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AI and ML

This article was only an introduction of these machine learning algorithms. If you want to know more, check out  our online Artificial Intelligence & Machine Learning Course contains the perfect mix of theory, case studies, and extensive hands-on assignments to even turn a beginner into a pro by the end. Our ML and artificial intelligence certification courses are perfect for students and working professionals to get mentored directly from industry experts, build your practical knowledge, receive complete career coaching, be a certified AI and ML Engineer.

Top 10 Machine Learning Algorithms You should Know in 2021

Living in an era of speedy technological development isn’t easy. Especially, when you are interested in Machine Learning!

New Machine Learning Algorithms are coming up everyday with an unmatchable pace to get a hold of them! This article will help you grasp at least some of these algorithms being commonly used in the data science community. Data Scientists have been enhancing the data-crunching machines everyday to build a sophisticatedly advanced technology.

Here we are listing top 10 Machine learning algorithms for you to learn in 2021 -

1. Linear Regression

This is a fundamental algorithm, used to model relationships between a dependent variable and one or more independent 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

2. Logistic Regression

This type of regression is very similar to linear regression but this one in particular is used to model the probability of a discrete number of outcomes, which is typically two - usually binary values like 0/1 from a set of independent variables. It calculates the probability of an event by fitting data to a logit function. This may sound complex but it only has one extra step as compared to linear regression!

 3. Naive Bayes

This algorithm is a classifier. It assumes the presence of a particular feature in a class which is unrelated to the presence of any other feature.  It may seem like a daunting algorithm because it necessitates preliminary mathematical knowledge in conditional probability and Bayes Theorem, but it’s extremely simple to use.

4.KNN Algorithm

KNN Algorithms can be applied to both - classification and regression problems. This algorithm stores all the available cases and classifies any new cases by taking a majority vote of its k neighbours. Then, the case is transferred to the class with which it has the most in common.

5. Dimensionality Reduction Algorithm

This algorithm like Decision Tree, Missing Value Ratio, Factor Analysis, and Random Forest can help you find relevant details.

6. Random Forest Algorithm

Random forests Algorithms are an ensembles learning technique that builds off of decision trees. It generally involved creating multiple decision trees using bootstrapped datasets of the original data. It randomly selects a subset of variables at each step of the decision tree. Each tree is classified and the tree “votes” for that class. 

  7. SVM Algorithm

SVM stands for Support Vector Machine. In this algorithm, we plot raw data as points in an n-dimensional space (n = no. Of features you have). Then the value of each feature is tied to a particular coordinate, making it extremely easy to classify the data provided.

8.  Decision Tree

This algorithm is a supervised learning algorithm which is used to classify problems. While using this algorithm, we split the population into two or more homogenous sets based on the most significant attributes or independent variables.

9. Gradient Boosting Algorithm

This algorithm is used as a boosting algorithm, which is used when massive data loads have to be handled to make predictions with high accuracy rates.

10. AdaBoost

AdaBoost also known as Adaptive Boost is an ensemble algorithm that leverages bagging and boosting methods and developed an enhanced predictor. The predictions are taken from the decision trees.

[boardinfinty]

AI and ML

This article was only an introduction of these machine learning algorithms. If you want to know more, check out  our online Artificial Intelligence & Machine Learning Course contains the perfect mix of theory, case studies, and extensive hands-on assignments to even turn a beginner into a pro by the end. Our ML and artificial intelligence certification courses are perfect for students and working professionals to get mentored directly from industry experts, build your practical knowledge, receive complete career coaching, be a certified AI and ML Engineer.

Top 10 Machine Learning Algorithms You should Know in 2021

 Living in an era of speedy technological development isn't easy. Especially, when you are interested in Machine Learning!

New Machine Learning Algorithms are coming up everyday with an unmatchable pace to get a hold of them! This article will help you grasp at least some of these algorithms being commonly used in the data science community. Data Scientists have been enhancing the data-crunching machines everyday to build a sophisticatedly advanced technology.

 Here we are listing top 10 Machine learning algorithms for you to learn in 2021 -

 1. Linear Regression

 This is a fundamental algorithm, used to model relationships between a dependent variable and one or more independent 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

 2. Logistic Regression

 This type of regression is very similar to linear regression but this one in particular is used to model the probability of a discrete number of outcomes, which is typically two - usually binary values like 0/1 from a set of independent variables. It calculates the probability of an event by fitting data to a logit function. This may sound complex but it only has one extra step as compared to linear regression!

   3. Naive Bayes

 This algorithm is a classifier. It assumes the presence of a particular feature in a class which is unrelated to the presence of any other feature.  It may seem like a daunting algorithm because it necessitates preliminary mathematical knowledge in conditional probability and Bayes Theorem, but it's extremely simple to use.

 4.KNN Algorithm

 KNN Algorithms can be applied to both - classification and regression problems. This algorithm stores all the available cases and classifies any new cases by taking a majority vote of its k neighbours. Then, the case is transferred to the class with which it has the most in common.

 5. Dimensionality Reduction Algorithm

 This algorithm like Decision Tree, Missing Value Ratio, Factor Analysis, and Random Forest can help you find relevant details.

 6. Random Forest Algorithm

 Random forests Algorithms are an ensembles learning technique that builds off of decision trees. It generally involved creating multiple decision trees using bootstrapped datasets of the original data. It randomly selects a subset of variables at each step of the decision tree. Each tree is classified and the tree "votes" for that class. 

    7. SVM Algorithm

 SVM stands for Support Vector Machine. In this algorithm, we plot raw data as points in an n-dimensional space (n = no. Of features you have). Then the value of each feature is tied to a particular coordinate, making it extremely easy to classify the data provided.

 8.  Decision Tree

 This algorithm is a supervised learning algorithm which is used to classify problems. While using this algorithm, we split the population into two or more homogenous sets based on the most significant attributes or independent variables.

 9. Gradient Boosting Algorithm

 This algorithm is used as a boosting algorithm, which is used when massive data loads have to be handled to make predictions with high accuracy rates.

 10. AdaBoost

 AdaBoost also known as Adaptive Boost is an ensemble algorithm that leverages bagging and boosting methods and developed an enhanced predictor. The predictions are taken from the decision trees.

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5 Most Common HR Interview Questions & Answers for Data Scientists

You cleared the technical round for a company and now it’s time for the  interview… Your are probably buzzing with questions regarding your interview.

Every company conducts an round of interviews to judge your capability, attitude, personality, strengths, weaknesses, intent, etc. It’s conducted after the technical round and has all sorts of questions

We’ve made easier for you..

Here’s a list of questions that are most commonly asked for  data scientist jobs

1) Why do you want to be a Data Scientist?

This is purely to understand your motivation and reason to work in this particular company. 

HR also wants to know your intent behind joining the specific company i.e. nobody wants people who will work for 6 months and then leave. Everyone is  looking for long term employees because the whole hiring process is very tedious and consumes a lot of time and energy.

The answer you give should reflect your love for Data Science and the inspiration this organization gives you to pursue your love.

 You can say something like, “I have a keen interest in data mining and analysis,  also I admire the company's technological capabilities. I look forward to combining both and delivering brilliant performance.”

A good tip that I can give you is that, do as much research as you can about the company. This will help you include specific things about the company in your answer, in turn, creating a great first impression with them.

2)  Do you have a prior experience as Data Scientist ?

Now, depending on if you have relevant experience or not, you need to answer this question skillfully. Your soft skills are being tested here.

Because, being a data scientist does not only mean having sound technical skills but rather having enough skills to be able to communicate their findings in a way that the main decision-makers can understand, work in a team or be able to lead a team.

And if you have work experience then you can specify how that experience has helped you grow as a Data Scientist.

3) How do you overcome challenges?

Now, these challenges could be task-based or even work culture related. This is to gauge how you approach problem-solving, and how you approach the resolution to a conflict.

You may often encounter stressful situations, which is where interpersonal skills come into play. When working in a team or a group, such situations can affect the work dynamics. You need to stress the importance of teamwork.

You can answer something like, 'I would acknowledge their contribution and findings, come up with a conclusion and invite open feedback'. This demonstrates leadership skills and maturity.

4) How would you work with large data sets?

This question is asked to make sure that your basics are absolutely clear. As a data scientist, you may have to come across huge volumes of data to work with.

Especially in a huge company, you’re constantly working with huge chunks of data. This question tests your knowledge of what kind of methods you'll use to organise  datasets. These methods are important as the organised data is then used to solve business concerns and is  crucial for a company.

You may name some tools and methods which are used to clean data, which will further highlight your knowledge.

5) Where do you see yourself in 5 years?

This is a tricky question, to check how much you have mapped out your career path. You most definitely should not say things related to quitting the company 

Instead of answering with a specific role, try and give benchmarks with respect to work experience. In the sense, you can give work milestones like gaining experience of data visualization tools, a better understanding of Softwares, Hadoop, mastering Python, working on the tableau platform, etc. Giving solid goals like this will give off an excellent impression, and the goals would also sound achievable.

These 5 questions are the most commonly asked ones in an HR interview.

You’re all set if you prepare your answers for these questions thoroughly, in this process, you’ll also get in the practice of putting your thoughts into words.

Finally, the more you prepare, the more confident you’ll be during the interview.

I hope you get selected.

Give it your best!

Check our blog on  learn data science