The term “Machine Learning” was coined by Arthur Samuel, who defined it as “a field of study that gives computers the capability to learn without being explicitly programmed”.
Machine learning is a wonder among wonders, the basis of plenty of industries today. If you’ve just now heard about machine learning, you don’t need to worry. Read on to know everything you need to know.
What is Machine Learning?
In simple words, machine learning is a kind of artificial intelligence that helps software applications increase accuracy at outcome prediction without being programmed to do so. It is the automation and improvement of a computer’s learning process based on their memory instead of human assistance. It is like muscle memory but for computers.
How is that done? In short, the computer is fed data, and training algorithms are built along with learning models for the computer. These algorithms are primarily based on what data you have presented and what task you are trying to accomplish.
Importance of Machine Learning
Machine learning helps in the development of new products and acts as a way to understand customer behavior and the operational patterns of a business.
Difference between Machine Learning and Traditional Programming
At the most basic level, traditional programming involves feeding data and a program, which is then run to receive an output. This is not the case in machine learning, wherein data and output are fed and run during the training period, and the machine creates the program.
Uses of Machine Learning
- It gathers any past data for processing. The modeling quality depends upon the quality of the data.
- Sometimes, data is collected in the primary form and needs to be pre-processed.
- There is a division of the data input into three sets: training, cross-validation, and test sets.
- Testing the Model with data that was not fed to it at the time of training and assessing its performance.
- Customer relationship management: They use models for the analysis of, for example, emails and prompt team members to understand and reply to important messages first. Advanced systems are capable of recommending responses to emails.
- Self-driving cars: Algorithms can make it possible to detect other vehicles or people.
- Virtual assistants: A combination of supervised and unsupervised machine learning; these interpret natural speech and supply answers.
- Human resource information systems: Use machine learning models to sift through applications and filter the best candidates.
Types of Machine Learning
- Reinforcement learning: This is used to make a machine complete a multi-step function. There are clear and pre-defined rules. Data scientists work an algorithm and give it cues so that the function is negatively or positively performed, but even though this happens, the algorithm mostly works on its own.
- Unsupervised learning: When algorithms train on unaltered and unlabelled data. The data sets are scanned, and meaningful connections are found. The data and the outcome are both predetermined.
- Semi-supervised learning: Algorithms of the labeled type are mostly given, but the computer is allowed to explore and develop its way of knowing for sure.
- Supervised learning: Algorithms provided are labeled training data. Variables are specified. Both input and outcome are specified.
- Reinforcement learning: This is usually used to teach the machine a task with clearly stated rules. An algorithm is programmed for the completion of a task. Positive or negative cues are given by it to complete the task. Yet, for most of the functions, the algorithm makes the decisions.
Working of Supervised Machine Learning
Here, the algorithm needs to have both inputs and desired outputs. The following tasks are best performed using supervised machine learning:
- Binary classification: Division of the data into two categories.
- Multi-class classification: When multiple answers need to be chosen.
- Regression modeling: When continuous values need to be predicted.
- Ensembling: When the predictions of multiple machine learning models are combined to produce an accurate prediction.
Working of Semi-supervised Machine Learning
Semi-supervised learning works such that small bits of labeled training data are fed to the algorithms. Once this is done, the algorithm studies the dimensions of the data set and proceeds to apply them to new data. Why is this done? Because training over and over can be quite useful and not training tampers with the performance. When we employ semi-supervised machine learning, we get both the efficiency of unsupervised learning and the performance of supervised learning. Here are a few areas where this is used:
- Machine translation: Algorithms can translate language using less than a complete dictionary of words.
- Labeling data: When algorithms are trained on small data sets, they can easily apply the data to larger sets.
- Fraud detection: Fraud can be identified even when there are only a few positive examples.
Working of Unsupervised Machine Learning
This kind of machine learning does not need labeled data. Unlabelled data is looked through for patterns and connections. These are further utilized to group data points into subsets. Most deep learning types are examples of unsupervised learning. This is used for the following:
- Dimension reduction: Reducing the number of variables in the data.
- Clustering: Splitting data sets into clusters based on their similarities.
- Anomaly detection: Identification of the various uneven, incorrect, or abnormal factors in the data.
- Association mining: Identification of points in the data that occur multiple times.
Working of Reinforcement Machine Learning
Although a set of predefined rules are to be followed for the completion of a task, most of the decisions are taken by the algorithm itself. This type of learning also usually programs the algorithm to seek positive rewards, which are received when it completes a task leading to the ultimate goal, and avoid punishments, which it receives on making mistakes. This is used in areas such as:
- Video games: Bots learn to play various games.
- Resource management: Reinforcement helps organizations to plan out the utilization of resources.
Advantages of Machine Learning
- It helps organizations understand customers by a collection of customer data and correlation of this data with their behavioral patterns.
- Can learn associations and help organizations in product development and marketing initiatives that cater to the demands of the consumers.
- The primary driver of some companies is machine learning. For example, Google uses machine learning to find advertisements suitable to the viewers, and Uber uses algorithms to find a driver for a rider.
Disadvantages of Machine Learning
- Its installment can be quite expensive since machine learning projects are headed by data scientists that demand high salaries. Also, the software required can be costly too.
- There can be unintentional bias. In other words, algorithm sets that contain data about a certain population with an error can at best fail and, at worst, be downright discriminatory. An enterprise running on discrimination incurs regulatory and reputational harm.
Choosing the Right Machine Learning Model
- Put together the problem and the potential data inputs that may or may not be the solution. This step requires the assistance of a data scientist.
- Assemble, format, and label the data. This step is also usually driven by data scientists.
- Choose your algorithm. Assess how well it performs and whether it gives the proper desired outputs. This step requires the help of a data scientist.
- Continue to receive and filter outputs till they reach your desired accuracy. This step is carried out by data scientists with the assistance of experts who understand the problem in depth.
Prerequisites to Pursue Machine Learning
- Linear Algebra
- Statistics and Probability
- Graph Theory
- Programming Skills (languages like Python, R, MATLAB, Octave)
What Exactly Does Machine Learning Mean for Your Computer
A computer learns tasks from experience, and so the performance in a given task improves with increased experience. Let us study this with the help of an example:
If Experience (E) happens when playing checkers, and (T) is the task of playing checkers, and (P) is the probability of winning the next game; similarly, a computer program learns from E concerning T, and performance is measured by P. T performed, as measured with P, improves with increase in E.
Layman and Machine Learning
While a complex algorithm is no doubt more accurate, it is difficult to explain. Many organizations cannot function without understanding how each function came into being. Hence, many a time, despite lower accuracy rates, a less complex model is used because it is very difficult to explain how each output was produced to the layman.
Future of Machine Learning
It is no surprise that machine learning algorithms have been around for decades now. But their recent gain in popularity was the ever-growing market of artificial intelligence. Deep, complex machine learning models are the basis of all artificial intelligence projects today.
Some of the most competitive and reputable organizations like Amazon, Google, Uber, etc. employ machine learning. They are all in the race to sign customers up for a plethora of online services that cover a spectrum of machine learning activities that include data collection, model building, data preparation, data classification, and more.
As machine learning continues to expand in demand and importance, artificial information has taken the pedestal, and the machine learning sector has far to go, and it will not stop anytime soon.