Artificial Intelligence and its subsets are the emerging technology that every individual is learning. Also, this sector has huge employability compared to the others because of companies incorporating AI in their working model. With this stated it is very much important for a person to take the right kind of education and possess the right skill to get placed in these companies. We all know that Machine Learning can be done through many tools that are directly or indirectly related to coding. This coding can be performed with n number of programming languages like Java, Python, C, etc. to name a few. Out of these languages, two of the most widely used ones are R and Python because of their syntax friendly nature and fast execution of ML algorithms. These programming languages contain many libraries that perform all the activities once called within the working console. So, how do these libraries work??
The answer to this question is through statistical and probability algorithms. These algorithms are the same ones that we have used since our school days and now they have been blended with these libraries to perform the task of Machine learning. Without wasting more time let’s take a look at these Machine Learning Algorithms i.e. the main algorithms that one should learn if he is willing to shift his career towards the field of AI:
Eight Popular Algorithms of Machine Learning
As we all know that Machine learning is an iterative process and there are broadly three categories of Machine learning that are Supervised, Unsupervised, and Reinforced. Let’s take a look at the best and frequently used algorithms that one should learn in Supervised and Unsupervised.
- Linear Regression: This is the first and foremost Machine Learning algorithm that one should learn in terms of supervised machine learning. As the name suggests that this library is especially for regression related problems where the dependency of the independent and dependent variables are met with the help of gradient descent and finding the best fit line satisfying the equation of a line.
- Logistic Regression: Second most important algorithm that one should know in the field of machine learning. This is also a supervised machine learning algorithm and here the idea is to classify the target based on the independent features. These algorithms are very powerful and find applications in different companies for their working purpose. The logic behind this application is the probability that is 1 if success and 0 if failure and always works with discrete kind of data and not continuous.
- K Means Clustering: This is an unsupervised machine learning algorithm where we need to combine our independent features to form a target feature. The algorithm is widely used by many famous websites like Wikipedia so that our search process becomes easy whenever we are typing a query in the same. The idea behind is to form clusters of data based on their semantic relationship i.e. closeness they hold with each other.
- Support Vector Machine: It is an amazing supervised machine learning algorithm used for both continuous and discrete data that is it is suitable for both regression and classification. It also works behind the idea of gradient descent and tries to find a line that will separate the features with a large distance so that the probabilistic assumptions can be made more accurately.
- Naive Baye’s Algorithm: Yet another powerful supervised machine learning algorithm that one should learn to make their ML journey easier. This algorithm works behind the principle of Baye’s theorem according to which the probability of classifying one event (H) based on the other event (E) is given by the formula: P(H∣E)= P(E∣H)P(H) / P(E). This is indeed a very useful algorithm and is used by Google Gmail to classify an email whether spam or ham.
- K Nearest Neighbors Algorithm: It is somewhat similar to SVM in the sense that it also tries to draw a separation boundary in the form of a line to separate the features. The main difference is that in this algorithm the line that is drawn and the features that are separated are done with the help of some distance formula say Euclidean distance or Manhattan distance and then the prediction for a new data point is made by finding the most similar neighbors from the data. Here the neighbors are denoted by the letter K and hence the name K Nearest Neighbors.
- Decision Tree Algorithm: This is a tree-based algorithm wherein the goal is to find the target output by combining various inputs and forming a tree. A decision is then taken as to which tree to go for and how to compare the same with the target. This algorithm is highly preferred by Data scientists to solve their ML problems but there is a drawback with this model that is, it has somewhat low bias and high variance. This means it performs well with the training data but when a new test data is included then the results may not be that good. So, to overcome this problem we have ensemble techniques and Random Forest is the most preferred ensemble method out there.
- Random Forest Algorithm: It an extension to Decision Tree as it helps in bagging various Decision trees together and then votes out the best one to make predictions. This algorithm is one of the most preferred algorithm to solve various Kaggle and Hackathon challenges. Also, with the help of this algorithm, we can eradicate the concept of low bias and high variance into low bias as well as low variance that we incurred using the Decision tree.
Conclusion
So, for excelling in the field of Data Science one must learn at least these above 8 algorithms as they help in solving 80% of our problem and also contains the necessary power to transform any machine learning model into a benchmark model.
