Every machine learning engineer should possess knowledge about these important Machine Learning algorithms.
Machine learning algorithms are classified into 4 types:
- Supervised and
- Unsupervised Learning
- Semi-supervised Learning
- Reinforcement Learning
However, these 4
are further classified into more types.
• Linear
Regression
• Logistic
Regression
• Decision
Tree
• Support Vector Machine Algorithm
• K Means
• Random Forest Algorithm
• Naive Bayes Algorithm
• Apriori Algorithm
• Dimensionality Reduction Algorithms
• Gradient Boosting and AdaBoosting Algorithm
Presently, nearly
all manual tasks are being automated. Machine learning algorithms are changing the definition of
manual. It is very evident that machine learning is one of the hottest trends in the tech
industry and is incredibly powerful to make predictions, and calculated
suggestions based on large amounts of data. Machine learning engineers should be thorough with the routine
algorithms to understand Machine Learning operations and execute advanced
techniques.
Here are the top
10 Machine Learning algorithms every Machine Learning Engineer should know.
• Linear
Regression: In this process, a relationship is established between
independent and dependent variables by fitting them into a line. It
demonstrates the impact on the dependent variable when the independent variable
has changed in any way. An example of a linear regression algorithm is its
usage for risk assessment in the insurance domain.
• Logistic
Regression: Logistic regression is used to discrete values from a set
of independent variables. It helps to predict the probability of an event by
fitting data to a logit function. Including interaction terms, eliminating
unnecessary features, and regularizing techniques could help improve the
performance of the logistic regression algorithm.
• Decision
Tree: A decision tree is one of the most popular algorithms used
today. It is a supervised learning algorithm that is used for classifying
problems. It works well for classifying both categorical and continuous
dependent variables.
• Support
Vector Machine Algorithm: It is used for classification or regression
problems. The data is divided into different classes by finding a particular
line that segregates the data set into multiple classes. The support vector
algorithm tries to find the hyperplane that maximizes the distance between
these classes so that the classification of data is more accurate.
• K Means:
It is an unsupervised learning algorithm that solves clustering problems. Data
sets are classified into several clusters in such a way that all the data
points within a cluster are homogenous and heterogenous than other clusters.
This algorithm uses a K number of clusters to operate on a given data set.
• Random
Forest Algorithm: A collection of decision tree algorithms is called
random forest. Each tree is classified individually to identify a new object
based on its attributes. Each forest chooses the classification having the most
votes.
• Naive
Bayes Algorithm: This Machine Learning algorithm is based on the Bayes Theorem of
Probability, and on applying it yields strong independent assumptions between
the features. This model is easy to build and is useful for large datasets. It
is simple to use and is known to outperform even highly sophisticated
classification methods.
• Apriori
Algorithm: This Machine Learning algorithm generates association rules
using the IF_THEN format. With the help of these association rules, the
algorithm determines how strongly or weakly two objects are connected. It is an
iterative process for finding the frequent datasets from the large datasets.
• Dimensionality
Reduction Algorithms: Vast amounts of data are being stored and analyzed by
companies, government agencies, and research organizations. Dimensionality
reduction algorithms like decision trees, factor analysis, and random forest
can help find relevant details efficiently.
• Gradient
Boosting and AdaBoosting Algorithm: These are boosting algorithms that are
used when massive loads of data have to be handled to make high accuracy
predictions. It combines multiple weak and average predictors to build a strong
data predictor.
If you want to build a career in machine learning, start right away. The field is increasing, and the sooner you understand the scope of machine learning tools, the sooner you'll be able to provide solutions to complex work problems.
Source : datamation.com
