Top 10 Machine Learning Algorithms in 2021

The definition of manual is changing in a world where almost all manual tasks can be automated. Machine learning algorithms can be used to help computers learn how to play chess and perform surgeries. They also make them more intelligent and more personalized.

We live in an age of technological advancement. Looking at how computing has evolved over the years, it is possible to predict what the future holds.

The main feature of this revolution is the way computing tools and techniques are now accessible to everyone. Data scientists have developed sophisticated data-crunching tools by seamlessly executing advanced techniques over the past five years. These results are amazing. To learn these Machine Learning skills and become an expert, you must register for the AI and ML courses. You will learn the Machine Learning and other topics such as Python, Deep Learnimg, NLP, etc.

There are two types of machine learning algorithms:

• Supervised Learning
• Unsupervised Learning

These two can be further divided into other types.

Here's a list of the Top 10 Machine Learning (ML), Algorithms that are most commonly used:

• Linear regression
• Logistic regression
• Decision tree
• SVM algorithm
• Naive Bayes algorithm
• KNN algorithm
• K-means
• Random forest algorithm
• Dimensionality reduction algorithms
• Gradient boosting algorithm and AdaBoosting algorithm

These Essential Algorithms can help you improve your Machine Learning skills.

These techniques can be used to create functional Machine Learning projects if you are a data scientist or an enthusiast for machine learning.

There are three main types of Machine Learning algorithms that are most popular

• supervised learning
• reinforcement learning
• unsupervised learning

Below are the 10 most common Machine Learning Algorithms:

Machine Learning Algorithms

1. Linear Regression

Imagine how random logs of lumber would be arranged in order to increase their weight. This will help you understand the functionality of the algorithm. However, you can't weigh each log. It is possible to determine the weight of each log by simply looking at its height and girth (visual analysis). Then, you can arrange them using a combination these parameters. This is how linear regression works in machine learning.

This is where independent and dependent variables are connected by fitting them to one line. This line, also known as the regression line, is represented by a linear equation: Y=a *X + B.

This equation:

• Y - Dependent Variable
• A - Slope
• X - Independent variable
• b - Intercept

The squared difference between the data points and the regression line is minimized to get the coefficients a and b.

2. Logistic Regression

Logistic Regression is used to calculate discrete values (usually binary numbers like 0/1) using a set independent variables. Logistic Regression is a method of predicting the likelihood of an event using data that has been fitted to a logit function. Logit regression is another name for it.

These methods are used often to improve logistic regression models.

• Include interaction terms
• Eliminate features
• regularize techniques
• use a non-linear model

3. Decision Tree

The Decision Tree algorithm is an algorithm used in machine learning. This is a supervised algorithm that is used to classify problems. It is capable of classifying both continuous dependent and categorical variables. This algorithm divides the population into multiple homogeneous sets, based on the most important attributes/independent variables.

4. SVM (Support vector machine) Algorithm

The SVM algorithm is a classification method that plots raw data in an n-dimensional space. (where n is how many features you have). Each feature's value is tied to a specific coordinate to make it easier to classify the data. You can use classifiers lines to divide the data and plot them on graphs.

5. Naive Bayes Algorithm

A Naive Bayes classifier assumes the absence of a particular feature within a class is independent of the presence of any other features.

These features may be related, but a Naive Bayes classification ifier would consider all of them independently when calculating the probability of a particular outcome.

It is simple to create a Naive Bayesian model, and it can be used for large datasets. It is simple to use and outperforms even the most sophisticated classification methods.

6. KNN (K- Nearest Neighbors) Algorithm

This algorithm can be used to solve both regression and classification problems. It is more commonly used in the Data Science industry to solve classification problems. It is a simple algorithm that stores all cases and then classifies new cases using a majority vote from its k neighbor. The case is assigned to the class that it shares the most similarities. This measurement is done by a distance function.

KNN can be easily understood if it is compared to real life. It makes sense, for example, to ask your friends and colleagues about information about someone.

Before selecting the K Nearest Neighbor Algorithm, there are some things to consider:

• KNN is computationally costly
• Normalizing variables is important to avoid biasing the algorithm if they are in a higher range.
• Pre-processing of data is still necessary.

7. K-Means

This algorithm solves clustering problems by using unsupervised learning. Data sets are divided into a number of clusters (let's just call it K). Each cluster contains data points that are both homogenous and diverse from other clusters.

• How K-means creates clusters
• K-means algorithms picks k points, or centroids, to determine the clusters.
• Each data point is a cluster that contains the nearest centroids (i.e. K clusters).
• It creates new centroids from the existing members of the cluster.
• These new centroids determine the distance between each data point and its closest neighbor. This is repeated until the centroids don't change.

8. Random Forest Algorithm

Random forests are a group of decision trees. Each tree is assigned a classification and each tree votes for the appropriate class.

Each tree is planted and grown in the following:

• If N is the number of cases in a training set, then a random sample of N cases will be taken. This will be the sample for growing the tree.
• If M input variables are available, the number m
• Every tree is grown to its maximum potential. No pruning is necessary.

9. Dimensionality Reduction Algorithms

Today's world is filled with vast amounts of data that are stored and analyzed daily by corporations, government agencies and research organizations. Data scientists know that raw data can contain a lot of information. The challenge is to identify significant patterns and variables.

You can find the relevant details using dimension reduction algorithms such as Decision Tree, Factor Analysis and Missing Value Ratio.

10. Gradient Boosting Algorithm, AdaBoosting Algorithm

These algorithms are getting used to boost predictions that require high accuracy. It is also an algorithm that uses the combined predictive power of multiple base estimators to increase robustness.

It combines several weak or average predictors into one strong predictor. These boosting algorithms are always successful in data science competitions such as Kaggle, AV Hackathon, and CrowdAnalytix. These are the best machine learning algorithms available today. These algorithms can be used in conjunction with R Codes and Python to produce accurate results.