Monday, November 8, 2021

Top 5 Machine Learning Algorithms for Data Science and ML Interviews

Hello guys, you may know that Machine Learning and Artificial Intelligence have become more and more important in this increasingly digital world. They are now providing a competitive edge to businesses like NetFlix's Movie recommendations. If you have just started in this field and are looking for what to learn, then I will share 5 essential Machine learning algorithms you can learn as a beginner.  These necessary algorithms form the basis of most common Machine learning projects. Knowing them well will help you understand the project and model quickly and change them as per your need.

In simple words, machine learning is the science or field of making the computer learn like a human by feeding it with the data without being programmed and separated into two categories. The first one is classification problems. The machine needs to classify between two objects or more like between human and animal. The second is regression problems, in which the machine needs to produce an output based on previous data.

Machine learning is one of the most used fields in Artificial Intelligence today like face recognition Softwares, self-driven cars, voice recognition, forecasting, and also when you are applying filters when you are using Snapchat. Learning these skills will make you one of the highest-paid jobs, with an average annual salary of $141,205 in the USA inside a business worth $3.9 trillion in 2022.

This article will see 5 of the most used machine learning algorithms that make you understand how AI and complicated IT technologies work. Also, you can create such an artificial intelligence software with these algorithms and maybe use it in your daily life.





Top 5 Machine Learning Algorithms for Data Science Interview

While Machine Learning is a vast field and there are many Algorithms, these are some of the essential algorithms that play a crucial role in many Machine learning projects and during job interviews. Even having a basic knowledge of these Algorithms can help you greatly in working on Machine learning projects and understanding them.

1. Decision Tree Algorithm

The decision tree is a type of classification algorithm used in supervised learning techniques, but it can be used for classification and regression problems.

For the classification problems, it is an answer like "True" or "False" where it will determine the solution by a bunch of logical if-then statements such as choosing the type of five cars based on some features and for the regression problems is used if you want to get the answer for a numeric problem like determine the price of home-based on some features.

You can further check Decision Trees, Random Forest, AdaBoost, and XGBoost in Python course by Start Tech Academy on Udemy for a solid understanding of the Decision Tree Algorithm. I highly recommend this course.

Best course to learn Decision Tree Algorithm




2. Support Vector Method Algorithm

Support vector machine is an algorithm for the advanced cases of classification problems like classifying between two types of dogs that are similar in color, size, body, and so on.

It separates your data into 2 classes and tries to find the best line that fits your model called a hyperplane. Hence, the two classes have some space between them and that line, and that space is called margin, and by doing so, it can classify some big classification problems like determining the gender of someone on a picture.

 Also, it can be used for regression problems as well. You can further check the Support Vector Machines in Python course on Udemy to learn more about this algorithm in Machine Learning and Artificial Intelligence space.

Best Course to learn Support Vector Method Algorithm




3. Logistic Regression

It is an algorithm for regression problems and a way of determining the relationship between two variables, and one of them is dependent. The other one is the independent variable and is like a predictive model.

The dependant variable is the variable you want to predict, and the independent variable is the variable is you give to the algorithm to learn. The good thing in this algorithm is considered a robust statistical technique for predicting events that include one or more independent variables.

If you want to learn more, you can also check out the Logistic Regression in Python course by Start Tech Academy on Udemy. It's another excellent course from Start Tech after their Decision tree one, and you can do Predictive Modeling using Python after going through this course.

Best Logistic Regression Course




4. K-means Clustering Algorithm

K-means is a type of clustering algorithm that uses unsupervised learning techniques to learn and solve the problem, and only it works with numeric data.

This algorithm can solve classification problems without the need for previous data to learn or train. It sees the difference and similarity of many lats say pictures and try to group them based on those features.

This means that any categorical variable should be converted to a numeric variable before this algorithm can be applied. You can further see Cluster Analysis and Unsupervised Machine Learning in Python course by Lazy Programmer on Udemy to learn about K-means and other clustering algorithms.

Best course to learn K-means Clustering Algorithm




5. Naive Bayesian classification

Naive Bayesian is a classification algorithm that is good for a large dataset. It is the best algorithm that uses statistical calculation in its background, like probabilities and advanced ones, to solve classification problems and imperative analysis problems.

This algorithm is used in many daily applications such as email spam detection, face recognition, pattern recognition and is considered as one of the easiest and fastest algorithms to predict the class of test data set.

You can also check out Bayesian Machine Learning in Python: A/B Testing to learn more about the practical usage of Bayesian methods in the real world.

best course to learn Naive Bayesian Classification


Conclusion

That's all bout essential machine learning algorithms a data scientist should learn.  These are also very important from an interview perspective, and you may be asked to explain and implement these algorithms during any Data Science and Machine Learning interviews. 

We have discussed many machine learning algorithms in this article. They are the most commonly used among machine learning engineers to create an app to facilitate their lives or for complex Softwares.

Of course, there are other algorithms that we didn't discuss here but they are the main ones and you can start learning them if you are considering a career as a machine learning engineer, but these essential Machine learning algorithms are great to start with.

Other Machine Learning and Artificial Intelligence Articles may like

Thanks for reading this article so far. If you find these essential Machine learning Algorithms useful then please share it with your friends and colleagues. If you have any questions or feedback, then please drop a note.

P. S. - If you are determined to make a career in the Machine Learning field and looking for the best online courses to level up your ski, I highly recommend you check out the Machine Learning A-Z - Hands-On Python course by Kirill Eremenko on Udemy. It's one of the most popular and comprehensive course to learn Machine Learning online.

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