Machine learning is the art to train machines to improve their performance. Today almost all manual tasks have been automated and the definition of manual learning is changing. There are several Machine Learning algorithms and they can help to play several games, perform useful tasks, and make the machines smarter.

This is the era of constant technological changes that can be noticed. Computing has advanced over the last few years. New technologies are being launched every year. Computing tools and technologies have been advancing a lot. Data scientists have launched many tools and techniques to handle data and analyze it.

Machine Learning and Artificial Intelligence are the most innovative and rising technologies. There are many Deep Learning algorithms and Frameworks associated with Machine Learning and here in this blog, we are going to discuss some of the popular Machine Learning algorithms.

**Learning Machine Learning or ML/AI**

Machine Learning emerges from Artificial Intelligence to enable the machines so that they can learn something new with the help of experience too without programming them. By feeding some good quality data and then training the machines by using good training data sets various machine models can be prepared.

Here to train the machines several algorithms are also used. The choice of algorithms depends on various factors and the task that we need to perform. Any technical or non-technical person can learn Machine Learning, by following the proper road map and steps and they are listed below

**1. Understand the Prerequisites**: There are certain prerequisites for learning ML and they are like mathematical concepts such as Calculus, Statistics, Linear Algebra just a basic understanding of these subjects may be sufficient for Machine Learning. However, knowledge of Python will also be advantageous for the course.

**2. Learn Several ML Concepts:** Machine learning involves several concepts and knowledge of these concepts can make the learning path convenient. Some concepts like Models, Features, targets, training, and prediction may be helpful.

There are several types of Machine Learning like supervised learning, unsupervised learning, reinforcement, and semi-supervised learning.

**3. Participate in Competitions or Earn Badges:** Several courses are offered by the training providers. Not only this, even the learners can participate in several competitions and earn badges. These competitions can make learners more competitive. Some competitions include Kaggle competitions, Machine Learning, Hackathons, and many others.

**Here are the Top 10 Machine Learning Algorithms List updated for 2021**

**1.) Linear Regression**

You can understand this algorithm by supposing the arrangement of wooden logs in increasing order of weight. Suppose you have to guess the weight of each log just by looking at it, then you can do this by arranging them and visualizing its height and width, this is what we discuss and follow in Regression analysis.

In this analysis, the relationship is established between dependent and independent variables. The variables are fitted to a line. The algorithm uses a regression line that is represented by the following equation:

Y=a*X + b

Here in this equation, the variable significance is defined below:

Y – Dependent Variable

X- Independent Variable

a – Slope

b – Intercept

Here the coefficients of the variables that are represented by a, and b are being derived by minimizing the sum. The sum is calculated by calculating the difference of distance between data points and the regression line. The sum is then squared to find the values of a, and b coefficients.

**2.) Logistic Regression**

If you want to estimate the discrete values then you can use Logistic Regression. It is used to predict a binary outcome based on a set of independent variables. The analysis is used when the user has to predict the probability of an event and data is usually being fitted to a log function that is known as the logit function. Following functions are used to improve logistic regression models:

- Regularizing technique
- Using a non-linear model
- By including interaction terms
- By eliminating features

**3.) Support Vector Machine (SVM)**

In this method or algorithm data classification is done by plotting raw data as points in an n-dimensional space. Here the value of ‘n’ is chosen as per the number of features that are taken into consideration. The value of these features is tied to a particular coordinate that makes it easy to classify the data. Here lines can be used for data classification and graph plotting as well.

SVM algorithms use a technique, known as ‘kernel’. This function converts the low dimensional input to high dimensional output. It is helpful for the conversion of not separable problems to separable problems. ** **

**4.)**** ****Decision Tree**

Decision Trees can be used to resolve the problems through a graphical approach and help in making the decisions. Decision trees are basically the inverted tree with root at the top moreover, in such trees the tree branches spread underneath. Decision tree algorithms come under supervised learning algorithms under Machine Learning. They are used for both Classification and Regression Tree (CART) algorithms. ** **

**5.) K – Nearest Neighbor**

The algorithm can be applied to both regression problems and classifications. The algorithm is mainly used to solve classification problems. All available cases are stored in this algorithm and the new case is classified through the algorithm. This is done by taking the majority of the votes of its k neighbors. The new case belongs to that class with which it resembles the most. The distance function is used to measure this distance.

KNN can be related to real-life for example if you want to know about a person, then you can ask his friends to know more about him. There are certain drawbacks or considerable points before selecting KNN that are listed below:

- KNN is computationally expensive
- Data may have to be pre-processed
- The variables must be normalized because high range variables can bias the algorithm

** 6) Naïve Bayes**

As per the Naïve Bayes theorem, every feature of any class is unrelated to other features. Even if all features are related to each other, but still Naïve Bayes classifier would consider all of them as an independent feature. In this way, the probability of any outcome is measured.

This method is known as Naïve Bayes. The method is quite simple and sometimes even outperforms, but is a highly sophisticated method.

** 7.) K-Means**

It is UnSupervised learning that is used to solve clustering problems. The data sets are classified into homogeneous or heterogeneous data points clusters. The data points of any cluster (say k) may be either similar or different. The clusters of K-Means are formed by the following methods:

- The K-means algorithm picks k number of points that are known as centroids for every cluster
- The cluster is formed with each data point that is close or near to the centroid
- Based on the existing cluster members the new centroid is then calculated or created
- The distance between each data point and the new centroid is then calculated or determined

This whole process keeps on repeating until the centroid does not get shifted.** **

**8) Dimensionality Reduction Algorithm**

Today vast or a huge amount of data is being visualized and analyzed by business organizations, research organizations, and government agencies. As a data scientist, everyone knows that raw data may contain a lot of information, but the challenge for them is to identify the significant pattern and the variables. There are many dimension reduction algorithms like Factor Analysis, Missing Values, Principal Component Analysis, and Missing Value Ratio algorithms.

** 9.) Random Forest**

Collective decision trees are called Random Forest. The new object is classified as per its object, attributes, and the “vote”, given to the class. The classification or forest with the maximum number of votes is chosen. The trees are formed by the following steps:

- If each sample has N cases or N number of training sets then a sample is picked or chosen randomly. The training set thus is chosen works as a sample training set for the growing tree.
- If there are M input variables, then a random number say m<<M is chosen and the node is then split as per the value of m. This value of m remains constant
- Every tree is grown up to its maximum growth without pruning

**10) Gradient Boosting and Adaboost**

When massive loads of data have to be handled then this boosting algorithm is being used. The algorithm makes a high prediction that is too accurate. Boosting learning algorithms combine predictive power to improve robustness. Multiple weights or average predictors are combined to form a strong predictor. The algorithms can be implemented by using R and Python.

**Endnotes**

If you want to learn Machine Learning and make your career in this profession, then you can start learning it either right from your home in offline mode or with the help of any expert professional through online mode. This is an innovative and rapidly increasing field. Sooner you will understand the concepts and complex problems more easily you will become able to solve your problems. However, if you want to boost your career as an ML professional, then learning math and the above-mentioned algorithm can help you in shaping your career as the best professional.

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