TOP 10 MACHINE LEARNING ALGORITHMS: THE KEY TO SUCCESSFUL ML CAREER IN 2021

TOP 10 MACHINE LEARNING ALGORITHMS: THE KEY TO SUCCESSFUL ML CAREER IN 2021

No one can beat you if you master these 10 Machine Learning algorithms: Know What Those Are

Machine learning engineers are in high demand. Several statistical insights show this demand is going to experience a rapid surge in the upcoming 3 years. So, it’s the best time to prepare yourself for a successful career transition to the field of artificial intelligence and machine learning.

But the most significant barrier to a smooth career switch as a machine learning engineer is the lack of proficiency in ML algorithms. Is an algorithm really hard to learn?

Not at all. Majority of aspirants are afraid of algorithms as it belongs to advanced statistics. But in reality, algorithms are very easy to learn once you understand the basics of the same. What makes the hurdle, in this case, is the appropriate applications of your algorithm knowledge as per situational needs. Once again, I must say that it also becomes a buttery smooth task if you are clear about the primary mathematical equation object of the algorithms.

In this blog, I am going to discuss the ten most widely used machine learning algorithms. Mastering these algorithms will make you eligible to solve any data science quarry or issues, even if you are not from a programming or statistical background.

All the ten algorithms I will discuss work with python programming.

Algorithm Using Linear Regression Technique

This is the most basic algorithm based on the mathematical representation of a straight line, as provided below. As of now, it’s its recognition of one of the algorithms that offer a powerful and precise interpretation of basic to moderate and sometimes for complex scenarios.

y=mx+c
Here, y =a dependent variable
x= an independent variable,
c= the intercept of the straight line
m= slope of the straight line

When to use this algorithm?

Scenario with two variables. [For multiple variables, we need to use multi-linear progression]
It offers the best solution to several supply chain queries.
Until now, it has proven a powerful algorithmic solution for demand and sales forecast, mainly in terms of sustainable price hikes.

Algorithm Using Logistic Regression Technique

Here, the name of the algorithm is a bit tricky. Although it’s termed a regression, in actuality, it doesn’t solve the regression problem. Rather, it’s more dedicated to classification responsibilities.
If you need to classify a range of features, then with a logistic regression algorithm, you can apply logic to predict the outcomes of variable combinations of features within your data set.
But yes, the combination of features has to be in linear form. Only then can logistic regression be applied.

When to use this algorithm?

Suppose you need to predict categorical outputs or draw insights from probabilities, where the response variable is the function of a second variable under consideration. For example, the insight for a smart band buying behavior of as the function of gender ‘female.’
If you come with a handful of explanatory variables, you need to classify the categorical insights. Classification of credit-card scoring behavior based on age is the best example in this case.
Binary response categories based on categorical dependent variables can also be carried out with the help of logistic regression.
This algorithm has found its most popular use in weather forecasting, insight generation for election results, upcoming product sales trends, etc.

The algorithm that Resembles with Human Neural Network

This algorithm is more connected with deep learning. It’s a bit more complex than other Ml algorithms. But, as automation and deep learning are in high demand, so at least a basic knowledge of this advanced algorithm is now required to stand tight into the competitive machine learning job market. Artificial neural network algorithms possess the capability of solving such a level of decisional complexity that requires the interference of human intelligence.

When to use this algorithm?

Solving problems that include natural language processing.
The most common use of this complex algorithm includes image and emotion processing, medical image analysis, speech recognition application like google voice assistance or amazon’s Alexa.
It can process hand-written characters.
Future use lies in automated loan applications and stock-market-related decision-making.

Algorithms using K-Means clustering Techniques

This is a self-learning (unsupervised) algorithm that looks for solutions to deal with complex clustering issues. The process that this classifier follows to handle the specified dataset is simply to partition the objects, and measures is linear. At the same time, the homogenization and differentiation of the same objects can be differentiated to other groups as well.
In case the number of clusters rises, the sum of the distances from the data points and the K-Means classifier approaches a limit, which is considered the final class, the variance decreases and ultimately identifies the class.

When to use this algorithm?

The best senior to use K-means clustering is when K has a lower value. In such cases, for an extensive dataset, traditional hierarchical clustering programs become very time-consuming. At the same time, the K-mean technique can land with the result in the lowest possible period.
This algorithm has found impressive popularities in optimizing search results of search engines like Google, Bing, and Yahoo (a mainly auto-complete mechanism).
The latest use includes optimization of Google image search results.

Random Forest Algorithm

The random forest includes a bunch or, better to say, a series of decision trees. The process steps of finding a solution through this algorithm include identifying new objects in each of the tree nodes. At the same time, each of the trees gets classified based on identified variables. The further approach of this algorithm can be compared to the voting system. The class gains the maximum number of votes and is presented as the finally identified class to be used.

When to use this algorithm?

In case the data set includes lots of interrelated variables and even for missing data sets.
This algorithm offers the best predictive results for scenarios lying with extremely threatful outcomes.
The best application of this algorithm has been found in scenarios like early detection of chronic diseases, prediction of future loan defaulters from current loan applicants of financial services, insight about social media post’s audience engagements, etc.

Decision Tree Approach

The decision tree approach can be used in the form of either classification or a regression tree. In the case of variable split-data under different classes, classification trees seem to be the best options.
In such cases, where the target variables are continuous or numerical, regression becomes the best option. However, if the data set includes erroneous data, then the decision tree might fail to provide a correct prediction. So, for the successful evaluation of data, the data set should be error-free.

When to use this algorithm?

Simply this is used either for classification or prediction of results.
This is the best-used algorithm for automated functionalities identification and implementation of treatment strategies for patients at high health risks.
This is best for working with non-linear datasets.

KNN algorithm

KNN’- this is the abbreviation of the K-Nernst Neighbour algorithm. As the name suggests, it searches for the similarities in occurrences (neighbor) almost within the data of the entire dataset. We get as the output is multiple numbers of insights based on different combinations of identified occurrences of variables.

When to use this algorithm?

This is a good option for the classification of more significant amounts of variables.
KNN is such an ML algorithm that can be used to solve various regular life seniors. And the key advantage is that in such cases, the algorithm doesn’t demand any structural changes. For example, if you want to know such buyers of your product, those are typical fields of your best friend; KNN can offer you that insight.
This works for such scenarios, where the variables under consideration are easier to normalize.

Naive Bayes algorithm

The Bayes theorem of probability generates the foundation of this widely used classification algorithm. However, in order to apply this algorithm, the first thing you need to do is consider all of the targeted variables free of internal dependencies.

When to use this algorithm?

This algorithm is the best fit for a dataset containing a moderate volume of data.
Projects for employee training purposes can consider applying this algorithm.
Nothing can be the best choice other than this algorithm in case the predictor instance has no internal dependencies.
Classified documentation in the marketing and finance domain, early prediction of several life-threatening diseases have found unbelievable benefits of this algorithm.

SVM Algorithm

Support Vector Machine (SVM) is another machine learning algorithm tailored to the classification of targeted variables based on vector machines. This classifier may be used for classification or as well as it is being trained for classifications of new data. SVM is a comparatively new algorithm in terms of its machine learning application constantly moving forward.
To work with SVM, you can frame all the raw data within a space having n dimension (where n implies the number of features in your hand). As features are distinguished by their specific coordinates, it is simple to place each one in a particular data category. Next, you can use classifiers to separate the data into groups and draw a graph of the groups.

When to use this algorithm?

SVM is mostly used for comparative analytics.
Since the last 2 years, this algorithm has earned massive popularity in the sales and marketing domain for comparative studies of the stock market, market risk assessments, etc.

Apriori Algorithm

This is the most popular data mining algorithm. This works with the goal of identifying the points of association among available data sets by generating requisite association rules.

When to use this algorithm?

Apriori Algorithm works best in case we want to find out the dependent association of two variables. For example, if a variable X can see through the dark, the second variable Y can also see throughout the dark.
The best real-life application of this algorithm has been made in the Google search engine’s autocomplete feature.
Walmart has shown the most beneficial use of this algorithm for fostering their beer sales. Wal-Mart data mining using Apriori Algorithm has revealed the following unbelievable fact (Source: Programmer Sought).

So these are the 10 key machine learning algorithms, learning which you can cover up all the prospect full aspects of core machine learning and basic deep learning problems. Later, you will learn about more algorithms as you start working on ML projects.

But as of now, your concern might be how to learn these algorithms?

For intensive learning along with creditable project experience, you can join our IBM-certified courses in AI and ML. This course is specially designed for working professionals with 4 plus years of working experience in the technical domain. In case you do not hold any technical background still, we have other courses for you that will help you learn machine learning from scratch. To know more about such courses, visit learnbay.co.

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