We humans learn from experiences and machine follows the instructions given by Humans. What If machines start to learn from experiences? This interesting activity is called as Machine Learning. Instead, it is more than just learning, it is also about reasoning and understanding. This is an immensely powerful way of being able to utilize the technologically advanced tool we have. It learns from the previous data and builds the foremost accurate model. i.e. For higher accuracy – More the data – Better the model it will predict.
What is Machine Learning?
A machine learning program begins with a generic formula but after every attempt it refactors its formula. As the formula is continuously using more experiences, the outcome too is improved. Self-improvement, in calculating the target outcome based on a new set of experiences, is the core of machine learning. We develop software applications which can become more accurate at predicting outcomes. Machine learning algorithms have the ability to study historical data as input to predict new output values. How fascinating is that if we can predict to see which direction my business would go, which buyers have a higher probability of buying which products or even which price variation of the product will have what buying implications?
Thus, another definition of Machine Learning by Tom Mitchell which is more technical is - A computer program is said to learn from experience E with respect to some class of tasks T and performance measure P if its performance at tasks in T, as measured by P, improves with experience E. (1)
What is ABC of ML?
Machine Learning is the core of Predictive Analytics (PA). One can perform PA using ML technologies. In order to understand this relation, let us first learn about Analytics and PA.
Analytics often involves studying past historical data to research potential trends, to analyze the effects of certain decisions or events or to evaluate the performance of a given tool or scenario. The goal of analytics is to improve the processes by gaining knowledge which can be used to make improvements or changes.
Analytics can be divided into three categories:
- Descriptive Analytics – ‘Insight into the Past’
- Predictive Analytics – ‘Understanding the Future’
- Prescriptive Analytics – ‘Advice on Possible Outcomes’
How machine learning is possible in Today’s World?
Well, that’s only because we have an enormous amount of data available. Everyone is online nowadays. Today Machine Learning has all the recognition that it needs. The intelligence of machines can only be achieved with the help of machine learning. This is helpful in businesses for routine tasks. Many industries have an enormous amount of data available and want to perform some operations and make intelligent decisions. Machine Learning helps in creating models for data analysis and to deliver the most accurate results. By building such pinpoint Machine learning models there will be a more profitable opportunity for businesses, and it avoids unknown types of risks.
Exploiting this immense potential of the tool to excel in business is no longer a far-fetched pipe dream. Many companies have already discovered this, and as technology advances at a rapid rate, it’s clear now that machine learning and marketing get along very well. Machine learning can improve the customer’s online shopping experience in many ways by guiding the buying journey, making personalized product recommendations to help the customer find what they want. The retail giant, Amazon, has harnessed the power of machine learning quite unlike any other, with 35% of their annual revenue generated through personalized product recommendations. Isn’t that amazing?
How can a Machine Learn?
In simple terms, a machine “learns” by looking for patterns among the massive data supplied to it, and when it sees one, it adjusts the program to reflect the “truth” of its finding. The more data you expose the machine to, the “smarter” it gets. And when it sees enough patterns, it begins to make predictions.
The following are the ways we can use to make the machine learn-
- Supervised Learning – The supervised approach is like human learning under the supervision of the teacher. The teacher provides examples to the student and the student then derives general rules from the specific examples. Similarly, Supervised Learning is task driven. It occurs when an algorithm learns from example data and associated target responses, in order to predict the correct response when posed with new examples.
- Unsupervised Learning – Unsupervised learning occurs when an algorithm learns from the example provided without any associated response. The algorithm resolves the data patterns on its own. In this type of learning, the training data does not include Targets. Here, you do not tell the system where to go, the system must understand itself from the data provided.
- Reinforcement Learning – Reinforcement learning is a behavioural learning model where the algorithm provides data analysis feedback, directing the user to the best result.
Let’s first understand Supervised Learning with an example- Suppose your father gives you one billion coins of three different currencies say one Euro, one Rupee, and one dollar. Each coin has different weights, for example, a coin of one Euro weighs 5 grams, a one-rupee coin weighs 3 grams, and a coin of one dollar weighs 7 grams. So, the Modal will predict the currency of the coin, here the weight becomes the feature of the coins while currency becomes the label when we feed this data to the machine it learns from which feature is associated with which label. For example, it will learn if a coin is of 5 grams it will be one Euro coin. Now if we give a new coin to the machine based on the weight of the new coin the model will predict the currency. Hence, Supervised Learning uses labelled data to train the model. (2) Here the machine knew the feature of the object and the label associated with the feature.
Let's move to Unsupervised Learning and see the difference – Suppose you have a cricket scorecard of various players with their respective scores and wicket taken when you feed this data set to the machine and machine identifies the pattern of the player performance. So, it plots the data with the respective wickets in the x-axis and runs on the y-axis while looking at the data you will clearly see that there are two clusters, the one cluster are the players who score high runs and took few wickets, and the other cluster are the players who took more wickets and score few runs. So here we interpret these two clusters as batsmen and bowlers and the important point to note here is that there was no label of batsman and bowler. Hence learning with unlabeled data is Unsupervised learning.
Finally, Reinforcement Learning – which is reward-based learning, or we can say it works on the principle of Feedback. Let's say you provide the machine with an image of a dog and ask it to identify it. The machine identifies it as a Cat. So, we give negative feedback to the machine saying that it is a Dog image, the machine learns from the feedback and then it comes across any other image of a dog it will be able to classify correctly, that is Reinforcement Learning.
Which model/algorithm should I use?
The answer to this question depends on various factors, but mainly considers three aspects Accuracy, training time, and ease of use. Without trying out different algorithms, even an experienced data scientist cannot predict which algorithm would perform the best. It is difficult to advocate a one-and-done approach, but based on the above three aspects, you can decide which algorithm to try first.
Let’s talk about some applications of Machine Learning which we have faced in real life: -
- Social Media Personalization – Have you accessed websites such as Amazon, Netflix, etc. for purchasing books, and different products, watching films, and television programmes? Every time you visit these sites, the recommendations of products, books, and movies based on your liking show up on your screen. How does this happen? These websites use ML to churn your past purchase and browsing data in order to predict what you like and thus recommend the items as per these predictions. Hence, it is targeting you with this advertisement with the help of Machine Learning.
- Spam detection – Spam detection was one of the initial problems solved by Machine Learning. Till a few years ago, email providers made use of rule-based techniques to filter out spam. However, with the advent of Machine Learning, spam filters are making new rules using a brain-like neural network to spam emails. The neural networks recognize phishing messages and junk mail by evaluating the rule across a huge network of computers.
In the Future, Machine Learning will be able to help in building self-learning robots and machines which are expected to improve their performance without using any human involvement. In this way, a machine can decide based on data to predict future actions. Machine Learning will continue to have a great impact in our lives in the future the need of the hour is to maintain a high-speed processing system that will carry out ML-based algorithm with high accuracy and precision free from any biases with the improvement of ML tools the data scientist will be able to focus more on efficient ML model development rather than spending time on tedious production tasks.
“Machine Intelligence is the last Invention that humanity will ever need to make.” - by Nick Bostrom
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