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We explicitly give the computer a list of rules for a particular task. The rules-based method is used to program computers.
Machine learning differs from rules-based programs and even supersedes them because it can infer these rules by itself. Imagine you are a bank manager and want to know if a borrower will likely default. A rules-based method would have the bank manager or other experts explicitly telling the computer to reject an application if the applicant's credit score is below a certain threshold.
A machine-learning algorithm would, however, simply use historical data about customers' credit scores and loan outcomes to determine what this threshold should consist of.
The machine learns from the historical data to create its own rules. It's important to note that this is only an introduction to machine learning since real-world models are usually much more complex.
It's still a good example of how powerful machine learning can be.
The data you have will allow you to optimize any KPI. You could, for instance, use a customer dataset to predict which customers are at risk of leaving so that you can prevent churn from happening.
Modern approaches to machine learning have made huge strides, and they can do much more than that. Machine learning algorithms are the foundation of many technological advances that we depend on, from self-driving vehicles to automated email filters that flag spam in your inbox.
Let's now examine the types of machine-learning algorithms that can solve specific problems.
Types of Machine Learning
Machine learning algorithms can be classified into three main categories: supervised learning (though other classification schemes may also be used) and unsupervised learning.
Learning Supervised
The term supervised machine learning describes algorithms in which the machine learning model receives a data set with labels that indicate the quantity of interest (often referred to by the terms response or target).
Semi-supervised learning is a method of training AI models using a mixture of unlabeled and labeled data.
You'll have to label data if you are dealing with unlabeled information. Annotating examples is what's called labeling.
It helps train a machine-learning model. Humans are usually the ones who perform labeling, and it can be time-consuming and expensive. There are ways to automate labeling.
The loan application scenario discussed earlier is a great example of supervised learning. We had access to historical information about the credit scores of past loan applicants (and possibly income levels, age etc.).
Alongside explicit labels that told us whether the person in question defaulted, we had historical data about past loan applicants' credit scores (and possibly income levels, age, etc.). The subdivisions of supervised learning algorithms are regression and classification.
This difference is the quantity we are aiming for. If you want to choose between discrete categories, such as whether the applicant will default or not or if this is a picture of a dog or a person, then the problem is called classification.
The problem is called classification because we are trying to identify the category that a data point belongs to. Regression is the term used when our target variable is continuous. Predicting the price of a home based on the number of rooms and the location.
Unsupervised Learning
Unsupervised learning is a problem in which we are given data without labels and simply look for patterns. Say, for example, you are Amazon.
Can we identify clusters (groups) of customers based on their purchase history?
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Even though we do not have definitive, explicit data about a person's interests, identifying a cluster of people who purchase similar products can help us make recommendations based on the items that other customers in the cluster also bought.
Amazon's "you may also be interested" similar systems power carousel.
K-means Clustering is a type of clustering model that assigns different groups of clients to clusters or groups based on similarity in their behavior patterns.
It works on a technical basis by determining the centroid of each cluster. This is then used to determine the initial mean value for the cluster. The clusters are assigned based on the similarity of new customers to existing members.
Once we have identified the clusters, we can then analyze their characteristics. Imagine, for example, that we notice a certain cluster buying many video games.
We can then make an educated guess as to whether this group is a gaming group, even if no one has told us.
We could use these labels to create supervised models. For example, we might be able to predict the amount of money that a 25-year-old gamer will spend at our store compared to 50-year-old fishing enthusiasts.
Reinforcement Learning
Reinforcement Learning is a group of machine-learning algorithms in which we ask a computer agent to perform a task without providing it with much information about what to do.
We allow the computer to make its choices, and based on whether or not those choices result in the desired outcome, we can assign penalties and rewards. This process is repeated multiple times to allow the computer to learn by trial and error, and repeated iterations are the best way to do something.
Imagine this as a carrot-and-stick approach to machine learning. It's like the computer playing a game to discover what works and doesn't.
Intriguingly, the results of reinforcement learning have been most impressive when applied to games. Google's AlphaGo model was created using reinforcement learning. It defeated even the best human Go players. Google has extended this technology to AlphaZero - a successor of the original AlphaGo, which chess players use to determine their best strategies.
Deep Learning
You've probably heard of deep learning if you've read about machine learning.
You might wonder how deep learning fits in with the above paradigm. Deep learning is an area of machine learning that breaks down a problem into several layers of neurons. These neurons are based on the way neurons work in the brain.
Deep learning is the name given to this class of machine learning because artificial neural networks (the collection of layers of neurons) often contain many layers.
Recent advances have expanded deep learning capabilities to include unsupervised and reinforcement problems.
They have also shown incredible results. Deep learning is at the heart of many of the most recent advances in computer vision.
These include self-driving vehicles and facial recognition systems. Deep learning is also responsible for the success of natural language processing. This allows computers to understand human conversation and powers Siri and Google Assistant.
How does Machine Learning Work?
How do machine-learning algorithms predict the future, then? They use computational techniques to collect information directly from the data without relying on mathematical models or equations.
This is best explained by referring to gaming, one of the most prominent applications of machine learning. Machine learning is extremely effective in mastering board games like chess and Go.
AI programs are used to try and "out-compute" their opponents, evaluating millions of moves per second. They would also apply strategies based on human understanding of the game.
IBM's Deep Blue, which beat Garry Kasparov in a six-game match in 1996, used a more sophisticated version of brute force.
Machine learning, however, offered a new approach. ML-powered software would start with only knowing the game's basic rules and desired outcome (victory).
The strategy of the program would be developed organically through analysis of data from previous games and testing and refining that knowledge in new games.
AlphaGo, a program that defeated the best human Go player in the world, was taught this way. This was a huge victory since Go is much more complicated than chess.
Previous AI programs were never able to defeat the top players. AlphaZero's successor AlphaGo used the same machine-learning techniques to achieve a superhuman skill level in chess by playing itself over and again for 24 consecutive hours.
A similar program, Stockfish, is now the strongest CPU chess engine.
Video games have also used the same algorithms. Watch the video below to see how machine learning can be used to teach an algorithm to play Super Mario World.
It only needs a simple goal (advance to the right).
Why Is Machine Learning So Valuable?
Machine learning is very good at Super Mario World and Go. Why is that important for business? Or the sciences? Machine learning is a great way to identify patterns in data.
Machine learning can be used to make objective, repeatable decisions. Machine learning is used in many industries, including manufacturing, retailing, healthcare and life science, travel and tourism, financial services, energy, feedstock and utilities.
Machine Learning Steps
It seems impossible to impart intelligence to machines. It is really simple. You can break it down into seven major steps:
- Collecting Data:
Machines learn by analyzing the data they provide. Collecting reliable data is essential for your machine-learning models to find the right patterns.
How accurate your machine-learning model will be depends on the data quality you provide. If you use incorrect or outdated data, you will get wrong predictions or outcomes.
Use reliable data since it will affect the results of your model. Good data is relevant, contains very few missing and repeated values, and has a good representation of the various subcategories/classes present.
- Prepare The Data:
You must prepare your data after you have collected it. This can be done by:
- Please put all your data together and randomize it. It is important to ensure that the data are distributed evenly and that the order does not impact the learning process.
- Data cleaning to remove duplicate values, duplicate rows and columns, unwanted data etc. Changing the dataset's index or rows and columns may be necessary.
- Visualize data to see how it is organized and the relationships between variables and classes.
- Divide the cleaned data into two sets: a training and a testing set. The training set is where your model will learn. The testing set is used for checking the model's accuracy after training.
- Selecting A Model:
A machine-learning model is the result you get when running a machine-learning algorithm on collected data. The model you choose should be relevant to the job at hand.
Scientists and engineers have developed various models suited to different tasks, such as speech recognition, image detection, prediction, etc. You also need to decide if you want your model to be able to handle numerical data or categorical information.
- The Model of Training:
The most important part of machine learning is training. You pass your data prepared for machine learning to the model in training to help it find patterns and make forecasts.
The model will learn from the data and be able to accomplish the tasks. With training, a model becomes better at predicting over time.
- Evaluation of the Model:
You must check the performance of your model after training. The model's performance is tested on data that has never been seen before.
This is done by testing the model on previously unseen data. Testing the same data used for training will result in inaccurate measurements because the model has already been trained to it and is familiar with it.
You will get a high level of accuracy.
Read More: Machine Learning Vs Deep Learning Vs Artificial Intelligence
You can accurately measure the speed of your model by using testing data.
- Parameter Tuning:
After you have created your model and evaluated it, check to see if there is a way that its accuracy could be improved.
You can do this by tuning the model's parameters. The parameters are the variables that the programmer generally decides. The accuracy of the model will be maximum at a certain value.
These values are found by parameter tuning.
- Making Predictions:
You can then use the model to predict future events based on data you have not seen.
Different Fields Of Application For Machine Learning
Machine learning is growing in marketing and many other areas: It contributes to science and technology advancements, makes it easier to use electronic devices in everyday life, etc.
These are just a few examples of the many fields of action that machine learning can perform, so we believe this intelligent system will soon be part of all aspects of our lives.
Science
Machine learning is equally important in science as it is in marketing. The intelligent processing of large data greatly simplifies the work of scientists.
Particle physics, for example, takes larger volumes of measured values and processes them to determine the deviations. It is also extremely useful in medicine, as doctors can use artificial intelligence to diagnose and prescribe treatments.
Robotics
Factory workers are particularly dependent on robots' omnipresence. They are essential for mass production because they enable the automation of various work processes.
However, they have little relationship to self-learning systems as they are programmed to perform a particular activity repetitively and without variations. If autonomous learning was introduced to these processes, machines could learn how to perform other tasks. Robots incorporating intelligent systems can be used in many other areas, from the space race to home.
Traffic
Autonomous driving is one of the greatest challenges in machine learning. These intelligent systems can make cars autonomous and safe from accidents.
It is impossible to program every situation, so autonomous cars must be equipped with Machine Learning systems. However, autonomous driving is one of many areas where these systems have made a mark. Intelligent algorithms in neural networks can analyze traffic and create effective systems that regulate smart traffic lights to reduce congestion and improve traffic flow.
Internet
Intelligent learning is a key component of the Internet. As mentioned above, the spam filter was already named. This program uses progressive learning to filter spam messages more precisely and make them disappear from your inbox.
Intelligent programs, which protect computers from malware and viruses more effectively, are the same. Search engines like Google RankBrain are self-learning systems. Even though the algorithm may not know how to handle a user's query because it is the first time it has been used, it can still deduce the meaning of your query.
Personal Assistants
These increasingly intelligent computer systems are becoming more important in the private sphere, making traditional homes smarter.
Moley Robotics, for instance, has created a smart kitchen with mechanical arms that can prepare meals. Personal assistants like Amazon Echo and Google Home, which allow users to control their homes, use machine learning to understand users better.
Many people have assistants, such as Siri, Cortana, and Google Assistant, allowing users to voice-command their smartphones to send questions or orders.
Games
The ability of artificial intelligence programs to play games was a key feature of the research. This has been proven in checkers, chess and Go, a game from China that is probably the most complex in the world.
It also faced the challenges of machine learning systems and humans. Video game developers often turn to autonomous learning to enhance their games. Game designers can also use this autonomous learning to create the best game between the computer's real players and their computer.
What Problems Can Machine Learning Solve?
Machine learning is helping businesses to make sense of the massive amounts of data they have, including mobile and sensor data.
Machine learning drives innovation across all industries because it can identify patterns and connections in large datasets. Here are some of the most promising applications across industries.
Fraud Detection
Financial fraud detection was expensive and ineffective before machine learning. Sorting through transactional data to look for irregularities required much time and resources.
Algorithms never tire and can work round the clock to identify fraudulent transactions, flag compromised accounts immediately, and increase safety for businesses and financial institutions. The more data machine learning algorithms analyze, the better they spot patterns. They also become better at understanding buying behavior and spotting outliers.
Marketing Targeted
Machine learning algorithms are particularly good at product and content recommendations (like what you see with Netflix and Spotify), predictive lead scoring (by identifying engagement patterns that suggest high conversion rates), customer lifetime-value forecasting (based on purchase history, logins, and other variables), and churn-rate prediction.
Machine learning algorithms excel at a variety of tasks, including product and content recommendation (like Netflix and Spotify), predictive leads scoring (by identifying patterns of engagement that indicate high conversion rates), forecasting customer lifetime value (based on logins and purchase history), and churn rate prediction (through subtle patterns in user behavior).
Traffic Forecast
Google Maps, for example, uses a combination of historical traffic data and real-time GPS transmissions from public buses and private cars to forecast traffic congestion, plot optimal navigation routes and estimate arrival times.
Chatbots
The success of natural language processing powered by machine learning is reflected in the widespread use of chatbots on the Internet.
Today, Chatbots are much more conversational and sophisticated than their predecessors, who often struggle to answer customer questions. Chatbots provide automated and scalable customer service. Customers can build chatbots within an hour with companies like Drift and without a development team.
Facial Recognition
In the 1960s, the first facial recognition programs were created. Face Recognition Grand Challenge launched in 2006 was the first time the technology took off, thanks to the increasingly powerful algorithms.
Machine learning and neural networks drive the deployment of facial identification in all kinds of applications today, from access control to secure facilities to identity verification at ATMs. In the future, facial recognition technology could be used for everything from digital advertising personalized to staff-free stores.
What Is The Future Of Machine Learning?
Machine learning has gained popularity as large companies and businesses use the technology to solve specific problems or drive innovation.
This investment is evidence that machine learning delivers ROI, particularly through the use cases that are proven and replicable.
Suppose Netflix, Facebook and Google Maps and many other companies can use the technology. In that case, your business will likely benefit from the same technology.
We'll see increasing applications as new machine-learning models are released and trained. These will be used across all sectors. Face recognition is a good example.
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What was once a novelty on the iPhone has now been incorporated into countless apps and programs, particularly those based on public safety.
Machine learning can be a great tool for businesses. But it is important to go beyond the futuristic hype and find the business problems the technology can solve.
