Probably we are living in the most defining period of human history. This is the period where computing has moved from large mainframes to PCs and cloud. What is making it defining is not what has happened, but it is what comes in our way in years to come.
Due to fast mechanization, through a technological revolution, the word manual is slowly getting lost in a crowd and very soon it will vanish completely from our lives. There is no doubt that from past couple of year’s machine learning and artificial intelligence is increasingly getting popularity and integrating into our lives. At the moment Big Data is the hottest trend in the tech industry, and machine learning is incredibly powerful to make predictions or calculated suggestion b ya large amount of data.
What is making this period more exciting is the democratization of tools and techniques after the boost in computing. Now data scientist can build data crunching machines with complex algorithms for just a few dollars per hour. What is machine learning? What are the most common types of machine learning and how they are transforming our lives? In this article you can find the details of every possible machine learning algorithm along with its functionality and role in our lives.
What is Machine Learning?
Machine learning is a process of data analysis which is used to automate the building of analytical models. Machine learning is a branch of artificial intelligence, and it is based on the idea that machine should have to learn and adapt through experiences. Machine learning can be described as the science of getting computers to act without being explicitly programmed.
Machine learning gave us self-driving cars, practical speech recognition technology and efficient web search and a vast understanding of the human genome. Machine learning is pervasive as we are using it dozens of times a day even without knowing it. According to researchers machine learning is the best way to make progress towards human level Artificial Intelligence.
Machine learning is a sub-domain of computer science that gives computers ability to learn without being explicit programming. Samuel, an American pioneer in computer gaming and artificial intelligence,had coined the term machine learning in 1959. The concept of machine learning is evolving from pattern recognition, computational learning theory in artificial intelligence to study and construction of algorithms that can learn from and make predictions on data.
These algorithms follow strictly stating program instructions for data drove predictions and decisions. In short machine learning is a range of computing tasks where designing and programming explicit algorithms with good performance are difficult or infeasible.
Types of Machine learning algorithms
Tools learning algorithms are divided regarding their purposes. Broadly speaking Supervised and unsupervised are two widely adopted machine learning algorithms. There are other machine learning algorithms too such as semi supervised and reinforcement machine learning algorithms. Here is a quick review of all these four major types of machine learning algorithms.
Supervised Learning Algorithms
Supervised learning algorithms are trained algorithms which use labeled examples or inputs where the desired output is known. These algorithms require humans to provide both input and desired output. The learning algorithms receive a set of data along with corresponding correct output. These algorithms learn by comparing its actual information with correct outputs and find errors. After comparison, it modifies the model accordingly.
Supervised learning algorithms use patterns such as classification, regression, prediction, and gradient boosting to predict the values of the labels on additional unlabeled data.The supervised learning algorithm is useful in application where historical data is used to predict future events, such as it can anticipate when credit card transaction liable to be fraudulent and which insurance customer can file a claim. Decision Tree, Random Forest, KNN and logistic Regression are some of the examples of supervised learning algorithms.
Unsupervised Learning Algorithms
The unsupervised learning algorithm is used where data have no old labels. In case of this algorithms, we don’t have any target or outcome variable to predict. We can use it for clustering population in different groups. This algorithm figures out what is being shown. The primary objective of this is to explore data and find some structure within it for further analysis and predictions.
This type of algorithms works well in case of transaction data as it can identify customer segments with similar attributes and characteristics for the launch of successful marketing campaigns. Self-organizing maps, nearest-neighbor mapping, k-mean clustering and singular decomposition are some of the examples of the unsupervised learning algorithm.
Semi Supervised Learning Algorithms
Applications of the semi supervised machine learning algorithms are same as supervised machine learning algorithms. This type of machine learning algorithm makes the use of both labeled as well as unlabeled data for training. Typically this type of learning algorithm uses a small amount of labeled data with a large amount of unlabeled data.
In other words, we can say that semi supervised learning algorithms falls in between the other two types of algorithms. Practically it is applicable when the cost to label as high as it requires skilled human expertise. So in case when you have data set with the absence of names in the majority of observations but available in few you can apply semi supervised learning algorithm for making rational decisions. This type of machine learning algorithm is used with methods such as classification, regression, and predictions.
Reinforcement Learning Algorithm
This kind of learning algorithm makes the use of observations which are gathered from the interaction with the environment to take actions and to maximize the reward at minimum risk. Reinforcement learning algorithm learns from the environment continuously in an iterative fashion. In this process of learning the agent learn from experiences of the environment.
Reinforcement learning is a type of machine learning and a branch of artificial intelligence. This kind of algorithms allows machines and software agents to determine ideal behavior concerning the context to enhance performance automatically. In this case, simple reward feedback is valuable as a reinforcement signal.
In reinforcement learning algorithm, the machine is trained to make an accurate decision. In the event of machine learning algorithm, the device is exposed to the environment where it teaches itself through trial and error. This machine learns from past experiences and captures the best possible knowledge to make accurate business decisions. Markov Decision Process is an example of reinforcement learning algorithm.
It is possible to use different criteria to classify different types of machine learning algorithms, but classification by purpose is an excellent idea to visualize the big picture of machine learning. According to the problem and data in your hand can help you to decide which type of machine learning algorithm is best for you.
Some Conventional Learning Algorithms
This algorithm is used to estimate the real values by continuous variables. We use linear regression to establish the relationship between dependent and independent variables by fitting the best line. Here the best fit is called regression line, and It can be represented by a linear equation. Y=a*X+B.
It is a classification, not a kind of regression algorithm. We can use this algorithm to estimate discrete values by given set of independent variables. In simple words, this algorithm is used to predict the probability of occurrence of an event by fitting data to a log it function.
Another supervised learning algorithm which is used for problems based on classifications. It works for both categorical and continuous dependent variables. In this algorithm, we have to split the population into two or more homogeneous groups.
SVM (Support Vector Machine)
It is a binary classification algorithm. In this classification method data algorithm, we have to plot each data item as a point in n-dimensional space. Here n is some features, and the value of each element is the value of particular coordinate.
This classification technique is based on Bayes’ theorem with an assumption of independence between predictors. Naïve Bayes classifier assumes that the presence of a particular feature in a class is not related to the presence of any other characteristic. Naïve Bayesian model is easy to build and useful for large data sets. This algorithm is known as a highly sophisticated classification method.
I have shared an idea of some commonly used machine learning algorithm. My sole intention behind this is to help you to start learning about machine learning algorithm and view the world from a different angle.
Machine learning is completely reshaping the world. We no longer have to provide instructions to our computers for performing complex tasks like image recognition or test translation. Instead, we have to build a system to let them learn themselves. We don’t know, but it is already playing a significant role in our daily life. Technology is enabling computers to get smarter and more personal than ever before. Due to a lot of intricacies, subtleties, and pitfalls in maze or labyrinth structure of machine learning a better understanding of machine learning is important.
This article has successfully covered the basic theory surrounding the field of the machine of learning and different types of machine learning algorithms, but of course, there is a lot more to dig in and explore!
The Article Was Written By Essa John a passionate blogger these days a blogging monster at Buzzing Tech!