An introduction to Machine Learning

Machine learning is an exciting branch of Artificial Intelligence that helps computer systems learn and improve from experience. By developing computer programs that can automatically access data and perform tasks via predictions and detections, machine learning brings out the power of data in new ways.  

The Global Market is expected to grow at a CAGR of 44.1% during the forecast period 2018-2024.

Moreover, The North American region (80%) is expected to hold the largest market share and dominate the global machine learning market throughout the forecast period, owing to the supportive government policies and initiatives and the presence of key players in this region. 

For starters, machine learning is a core sub-area of Artificial Intelligence (AI). ML applications learn from experience (or to be accurate data) as humans do without direct programming. When exposed to new data, these applications learn, grow, change, and develop by themselves. In other words, machine learning involves computers finding insightful information without being told where to look. Instead, they do this by leveraging algorithms that learn from data in an iterative process.

At a high level, ML is the ability to adapt to new data independently and through iterations.  Likewise, Applications learn from previous computations and transactions and use “pattern recognition” to produce reliable and informed results.

The explosive growth of data has made it difficult for humans to process and make decisions. Machine learning helps us make sense of this data and uncover hidden patterns.

It is also important because it can be used to automate decisions and processes. For example, ML can be used to automatically approve or reject loan applications, or to identify fraudulent transactions.

By assessing data much faster and more accurately than humans, It can help businesses make better decisions, improve customer service, and increase efficiency.

At a basic level, ML algorithms are trained on data sets. A data set is a collection of data that is used to train the algorithm. The algorithm looks for patterns in the data set, and uses these patterns to make predictions or recommendations.

To illustrate, a retail company may use machine learning to predict how much inventory to order. The company will first train a machine learning algorithm on past sales data. The algorithm will then look for patterns in the data, such as seasonality or trends. Based on these patterns, the algorithm will make a prediction about future inventory needs.

The predictions made  will never be 100% accurate. However, the more data the algorithm is trained on, the more accurate it will become.

Broadly, there are 3 types  – Supervised Learning, Unsupervised Learning, and Reinforcement Learning.

Reinforcement Learning:

Reinforcement learning is a type of machine learning where the algorithm is “rewarded” for making correct predictions. The algorithm is given a data set, and it makes predictions based on that data set. If the predictions are correct, the algorithm is rewarded. The more rewards the algorithm receives, the more accurate it will become.

Supervised Learning:

Supervised learning is a type of machine learning where the algorithm is “trained” on a data set. The data set is used to teach the algorithm what to do.

For example, you can use supervised learning to train an algorithm to recognize objects in images. First, you would need a data set of images that have been labeled with the object that they contain. For example, the data set may contain images of cars that have been labeled as “car.” The algorithm would then learn to recognize cars in images by looking for patterns in the data set.

Once the algorithm has been trained, you can then give it new images to see if it can correctly identify the objects in those images.

Unsupervised Learning:

Unsupervised learning is a type of machine learning where the algorithm is not “trained” on a data set. Instead, it is left to learn from the data on its own.

For example, you can use unsupervised learning to cluster data points into groups. The algorithm will look for patterns in the data and group data points together based on those patterns.

Reinforcement Learning:

Reinforcement learning is a type of machine learning where the algorithm is “rewarded” for making correct predictions. The algorithm is given a data set, and it makes predictions based on that data set. If the predictions are correct, the algorithm is rewarded. The more rewards the algorithm receives, the more accurate it will become.

There are many different applications . Some of the most popular applications are

1.Face Recognition:  

Face recognition is done by extracting certain features from an image and matching them with the database. This process was earlier done manually. However, with machine learning, this process can be automated. The system first needs to be trained on a data set of images that have been labeled with the names of the people in those images. Once the system has been trained, it can then be used to identify faces in new images. 

2.Recommender systems: 

You must have come across this while using YouTube or Netflix. For instance, when you search for a particular thing, the system recommends similar things. This works on the principle of Collaborative filtering.  It’s a type of unsupervised learning where the system looks at the behavior of users and items to understand the relationship between them. This relationship is then used to make recommendations. 

3.Spam Classification: 

Spam classification is a type of supervised learning. The system is “trained” on a data set of emails that have been labeled as “spam” or “not spam.” The system then learns to identify spam emails by looking for patterns in the data set. Once the system has been trained, it can then be used to classify new emails as “spam” or “not spam.”

4.Advertising: 

Advertising is one of the most popular applications for machine learning. Machine learning can be used to target ads to specific users based on their interests. For example, if a user is interested in cars, the machine learning algorithm can target ads for car products to that user.

5.Sentiment Analysis: 

Sentiment analysis is a type of machine learning that can be used to understand the emotions of a text. The system is “trained” on a data set of texts that have been labeled with the emotions they express. Thereafter, the system learns to identify the emotions in new texts by looking for patterns in the data set. Once the system has been trained, it can then be used to classify new texts as “positive,” “negative,” or “neutral.”

6.Anomaly Detection: 

Anomaly detection is a type of machine learning that can be used to identify outliers in a data set. The system is “trained” on a data set of points that are known to be “normal.” The system then learns to identify outliers by looking for patterns in the data set. Once the system has been trained, it can then be used to identify new outliers in a data set.

The future of Machine Learning

Machine Learning is constantly evolving and the future looks very promising. With the advancement in technology, the data sets are getting larger and more complex. This is making it difficult for traditional methods to handle such data. Machine learning is the need of the hour as it can handle such large and complex data sets.