Machine Learning
Have you questioned why people are talking about machine learning but never had a clear idea about what it means? Are you tired of nodding your way through conversations? Let’s revamp that!
One area of computing that is improving the way we use our smartphones and using the web is Machine learning.
Sometimes machine learning, deep learning, and AI are used interchangeably. However, they are entirely three distinct ideas of computing, but they are connected. Machine learning is more closely related to data mining and statistics than it is to AI. It is the extraction of knowledge from data you have a question you are trying to answer.
If we talk about Artificial Intelligence, it is entirely a different thing from Machine learning, and deep learning, actually deep learning and machine learning both are the subsets of AI.
Machine learning focuses on the development of computer programs that can access data and use it to learn for themselves. The goal of machine learning generally is to understand the structure of data and fit that data into models that can be understood and utilized by people.
Deep learning is just a subset of machine learning. In fact, deep learning technology is machine learning and functions in a similar way (hence why the terms are sometimes loosely interchanged).
Machine-learning can be split into three widely adopted machine learning methods. Let’s have a look at what they mean;
Supervised Learning
You teach train the machine using data that is well labeled, which means that the information is already tagged with the correct answer to the right outcome. The higher the data set, the more the machine can learn about the subject matter. After the machine is trained, it’s given new previously unseen data, and the learning algorithm then uses the past experience to provide you with an outcome.
Supervised learning problems can be further grouped into regression and classification problems.
- Classification: A classification problem is when the output variable is a category, such as “blue” or “green” or “happy” and “unhappy.”
- Regression: A regression problem is when the output variable is a real value, such as “dollars” or “weight.”
For example, with supervised learning, an algorithm may be fed data with images of an eagle labeled as bird and images of phones labeled as gadgets. By being trained on this data, the supervised learning algorithm should be able to identify unlabeled eagles images later as birds and unlabeled phone images as gadgets.
Unsupervised Learning
Unsupervised learning is where you only have input data (X) and no corresponding output variables.
The goal of unsupervised learning may be as straightforward as discovering hidden patterns within a dataset. Still, it may also have a purpose of feature learning, which allows the computational machine to automatically detect the representations that are needed to classify raw data.
What if you didn’t know the sale price for each house? Even if all you know is the size, location, etc. of each house, it turns out you can still do some really cool stuff. This is called unsupervised learning.
Clustering is the most common unsupervised learning technique. It is used for exploratory data analysis to find hidden patterns or groupings in data. Applications for cluster analysis include gene sequence analysis, market research, and object recognition.
An example of this is when you want to group consumers that have the same purchasing behavior. It may be determined that women of a specific age range who buy good quality colognes are likely to have more compliments from people, and therefore a marketing campaign related to colognes can be targeted to this audience in order to increase their number of purchases.
Reinforcement Learning
Reinforcement learning is also known as semi-supervised machine learning. Most of the machine learning system is in this type. Reinforcement learning is the act of teaching the machine with unlabeled data and learned from that data with the grading output. The machine learns from its experience by creating a feedback loop to improve it every time it performs.
The most basic example of this type of machine learning is solving a maze. Reinforcement learning will analyze many choices of a maze based on its failure on each decision to find the ultimate result.
Reinforcement learning is the training of machine learning models to make a sequence of decisions. The agent learns to achieve a goal in an uncertain, potentially complex environment. In reinforcement learning, artificial intelligence faces a game-like situation. The computer employs trial and error to come up with a solution to the problem. To get the machine to do what the programmer wants, artificial intelligence gets either rewards or penalties for the actions it performs. Its goal is to maximize the total reward.
Sectors of the applications of Machine learning
Financial Services
A company that deals with finance can be protected with the help of machine learning. This may help them stumble on financial fraud that may occur in the future. Aside from this, machine learning can help predict catalysts or opportunities that can be implemented for future investments.
Virtual Personal Assistants
We know that assistants help us guide and assist us in a particular direction. They are the ones who can give us suggestions or recommendations. Typically, we have they give us more detailed information about a subject we don’t know about. In this case, we now have virtual assistants.
Siri, Google Now, and Alexa are some of the examples of Virtual Personal Assistants. They assist information to us. You just have to talk to them on a device like a smartphone or a speaker that they are currently installed. Then they give you the information about the things you ask of. This makes our lives easier, isn’t it?
Marketing and Sales
Ever wondered while scrolling through the web, you see relevant ads that are somehow related to your previous searches? An example of this is a bag you were searching on the internet. Later on, you’ll see advertisements suggesting good deals for it.
It is said that Machine learning technology predicts future events on the basis of previous involvement. In this case, marketing and sales bases of previous capture customer’s likings, and it promotes sales and marketing.
Traveling
Had you booked an Uber or Lyft? You may have seen the estimated cost of your rides. That’s because of machine learning.
Sometimes if you want to reduce the price of your ride, you can choose the option to carpool with people. This also shows how much you save. Thank you to machine learning!
Healthcare
Sensors that are wearing by the patient provide information regarding an individual’s condition. It could be a heartbeat, blood pressure, etc. The information gathered through the sensors could help doctors diagnose the health and condition of a person.
Social Media Services
How enjoyable and colorful your social media has become? Whatever’s in your mind, that thing is, social media gives you advertisements for your particular interest. So, this is all about that Social Media has smartly connected with Machine learning in order to make your social presence beneficial and knowledgeable. It shows your metrics on where to improve.
5 Different Machine Learning Algorithms
1. Linear Regression
Linear regression is supervised learning in machine learning. It finds the relationship between two continuous data and to analyze if those data have a deterministic relationship.
For example, we can relate to the relationship between sunny and rainy. Based on that relationship, we can forecast the results if there’s a high chance of being sunny so we can go out and have a picknick. Maybe it will rain, then we must have to be prepared to bring an umbrella.
We can use this algorithm to predict future sales or the stock price on the stock market.
2. Decision Trees
Decision trees are a form of supervised learning that can be used for both classification and regression purposes.
They are typically utilized for classification purposes. The model takes in an instance and then goes down the tree, testing significant features against a determined conditional statement. Depending on the result, it will go down to the left or right child branch and onward after that. Typically the most significant features in the process will fall closer to the root of the tree.
An example of this is: What are you going to do if the weather is sunny or rainy. Will I walk, clean, or shop?
3. K-Nearest Neighbors
K-Nearest Neighbors, KNN for short, is a supervised learning algorithm specializing in classification.
The algorithm looks at different centroids and compares distance using some sort of function, then analyzes those results and assigns each point to the group so that it is optimized to be placed with all the closest points to it.
4. K-Means Clustering
K-Means is a popular unsupervised learning classification algorithm typically used to address the clustering problem.
The ‘K’ refers to the user inputted a number of clusters. The algorithm begins with randomly selected points and then optimizes the clusters using a distance formula to find the best grouping of data points. It is ultimately up to the data scientist to choose the correct ‘K’ value.
5. Random Forests
Random forests are a popular supervised ensemble learning algorithm. ‘Ensemble’ means that it takes a bunch of ‘weak learners’ and has them work together to form one strong predictor.
In this case, the weak learners are all randomly implemented decision trees that are brought together to form the strong predictor — a random forest
Future of machine learning and Conclusion
Machine learning is fascinating. It continuously makes our lives easier and enjoyable. We now understood how Machine learning helps us to predict data and give us an output that will help improve anything.
Thank you for spending time reading my article.
