Both ML & Artificial Intelligence are highly booming technologies. The ML is basically a subset of Artificial Intelligence. The ML is the field of applying and developing algorithms, which are capable of learning things from past experiences. If there is some type of behavior that existed in the past, then based on that there will be a prediction of whether it can occur again. I suppose there are no past experiences then the prediction won’t exist.
Machine Learning is majorly applied to solve difficult problems that include face detection and recognition, credit card fraud detection, and enable self-driving cars. It mainly uses complex algorithms, which constantly iterate over huge data sets. The reason behind making this is to simplify the details of the aspiring data scientists and machine learning enthusiasts who are always on the lookout to learn something new and more relevant. In this article, one can get a high-level understanding of the major machine learning algorithms.
Let’s gain few insights on some of the learning algorithms.
Artificial Intelligence Algorithms
The term AI algorithms are usually used to mention the details of the algorithms. But the accurate word to use for this is “Machine Learning Algorithms”. Artificial Intelligence is a culmination of technologies, which embrace Machine Learning and it is quite tough to define all the algorithms that are present in this vast field. As of now let’s understand the three important groups of algorithms.
The 3 major kinds of Machine Learning Algorithms are:
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Supervised Learning
The supervised learning algorithms are based on outcome and target variable mostly dependent variable. This gets predicted from a specific set of predictors which are independent variables. By making use of this set of variables, one can generate a function that maps inputs to get adequate results. The training method continues till the model gets a much-needed level of accuracy on the training data. For example, Autonomous Cars. The core algorithms that are present in supervised learning are Support Vector Machines (SVM), Decision Tree, and naïve Bayes classifiers, Ordinary Least Squares (OLS), Random Forest, Regression, Logistic Regression, and KNN.
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Unsupervised learning
The next machine learning algorithm is an unsupervised learning algorithm; these are quite similar to the supervised learning algorithms, but there is no certain target or result that can be estimated or predicted. As they keep on adjusting their models entirely depending on input data. The algorithm operates a self-training process without any kind of external intervention.
The unsupervised learning algorithm is mainly used for clustering populations in various groups that are majorly used for segmenting customers in a variety of groups for relevant types of intervention. The instances where the unsupervised learning algorithm is availed are Independent Component Analysis (ICA), Apriori algorithm, K-means, Singular Value Decomposition (SVD), and Principal Component Analysis (PCA).
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Reinforcement Learning (RL)
Reinforcement Learning (RL) has the constant iteration which depends on trial and error, in which the machines can generate the outputs based on the certain type of conditions, the machines are well-trained too so that they can take relevant decisions. For a better explanation, let’s consider the game of chess where the machine will be exposed to the environment where it gets self-training continuously with the method of trial and error.
The machine learns well depending on past experiences and then captures the most suitable and relevant information to develop business decisions accurately. In this manner, one can acquire the results, paths, correlations, and outcomes depending on the previous experience concluded by the machine. The best examples for Reinforcement Learning are Q-Learning, Markov Decision Process, SARSA (State – action – reward – state – action), and Deep Mind’s Alpha Zero chess AI.
Few examples of Machine Learning platforms or Artificial Intelligence are:
- Watson AI
- Google Cloud AI
- Microsoft Cognitive Services
If you are curious to master the craft of AI algorithms, then you should start immediately.
