Machine learning and robotics have witnessed a revolution in the last decade. However, none can deny that no matter how improved machine learning models are, they are only as good as the data they get trained on. So, even if you have the most advanced machine-learning algorithms, they will most likely fail if it is not trained with quality data. The requirement for accurate, relevant and complete data starts during the early days of the training process. It will learn to pick up the features and predict outcomes by feeding the algorithm with high-quality training data. Hence, training data can be considered the most important aspect of artificial intelligence and machine learning. Read along to learn about training data and how it is used in machine learning and take programming help.
Before we proceed to how we can use training data in AI integration, let’s know what training data is.
What is Training Data?
Training data is the preliminary dataset that a user uses to train artificial intelligence or machine learning algorithms. This data set is designed to fit the specific requirements of the machine learning model. You can feed these data to the algorithm and train it to do its designated task. By crunching these data, the machine or the AI understands the characteristics of an issue, makes necessary adjustments, predict accurate outcomes and performs any other tasks.
We can broadly classify training data into two categories –
- Labelled Data
This is a group of data samples tagged under one or more meaningful labels. These labels can identify specific properties, characteristics, classifications and contained objects. It is also termed annotated data by many and is primarily used in supervised learning. Labelled data enables machine learning models to embrace characteristics associated with specific labels and further classify them into new data points.
For example, images of vegetables or fruits can be tagged as potato, tomato, onion, grapes, etc.
- Unlabelled Data
This denotes everything opposite of labelled data. It is the raw form of data that is devoid of any tags. So, it is much more difficult to identify and segregate between its properties, characteristics or classifications.
Unlabelled data is used in unsupervised machine learning, and the AI has to look for similar patterns in the dataset to derive logical conclusions. For example, if we present the AI with a set of images of fruits, it will look for specific patterns to identify an apple and a banana. However, they cannot classify them by their name until they are fed that specific information.
With recent advancements, there has been a new hybrid model of training data where it uses both supervised and unsupervised machine learning.
Training Data Used in Machine Learning
Traditional programming algorithms strictly abide by a set of instructions for generating output. The user needs to input necessary data, and the system will generate the output accordingly. However, in a machine learning model, it doesn’t rely on instructions. It can scan historical data and make necessary judgments without getting limited to rules. This is why the machine learning model is open to constant improvements, unlike traditional models, which get stagnant over time.
In a machine learning model, historical data acts like fodder. Just like humans learn to make better decisions by learning from experience, machine learning models observe their old training dataset and make accurate predictions. They use technology like image recognition to identify and classify images correctly. They can also make predictions by understanding the context of a sentence using NLP (Natural Language Processing).
But you cannot keep ramming in random data in the algorithm of an algebra calculator and expect it to work accurately. To find the perfect training data, the coder needs to find out which data fits the model specifications perfectly. We can only achieve this through regular evaluation of test data. They can understand the algorithm model’s performance efficiency through test data. There are two different types of data set that are often used.
- Training data
This dataset teaches machine learning models to identify specific patterns from a particular task. The training dataset is mostly used to find out the effectiveness and accuracy of the training or the model.
- Validation data
This dataset is used frequently during the training phase for continuous evaluation. Although the basic difference between validation datasets is that it doesn’t learn from the mistakes even though it observes everything. This dataset is strictly used in the development stage to protect models from under or overfitting.
In machine learning algorithms, data testing is just like exams in real-life. Just like a student reads textbooks and aspires to score well in their exam, machine learning algorithms rely on “textbooks” like training datasets. The algorithm covers the various examples to answer the questions correctly. It is never possible to train the machine language algorithm with the answers to every question. However, you can always implement necessary codes to prepare the algorithm to modify questions of similar patterns and predict the closest possible answers. This is why we must update the data training set of machine learning algorithms periodically. Only through regular updation the algorithms can keep on improving.
There is no fixed answer to how much training a machine learning model needs. It depends on its application, complexity, expected outcome and several other factors. Read this blog and understand how you can use training data perfectly.
Adam Shearer worked as a professor of data science in a reputed university for more than ten years. He has recently joined My Assignment Help as an assignment writer.