Machine learning is a type of artificial intelligence that trains computers to think as humans do: by learning from and improving on previous experiences. Machine learning can automate almost any operation that can be accomplished using a data-defined pattern or set of rules.

Machine Learning is a branch of study that focuses on teaching computer programs and algorithms to improve at a specific task. Insights extracted from data are used by machines. Machines must learn how to do things and anticipate in a world where machines perform the majority of the work. This is where artificial intelligence (AI) comes in. It teaches machines to learn on their own and predict outcomes based on prior knowledge.

Importance of Machine learning

It enables organizations to automate operations that were previously only possible for humans to complete, such as answering customer service calls, bookkeeping, and screening resumes. Image identification for self-driving cars, anticipating natural disaster sites and timeframes and analyzing the potential interaction of medications with medical conditions before clinical trials are all examples of how machine learning can scale to handle greater challenges and technical questions. This is why machine learning is so crucial.

Types of Machine Learning Models

Machine learning uses two types of techniques: supervised learning, which trains a model on known input and output data so that it can predict future outputs, and unsupervised learning, which finds hidden patterns or intrinsic structures in input data

Supervised Learning

In supervised learning, we train machine learning models by giving them a set of inputs (training data) and expected outputs or labels.

This approach basically teaches machines by example. During training for supervised learning, systems are exposed to large amounts of labeled data, for example, images of handwritten figures annotated to indicate which letter or number they correspond to.

However, training these systems usually necessitates a large quantity of annotated data, with some systems requiring millions of instances to master a task. As a result, the datasets that are utilized to train these systems can be rather large. Outsourcing or crowd-working services are frequently employed to complete the time-consuming task of annotating the datasets used in training. If you have known data for the outcome you’re trying to anticipate, use supervised learning.

Data Requirement – Supervised learning model needs structured data for training. Once the data is collected from multiple sources across multiple time frames and concerning various business entities the data then requires Annotations. Data Annotation is performed to attach labels to the data to make the machine recognize each entity with a label. Thus Data annotation is a crucial step for supervised learning. It is very important to choose the classes for Annotations wisely based on the outcome we are expecting from the model.

Unsupervised Learning

In contrast, unsupervised learning tasks algorithms identifying patterns in data, trying to spot similarities that split that data into categories. The model’s goal is to find the underlying structure within the data without any guidance. These techniques are mostly used in exploratory data analysis and data mining, where the goal is to discover new knowledge about underlying data rather than improve and predict existing knowledge.

Airbnb, for example, might group together houses for rent by neighborhood, while Google News might group together stories on related topics each day. Unsupervised learning algorithms don’t look for data that can be grouped by similarities or anomalies that stand out; instead, they look for data that can be grouped by similarities or anomalies that stick out.

Data Requirement – Unsupervised learning draws conclusions on unlabeled data. The output is just based on the collection of perceptions. The model is handed a dataset without explicit instructions on what to do with it. There are no labels attached or metadata provided with the data. The training dataset is a collection of examples without a specific desired outcome or correct answer. The neural network then attempts to automatically find structure in the data by extracting useful features and analyzing its structure.

Data Preparation for Machine Learning

ML depends heavily on data. The thing is, all datasets are flawed. That’s why data preparation is such an important step in the machine learning process. In a nutshell, data preparation is a set of procedures that helps make your dataset more suitable for machine learning. In broader terms, the data preparation also includes establishing the right data collection mechanism. And these procedures consume most of the time spent on machine learning. Sometimes it takes months before the first algorithm is built.

1. Data Collection– The first stage in AI development is Data acquisition. Here’s where companies collect and aggregate data. There are a few requirements to take into consideration when you collect the data: it should be high-quality, relevant, comprehensive, and big. When collecting data, it’s important to first define exactly how the system will be applied and make sure that the data we use to train the model is a good representation of the data it will handle when released to the market.

2. Data Processing– When you’ve collected your data that is relevant to your goals and ticks all the important boxes on the requirements list, it’s time to make it manageable, as well as make sure that it will cover every possible case your model will have to deal with in the future. This means your human experts will need to improve the data by:

Cleaning it

Removing duplicate values present

Reformatting it to fit the desired file formats

Anonymizing if applicable

Making it normalized and uniform

3. Data Annotation– It is simply the process of labeling or annotation making the object of interest detectable or recognizable while feeding into algorithms. Annotation is a complex process that deserves separate attention. If you want your model to train well, it’s important for the labels assigned to your data to be consistent and of high quality.

Wrapping Up

Machine learning uses algorithms to parse data, learn from that data, and make informative decisions based on what it has learned. The above information has certainly helped you in deciding if you will use supervised or unsupervised learning and your data preparation workflow.

TagX is dedicatedly involved in data collection and classification with labeling and image tagging or annotations to make such data recognizable for machines or computer vision to train AI models. Whether you have a one-time project or need data on an ongoing basis, our experienced project managers ensure that the whole process runs smoothly.

Prashi Ostwal - Author