How does Machine Learning work?

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Ever climbed a mountain?

It takes 6 stages to climb the Data Summit and leverage the full potential of Data Science.

Machine Learning(ML), an intriguing field of Artificial Intelligence, is currently gaining the attention it deserves simply because it is all around us in today’s environment, and with the emergence of big data, understanding how it works has become a critical tool for solving problems in a range of domains.

In computational finance, ML can be used for credit scoring and algorithmic trading, while in image processing and computer vision, it can be used for face identification, motion detection, and object detection. In  the early stages of machine learning (ML), experiments mainly involved computers finding and learning from patterns in data. It’s not surprising, then that today, after building upon those foundational experiments, machine learning has become even more complex. While machine learning algorithms have long existed, the ability to apply sophisticated algorithms to large data applications more quickly and effectively is a relatively new phenomenon.

How to get started with Machine Learning?

Let’s have a look at some of the key terms used in Machine Learning to get you started:

  • Model: A machine learning model, often known as a hypothesis, is a mathematical representation of a real-world process. It is created using a machine learning algorithm and training data.
  • Feature: A feature is a quantifiable trait or parameter of a data set
  • Feature Vector: It’s a collection of numeric characteristics. We use it as a training and prediction input for the machine learning model.
  • Training Data: An algorithm takes a set of data known as “training data” as input. The learning algorithm searches for patterns in the data and then trains the model to deliver predictable results (target). The training process produces the machine learning model.
  • Prediction: Following the creation of the machine learning model, reference input data can be used to predict the outcome.
  • Target: This is the value that the machine learning model must predict.
  • Overfitting: When a machine learning model is trained with a large amount of data, it tends to learn from the noise and incorrect data entries. In this case, the model fails to accurately characterize the data.
  • Underfitting: Is a data science scenario in which a trained model fails to accurately represent the link between input and output variables, resulting in a high error rate on both the training set and unobserved data.
How machine learning works

Machine Learning is, without a doubt, one of the most fascinating branches of AI. It completes the work of learning from data by providing the machine with specific inputs. It is critical to comprehend how Machine Learning works and, as a result, how it can be applied in the future. Inputing training data into the chosen algorithm is the first step in the Machine Learning process. To construct the final Machine Learning algorithm, training data must be known or unknown. It’s worth noting that the sort of training data used has an effect on the algorithm. New input data is given into the Machine Learning algorithm to see if it is working properly. After that, the prediction and results are double-checked. If the prediction does not turn out as expected, the algorithm is retrained several times until the intended result is obtained. This allows the Machine Learning algorithm to train on its own and come up with the best possible solution. The entire ML process is discusssed below:

The machine learning process is divided into seven steps which includes the following:

  1. Gathering data
  2. Preparing the data
  3. Choosing a model
  4. Model training and data pipeline
  5. Model evaluation
  6. Hyperparameter Tuning
  7. Prediction

Credit: Great Learning

Gathering data: The describes the process of extracting raw datasets for the Machine Learning task. This data  might originate from a variety of sources, including free online resources and paid crowdsourcing. This is undoubtedly the  most critical phase in the machine learning process If the data you obtained is bad or irrelevant, the model you train will be bad as well. Machine learning uses two techniques: supervised learning, which includes training a model on known input and output data to predict future outputs, and unsupervised learning. It involves identifying hidden patterns or intrinsic structures in input data.

Preparing the data: Once you have obtained the necessary information, you will  need to process it and ensure that it is in a format that can be used to train a machine learning model. This involves dealing with missing data, outliers, and so on.

Choosing a model: Data scientists have created a variety of models that can be utilized for a variety of purposes. You will choose which model architecture to utilize based on the dataset. This is one of the primary responsibilities of data engineers. Furthermore, rather than attempting to construct a completely new model architecture, the bulk of operations can be successfully done by modifying an existing design (or combination of model architecture).

Model training and data pipeline: The training of the model lies at the heart of the machine learning process. At this point, the majority of the “learning” is completed. You will need to design a data pipeline to train the model after you’ve settled on the model architecture. Providing a continual stream of batches data observation is required to efficiently train the model.

Model eveluation: You will need to validate the model’s performance on a held-out subset of the whole dataset after you have trained it for a period of time. This data must originate from the same underlying distribution as the training dataset, but it must be unique to the model. This places the model in a situation where it must deal with conditions that were not a part of its training.

Hyperparameter Tuning: We can go on to hyperparameter optimization if the evaluation goes well. The goal of this stage is to build on the positive results of the previous step’s review. You must be able to appropriately save model weights and perhaps put the model into production. This entails creating a system that allows new users to make predictions rapidly using your pre-trained model.

Prediction: Prediction is the final phase in the machine learning process. This is the point at which we consider the model to be ready for use in real-world scenarios. This is the culmination of all of our efforts, and it is here that the value of machine learning is apparent.


Types of Machine Learning

Machine Learning uses two main techniques: supervised learaning, which uses existing input and output data to train a model to predict future outputs and unsupervised learning which analyses input data to discover hidden patterns or intrinsic structures.

Supervised learning

In the presence of uncertainty, supervised machine learning creates a model that makes predictions based on evidence. A set of input variables (x) and an output variable (y) are used in the supervised learning model (y). The mapping function between the input and output variables is identified by an algorithm. The equation is y = f. (x). The learning is supervised or monitored in the sense that the output is already known and the algorithm is modified each time to improve the outcomes. The algorithm is tweaked until it achieves an acceptable degree of performance after being trained on the data set.

Examples of supervised machine learning models are regression – predicts continuous responses, such as temperature changes or power demand fluctuations. Electricity load forecasting and algorithmic trading are two typical applications. If you are working with a data range or the nature of your response are real numbers, use regression techniques.

Classification models classify input data into categories. Classification – predicts discrete responses, such as whether an email is real or spam, or whether a tumor is malignant or benign, for example. Medical imaging, speech recognition, and credit scoring are examples of common applications. It is always a good idea to employ classification if your data can be tagged, categorized, or separated into distinct groups.

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