Technology with time has progressed a lot. We have seen drastic changes in the past few years. From the introduction of various applications to the inventions of new forms of technology.
If I would have told you 20 years ago, that on your one single command, a speaker-looking thing will turn off the lights of your room and play songs for you, would you have believed me?
I don’t think so, you would just have sent me to the terrace to adjust the antenna and keep holding it in the desired position so that you could watch the live cricket broadcast. Unlike today, when you just open your laptop, computer, or smart TV to watch the match whenever and wherever you like to.
That’s the kind of drastic change and technological reform I was talking about. One such milestone in the road to the future came as Artificial Intelligence(AI).
Artificial intelligence (AI) is the emulation of human intellect in computers that have been trained to think and act like humans. The word may also refer to any machine that displays human-like characteristics like learning and problem-solving.
The capacity of artificial intelligence to rationalize and execute actions that have the highest probability of attaining a given objective is its ideal feature. Machine learning is a subset of artificial intelligence that refers to the idea that computer systems can learn from the data fed and adapt to new data without the need for human intervention.
We have seen numerous trends in Machine Learning and Artificial Intelligence. AI works on various algorithms, with a pledge of making human life easier with the help of technology.
All AI models strive to discover a function (f) that offers the most exact correlation between input and output variables (x) (y). Y=f(X)
The most typical scenario is when we have some historical data X and Y and can use an AI model to find the optimal mapping between them. The outcome cannot be 100% correct, because otherwise, this would be a straightforward mathematical calculation that would not require AI.
Instead, we may utilize the f function we learn to forecast new Y using new X, providing predictive analytics. Although different AI models use different techniques to attain this objective, the basic principle stays the same.
In this blog, we are going to focus on the Top-10 for beginners of the various AI concepts or algorithms present out there in the technological universe.
For more than 200 years, linear regression has been used in mathematical statistics. The algorithm's goal is to discover coefficients (B) that have the greatest influence on the accuracy of the function f we're seeking to train.
y= B0 + B1 * x is the simplest example, where B0 + B1 is the function in question.
Example of Linear Regression, Image source
The data scientists can achieve different training outcomes by changing the weight of these factors. The clear data with little noise (low-value information) and the removal of input variables with comparable values are the two most important prerequisites for success with this method (correlated input values).
This enables the financial, banking, insurance, healthcare, marketing, and other industries to use the linear regression technique for gradient descent optimization of statistical data.
(Must read: Multiple Linear Regression)
Another prominent AI method that may provide binary outcomes is logistic regression. This indicates that the model can forecast the result as well as identify one of two y value classes.
The logistic regression function is likewise based on modifying the weights of the algorithms, but it varies in that the output is transformed using a non-linear logic function. This function may be seen as an S-shaped line that separates true and false values.
The success criteria are the same as for linear regression: eliminating input samples with the same value and lowering the amount of noise (low-value data). This is a reasonably basic function that can be learned quickly and is ideal for binary categorization.
Linear Discriminant Analysis(LDA)
When there are more than two classes in the output, this branch of the logistic regression model can be employed. This model calculates statistical characteristics of the data, such as the mean value for each class separately and the total variance averaged for all classes.
The predictions allow for the calculation of values for each class and the identification of the most valuable class. The data must be distributed according to the Gaussian bell curve for this model to be valid, thus all large outliers should be eliminated beforehand. The LDA algorithm of AI is a fantastic and straightforward approach for data categorization and predictive modeling.
This is one of the most widely utilized, simplest, and efficient AI algorithms available. It's a traditional binary tree, with a Yes/No decision at each split until the model reaches the outcome node.
This approach is easy to understand, does not need data standardization, and may be used to address a variety of issues.Learn more about decision trees from the link.
It is a simple, yet really strong AI algorithm for solving a variety of complex problems. It is capable of calculating two sorts of probabilities:
The probability of each class occurring.
For a standalone class with an additional x modifier, a conditional probability.
The model is referred to as naïve since it is based on the assumption that all of the input data values are unrelated. While this is not possible in the actual world, this basic technique may be used in a variety of normalized data flows to accurately anticipate results.
This is a basic yet effective AI algorithm that uses the entire training dataset as the representation field. The outcome value predictions are produced by searching the whole data set for K data nodes with comparable values (so-called neighbors) and determining the resulting value using the Euclidean number (which can be readily computed based on the value differences).
Such datasets can use a lot of computational resources to store and analyze the data, suffer from accuracy loss when numerous characteristics are present, and must be curated regularly. They are, nevertheless, incredibly quick, precise, and efficient when it comes to discovering the required values in huge data sets. You can learn more about how KNN works in Machine Learning from here.
Learning Vector Quantization
The single significant disadvantage of KNN is the requirement to maintain and update large datasets. Learning Vector Quantization, or LVQ, is an advanced KNN model, a neural network that defines training datasets and codifies necessary outcomes using codebook vectors.
As a result, the vectors are initially random, and the learning process entails changing their values to enhance prediction accuracy and consequently, locating the vectors with the most comparable values yields the best level of accuracy in predicting the end value.
(Similar blog: Machine Learning Algorithms)
Support Vector Machines
This AI algorithm is one of the most extensively discussed among data scientists because it offers extremely robust data categorization skills. The so-called hyperplane is a line that divides data input nodes with different values, and the vectors from these points to the hyperplane can either support it (when all data instances of the same class are on the same side of the hyperplane) or defy it (when all the data instances of the same class are on opposite sides of the hyperplane) when the data point is outside the plane of its class.
The hyperplane having the most positive vectors and separating the most data nodes would be the best. SVM is a very sophisticated classification machine that may be used to solve a variety of data normalization issues.
Random Decision Forests or Bagging
Random decision forests are made up of decision trees that evaluate many samples of data and aggregate the findings like putting many samples in a bag to get the most accurate output value.
Rather than identifying a single ideal route, many inferior paths are specified, resulting in a more precise overall outcome. If decision trees solve your problem, random forests are a variation of the method that yields even better results.
(Must read: How to use a random forest classifier in ML?)
Deep Neural Networks
DNNs are one of the most used AI and machine learning algorithms. Deep learning-based text and voice apps, deep neural networks for machine perception and OCR, as well as employing deep learning to enhance reinforced learning and robotic movement, as well as other DNN applications, have all seen substantial advances.
Example of Deep Neural Networks, Image Source
These were the Top-10 most popular AI algorithms for beginners. These algorithms are widely used by data scientists, computer experts, and have different AI applications all around the globe.
The way the AI market is increasing, if someone begins with these and gains expertise in AI algorithms and starts a career right away, he or she would be solving complex AI/ML problems soon.
(Recommended blog: Deep Learning Algorithms)
On an endnote, we must look at the bright future of AI, especially the ones beginning with the algorithms, and try to look at all the applications of AI around us for a better understanding.