If you are a newcomer to the industry, you may be wondering how to handle machine learning aspects and understand the important algorithms. But, there is just too much to learn and understand that you can easily wander and then end up learning nothing to the full efficiency. Hence, to help the freshers in the field understand the most important machine learning concepts, we have prepared a list of 10 machine learning algorithms that you should not miss.
Dive in and read what should know about machine learning.
- The principal component analysis algorithm is used by experts to decrease data dimension without losing much information. So, experts use the algorithm for computer vision, object recognition, and data compression.
- The linear regression algorithm is used to match constraints with the regression lines to avoid overriding. Here, a certain set of input variables (x) are utilized to draw output variables (y) with the relation y = a + bx.
- The k-means algorithm is used for clustering in machine learning. Clustering means breaking up datasets to form clusters containing a similar type of information. This algorithm is utilized to decrease the standard deviation.
- The logistic regression algorithm is like a linear algorithm with non-linear values. It is preferred for binary classification or probability of event occurrence.
- The support vector machine is another algorithm which is like linear regression. But, it has a function for margin-based loss which can be optimized by using several methods.
- The Apriori algorithm is popular in market-based studies as it mines the datasets to find out frequent item sets. For instance, if Customer A buys item x, he will also buy item y.
- The feed-forward neural network algorithm contains multiple levels of logistic regression identifiers. This type of algorithm is self-learning or harbor learning without a teacher method.
- The conditional random fields are used for structured prediction tasks such as image segmentation. These algorithms are commonly utilized in sequences of sequence such as DNA, image, time series, etc.
- We have all heard about decision tree algorithms of machine learning. It is used for predictive analysis and statistics. It contains branches and leaves, where branches are attributes and leaves are values of attributes.
- Lastly, the naïve Bayes theorem for predictive analysis. It finds the probability of whether an event will occur based on the fact that another event has already occurred.
Machine learning is a difficult career field if you don’t know where to start. You can refer the above algorithms to kick-start your career while we’ll keep you posted on what you should learn next.
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