The Real World Applications of Machine learning!!
What is Machine Learning?
Machine Learning(ML) is a type of Artificial-Intelligence that can extract patterns out of raw data by using algorithms or methods. Machine learning aims at making sense out of data in a similar manner that a human being does. Can a computer learn on their own and perform or make decisions without necessarily being programmed? This is not science fiction but Machine Learning.
Today we take a look at the pertinence of machine learning in our day to day life. We all know that we are in the era of Machine Learning and artificial intelligence. As time goes by we see more and more machine intelligence coming into our world. What does this machine have that makes them an entry to our lives? we shall answer these questions in our article today.
According to research, we are in the golden age of Artificial intelligence and Machine learning. Given the rapid growth of this technology, we experience a change in our life. With ML we see the solving of complex problems that were once unsolvable.
Real world applicability of ML:
The first application of Machine learning is to teach computers how to manage the data more effectively. If we are to observe data, we cannot understand the patterns or get details from the data. The need for machine learning is on the rise with the increase of datasets and the emergence of big data. From data, machine learning algorithms can learn. This is the main purpose of machine learning.
Computerized analysis of spoken words in order to identify the speaker, as in security systems, or to respond to voiced commands. An analysis is performed by finding patterns in the spectrum of the sound and compares them with stored patterns of elements of sound, as phones, or of complete words. This involves stored data where a machine learns the unique patterns in the audio. when given audio it compares the sound patterns to find the best fit with a given percentage of accuracy. A high percentage means a likely match. speech recognition is mostly used in security systems.
The most basic is Siri the apple assistant that identifies the users’ voice and uses it in future reference.
A self-driving car is a type of vehicle that does not need a person to operate it. Instead, It uses advanced sensory technology like Sonar, lidar, GPS, radar, or odometry and inertial measurements to identify environmental changes and adapt to restore safe speed or distance. This enables the vehicle to make optimized decisions that result in arrival at the destination.
Deep learning is a class of ML that focuses on computer learning from real-world data using feature learning. This enables a car to turn raw complex data into actionable information. It involves getting meaning from raw data that enables decision making.
Obtaining a diagnosis of an ailment is one of the major applications of ML in the medical field.
Let us use a simple illustration of why ML is used in Medicine. The amount of data in the medical field is huge we can call it The Big data in medicine. Today unlike a few decades ago the computer processor is advanced hence coming in with great power. Looking at Machine learning a branch of computer science that gives the computer the ability to improve its performance. Combining ML and the huge data in the medical field we get the perfect application of ML.
Identifying a disease is a crucial process this makes the time of the essence. By combining research done by medical experts and ML it speeds up the process of identifying the disease and its traits. ML improves the quality of the drugs produced this enables a drug to perform its function with minimal side effects. Another use of ML is in personalized treatment. This involves monitoring a patient over time the data collected is then used to determine the best treatment based on their symptoms.
Do you ever wonder how media production companies are able to recommend a series or a movie to you? This is just one of the applications of ML in product recommendation. Product recommendation is a system that is designed to generate and provide suggestions for items or content a specific user would like to purchase or engage with.
Data in the format that the Recommendation service requires is extracted from the Commerce operational database and sent to Data Lake Storage or an Entity store. The recommendations service uses the stored data to train recommendation models. The models are trained on specific qualities this enables the model to recommend a particular product to one person and not the other.
A real-life scenario is when you enter a clothes shop the owner will identify the kind of clothes you wear based on your gender hence will recommend you check the male (if male) or the female side (if female). This is the same case when it comes to ML but in software.
This is the ability to perceive an object and identify it based on physical properties such as size, color, texture, etc. ML models use features to identify objects or people. The models are trained from data that describes the behavior and features that describe a person or object uniquely.
Stock market Analysis and fore-casting
Machine Learning when integrated with Banking and the stock market can prevent losses largely. ML plays a major role when it comes to stock predictions. ML model trained on data that includes the stock prices over time, the algorithm will make future predictions by using such a data set.
ML comes in handy when it comes to credit card fraud. Banks use ML models to tackle such problems.
Virtual Personal assistant
Virtual assistants like Alexa and Siri are able to give you the results that you need based on your previous interactions with them. A virtual personal assistant is a Software that can perform tasks or services for an individual based on commands or questions. Virtual assistants are also referred to as “chatbot“. This depends on the data from previous engagements.
A simple example is the use of a GPS service. When one uses the GPS service to commute or any other purpose Machine learning algorithms make it possible. Predictions like traffic predictions based on data are made possible by the use of machine learning.
Other applications include;
- Speech recognition.
- Making predictions.
- Product recommendation.
- Self-driving cars.
- Medical diagnosis.
- Virtual personal assistant.
- Object recognition.
- Stock market analysis and forecasting.
- Google lens
In every application of ML, we can see that data is the major force behind Machine learning. With data, we can apply ML in all fields and use it to gain insights that will help us in solving real-life problems. Machine learning applications are so vast and numerous. Soon we shall see ML become a huge part of our life.
Hope you liked our article leave a comment below if there are other ML applications we have left out.