In the fastevolving field of machine learning, understanding the right algorithms is crucial for any aspiring engineer or data scientist. This article highlights the top 10 machine learning algorithms that every machine learning engineer should be familiar with to build effective models and derive meaningful insights from data.
Top 10 Machine Learning Algorithms?
Below is the list of the top 10 commonly used Machine Learning Algorithms:
 Linear regression
 Logistic regression
 Decision tree
 SVM algorithm
 Naive Bayes algorithm
 KNN algorithm
 Kmeans
 Random forest algorithm
 Dimensionality reduction algorithms
 Gradient boosting algorithm and AdaBoosting algorithm
Types of Machine Learning Algorithms
Read More: How to Become a Machine Learning Engineer?
List of Popular Machine Learning Algorithms
1. Linear Regression
To understand the working functionality of Linear Regression, imagine how you would arrange random logs of wood in increasing order of their weight. There is a catch; however – you cannot weigh each log. You have to guess its weight just by looking at the height and girth of the log (visual analysis) and arranging them using a combination of these visible parameters. This is what linear regression in machine learning is like.
In this process, a relationship is established between independent and dependent variables by fitting them to a line. This line is known as the regression line and is represented by a linear equation Y= a *X + b.
In this equation:
 Y – Dependent Variable
 a – Slope
 X – Independent variable
 b – Intercept
The coefficients a & b are derived by minimizing the sum of the squared difference of distance between data points and the regression line.
2. Logistic Regression
Logistic Regression is used to estimate discrete values (usually binary values like 0/1) from a set of independent variables. It helps predict the probability of an event by fitting data to a logit function. It is also called logit regression.
These methods listed below are often used to help improve logistic regression models:
 include interaction terms
 eliminate features
 regularize techniques
 use a nonlinear model
3. Decision Tree
Decision Tree algorithm in machine learning is one of the most popular algorithm in use today; this is a supervised learning algorithm that is used for classifying problems. It works well in classifying both categorical and continuous dependent variables. This algorithm divides the population into two or more homogeneous sets based on the most significant attributes/ independent variables.
4. SVM (Support Vector Machine) Algorithm
SVM algorithm is a method of a classification algorithm in which you plot raw data as points in an ndimensional space (where n is the number of features you have). The value of each feature is then tied to a particular coordinate, making it easy to classify the data. Lines called classifiers can be used to split the data and plot them on a graph.
5. Naive Bayes Algorithm
A Naive Bayes classifier assumes that the presence of a particular feature in a class is unrelated to the presence of any other feature.
Even if these features are related to each other, a Naive Bayes classifier would consider all of these properties independently when calculating the probability of a particular outcome.
A Naive Bayesian model is easy to build and useful for massive datasets. It’s simple and is known to outperform even highly sophisticated classification methods.
6. KNN (K Nearest Neighbors) Algorithm
This algorithm can be applied to both classification and regression problems. Apparently, within the Data Science industry, it’s more widely used to solve classification problems. It’s a simple algorithm that stores all available cases and classifies any new cases by taking a majority vote of its k neighbors. The case is then assigned to the class with which it has the most in common. A distance function performs this measurement.
KNN can be easily understood by comparing it to real life. For example, if you want information about a person, it makes sense to talk to his or her friends and colleagues!
Things to consider before selecting K Nearest Neighbours Algorithm:
 KNN is computationally expensive
 Variables should be normalized, or else higher range variables can bias the algorithm
 Data still needs to be preprocessed.
7. KMeans
It is an unsupervised learning algorithm that solves clustering problems. Data sets are classified into a particular number of clusters (let’s call that number K) in such a way that all the data points within a cluster are homogenous and heterogeneous from the data in other clusters.
How Kmeans forms clusters:
 The Kmeans algorithm picks k number of points, called centroids, for each cluster.
 Each data point forms a cluster with the closest centroids, i.e., K clusters.
 It now creates new centroids based on the existing cluster members.
 With these new centroids, the closest distance for each data point is determined. This process is repeated until the centroids do not change.
8. Random Forest Algorithm
A collective of decision trees is called a Random Forest. To classify a new object based on its attributes, each tree is classified, and the tree “votes” for that class. The forest chooses the classification having the most votes (over all the trees in the forest).
Each tree is planted & grown as follows:
 If the number of cases in the training set is N, then a sample of N cases is taken at random. This sample will be the training set for growing the tree.
 If there are M input variables, a number m<
 Each tree is grown to the most substantial extent possible. There is no pruning.
9. Dimensionality Reduction Algorithms
In today’s world, vast amounts of data are being stored and analyzed by corporates, government agencies, and research organizations. As a data scientist, you know that this raw data contains a lot of information – the challenge is to identify significant patterns and variables.
Dimensionality reduction algorithms like Decision Tree, Factor Analysis, Missing Value Ratio, and Random Forest can help you find relevant details.
10. Gradient Boosting Algorithm and AdaBoosting Algorithm
Gradient Boosting Algorithm and AdaBoosting Algorithm are boosting algorithms used when massive loads of data have to be handled to make predictions with high accuracy. Boosting is an ensemble learning algorithm that combines the predictive power of several base estimators to improve robustness.
In short, it combines multiple weak or average predictors to build a strong predictor. These boosting algorithms always work well in data science competitions like Kaggle, AV Hackathon, CrowdAnalytix. These are the most preferred machine learning algorithms today. Use them, along with Python and R Codes, to achieve accurate outcomes.
You can also watch our indemand video on top Machine Learning Algorithms.
Supervised vs. Unsupervised vs. Reinforcement Learning Algorithms
Let’s look at how supervised, unsupervised, and reinforcement learning really stack up across a few key areas.
In supervised learning, you have labeled data at your disposal, meaning the answers are already known for each example, making it easier to train the model. Unsupervised learning, on the other hand, doesn’t come with labels, so the algorithm has to figure out patterns on its own. Reinforcement learning also skips labeled data; instead, it learns by taking actions, getting feedback through rewards or penalties, and using that feedback to keep improving.
Supervised learning has a clear goal in mind; you’re trying to predict specific outcomes using labeled data. Unsupervised learning isn’t as structured; it’s more about exploring the data to uncover hidden patterns or clusters. With reinforcement learning, the goal is all about maximizing rewards over time, adjusting actions based on past mistakes and successes to do better as it goes along.
In supervised learning, it involves giving the model numerous examples with a known result and the model is trained to achieve the results through such examples. Unsupervised learning takes the algorithm in a different role: discovering structure within the data. For example, finding clusters or associations. Reinforcement learning in its approach is relatively different, it is more fluid in that it evolves through interacting with the environment and learning as it progresses through its strategy.
Supervised learning is best suited for tasks such as outcome forecasting and pattern recognition. It involves classification and prediction. On the other hand, unsupervised learning is most useful in identifying groups within the data, detecting outliers, or reducing the dimensionality of the data. Reinforcement learning is particularly useful in areas where realtime decisions are required such as robotics, games, etc. in which performance can be enhanced through experience.
When to Use Supervised, Unsupervised, or Reinforcement Learning
Supervised learning works best when labeled data is readily available, and you need precise predictions. It’s often used in spam detection, stock price prediction, and medical diagnosis.
Unsupervised learning is great when exploring new data to find patterns or clusters, such as customer segmentation or anomaly detection.
Reinforcement learning is suitable for scenarios involving continuous learning, like training a robot to navigate or optimizing game strategies, where feedback is given over time.
Factors to Consider When Choosing a Machine Learning Algorithm
Let’s explore what to consider when making choosing a machine learning algorithm:
The first thing to look at is determining the type of data that you have. For instance, labeled datasets or those with defined outputs can be entrusted in the hands of supervised methods. On the other hand, in the case of unlabeled data, unsupervised approaches are required to locate hidden structures. In scenarios where learning is carried out through interactions, reinforcement learning seems to be a useful candidate.

Complexity of the Problem
After that, evaluate the complexity of the problem you are trying to solve. In tasks that are less complex, simpler algorithms can do the job. However, if you’re tackling a more complex issue with intricate relationships, you might want to use more advanced methods, like neural networks or ensemble techniques. Just be prepared for a bit more effort and tuning.
Another important factor is the computational power at your disposal. Some algorithms, like deep learning models, can be resourceintensive and require powerful hardware. If you’re working with limited resources, simpler algorithms like logistic regression or knearest neighbors can still deliver solid results without putting too much strain on your system.

Interpretability vs. Accuracy
Finally, think about whether you need an algorithm that’s easy to understand or one that prioritizes accuracy, even if it’s a bit of a black box. Decision trees and linear regression are generally easier to interpret, making them great for explaining to stakeholders. In contrast, more complex models like neural networks might give you better accuracy but can be harder to explain.
Conclusion
IMastering these Machine Learning Algorithms are a great way to build a career in machine learning. The field is proliferating, and the sooner you understand the scope of machine learning tools, the sooner you’ll be able to provide solutions to complex work problems.
However, if you are experienced in the field and want to boost your career, you can takeup the Post Graduate Program in AI and Machine Learning in partnership with Purdue University collaborated with IBM. This program gives you an indepth knowledge of Python, Deep Learning algorithm with the Tensor flow, Natural Language Processing, Speech Recognition, Computer Vision, and Reinforcement Learning. Explore and enroll today!
FAQs
1. What is an algorithm in machine learning?
Algorithms in machine learning are mathematical procedures and techniques that allow computers to learn from data, identify patterns, make predictions, or perform tasks without explicit programming. These algorithms can be categorized into various types, such as supervised learning, unsupervised learning, reinforcement learning, and more.
2. What are the three types of machine learning algorithms?
The three basic machine learning algorithms are:
 Supervised Learning: Algorithms learn from labeled data to make predictions or classify new data.
 Unsupervised Learning: Algorithms analyze unlabeled data to discover patterns, group similar data, or reduce dimensions.
 Reinforcement Learning: Here, algorithms learn through trial and error by interacting with an environment to maximize rewards.
3. What are the 4 machine learning algorithm?
The 4 machine learning algorithms are:
 Supervised Algorithm
 Unsupervised Algorithm
 SemiSupervised Algorithm
 Reinforcement Algorithm
4. Which ML algorithm is best for prediction?
The best ML algorithm for prediction depends on variety of factors such as the nature of the problem, the type of data, and the specific requirements. Popular algorithms for prediction tasks include Support Vector Machines, Random Forests, and Gradient Boosting methods. However, the choice of an algorithm should be based on experimentation and evaluation of the specific problem and dataset at hand.
5. What is the difference between supervised and unsupervised learning algorithms?
The primary difference between supervised and unsupervised learning lies in the type of data used for training. Supervised learning algorithms use labeled data, where the target output is known, to learn patterns and make predictions. Unsupervised learning algorithms work with unlabeled data, relying on intrinsic patterns and relationships to group data points or discover hidden structures.
6. Is CNN a machine learning algorithm?
A convolutional neural network (CNN or convnet) is a type of artificial neural network used for various tasks, especially with images and videos. It’s a part of machine learning and works with different kinds of data.
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