if your ML model has to solve a classification problem, you will have to use different evaluation metrics, including recall, accuracy, and precision.
Choose Appropriate Evaluation Protocol
The next question you will have to answer is how you will measure the progress towards making accurate predictions. To do it, you will need to choose a relevant evaluation protocol. You can choose from:
- Hold out validation set
- K-fold validation
- Iterated K-fold Validation with shuffling
Getting the Data Ready
For an ML model to be able to use the data, it has to be in pristine condition. What does it mean? You will have to deal with missing values by getting rid of the samples with missing values or adding values with the help of estimators.
If you are going to use categorical data, you will have to map ordinal features and encode nominal features via class labels. You will have to scale the features as ML works better when all the features are on one scale. You can use either normalization or standardization to do so.
Finally, you need to select the meaningful features and get rid of the redundant data to avoid the risk of making the ML model too complex. You need to identify the features that can explain one another and either delete them or merge them.
Using The Relevant Tools
Once you have defined the problem and got your data ready, you will need to use specific tools to develop an ML model. For instance, we’ve already mentioned PandioML, a Python library and CLI tool designed to help you build online and streaming ML models.
Generally speaking, getting proficient in the Python programming language can help you propel your career as a data scientist. Other ML frameworks include TensorFlow, XGBoost, Sklearn, and PyTorch. Data scientists commonly use Pandas (Python library), Scikit-learn, and Keras to do pre-processing and get the data in the correct format.
The final steps in producing a working ML model are training and evaluation. After successfully completing them, you can save your ML model and test locally or deploy it with an AI.
Hopefully, now you have a better understanding of how ML models are made. The workflow can be divided into two phases. One consists of defining a problem, finding the data, and making it ready for ML algorithms. The other phase consists of inputting the data into an ML algorithm, training it, and evaluating the predictions.
