In a rapidly-moving and ever-dynamic digital world, there has been an emergence of extremely intuitive fields that have transformed the direction of the digital sphere. One of these disciplines involves the use of data, mathematical concepts and statistical algorithms to help machines learn and become partially or entirely autonomous in performing given tasks. This data-driven area of study under Artificial Intelligence is known as Machine Learning. Machine learning has proven to be advantageous in nearly every broad discipline (like, healthcare, marketing, finance, etc.) due to its predictive nature as well as its ability to enable smooth automation of tasks with minimal human intervention. Since algorithms form the cornerstone and guiding force of machine learning, this article will explore popular machine learning algorithms in data science for the ease of beginners. Read further to find a comprehensive guide to a basic machine learning algorithms list and start your learning journey today!
The Ultimate Beginner’s Guide to Machine Learning Algorithms:
Since the principles of all machine learning algorithms in data science are grounded in mathematics and statistics, it is important that you get some basic idea on each of these disciplines and their key concepts in the context of machine learning. However, these can also be learned along the way as you dive into different machine learning algorithms and their meanings. These will also be touched briefly throughout this section. Remember that the process may be slow but you must be patient while trying to master each technicality as machine learning algorithms leave minimal to no room for errors.
I. Firstly, ML algorithms are broadly divided into two parts: Semi-supervised, Reinforcement, Supervised and Unsupervised machine learning algorithms. These are further divided into Regression and Classification, and Clustering and Association algorithms respectively. Let us now dive into each one of these machine learning algorithms individually!
- Supervised Machine Learning Algorithms – This type of machine learning algorithms are concerned with making future predictions based on a set of input data and given outputs. By analyzing these known outcomes to previous data, supervised machine learning algorithms can make accurate predictions about future outcomes to similar inputs. For this, labeled datasets are used, wherein every data point is assigned a distinct name, meaningful tag or anything that eases identification. Some supervised machine learning algorithms examples include linear regression, native bayes, decision tree, support vector machines (SVM), random forest, gradient boosting, etc. There are two kinds of supervised machine learning algorithms in data science.
- Regression Algorithms: Regression studies the relationship between dependent and independent variables to make informed predictions. In the context of machine learning, regression makes predictions of continuous output values on the basis of input data. Machine learning regression algorithms can be of various types like linear regression, polynomial, and so on.
Classification Algorithms: As the name suggests, machine learning classification algorithms do the job of categorizing data into different predefined sets or groups. They first analyze the way past data is divided into categories and then use this information to classify new inputs.
II. Unsupervised Machine Learning Algorithms – Contrary to the above type, unsupervised machine learning algorithms in data science deal with unlabelled data and are used for complex analysis among other tasks. In this case, the machine learning models have no predefined information and work on their own to identify any kind of structure within the given datasets. Some of the unsupervised machine learning algorithms examples include K-means clustering, hierarchical clustering, K-nearest neighbors (KNN), anomaly detection, apriori, independent component analysis, etc.
- Clustering: Clusters in machine learning are basically groups of data values that are identical in any way or present patterns and thereby belong together. Thus, machine learning clustering algorithms identify these clusters with similar data values within heterogeneous datasets and group them together. Besides helping to spot similarities, these machine learning algorithms can also detect odd values and outliers.
- Association: Association pertains to any kind of relationship between variables. Therefore, association machine learning algorithms recognize relationships or patterns between different values within bulky datasets. These patterns are also known as co-occurrences. By doing this, these machine learning algorithms can help identify certain interesting rules to explain these associations.
III. Semi-Supervised Machine Learning Algorithms – These are quite similar to supervised machine learning algorithms in terms of making predictions on output data based on given input data. The only difference is that they learn from training datasets that are partially labeled and predominantly unlabeled. Some examples of semi-supervised machine learning algorithms include image classification, text classification, and so on.
IV. Reinforcement Learning Algorithms – RL Algorithms are mainly responsible for decision-making. In this case, models learn from data that is collected from several machine learning systems in order to determine which is the most optimal action and help make the most rewarding decision. Essentially, reinforcement learning algorithms learn from trial-and-error methods on their own to analyze and suggest the best possible outcome.
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