Introduction
Artificial intelligence, machine learning, and natural language processing have surely become a heap, especially in today’s times. Every industry finance, banking, retail, manufacturing, software development, healthcare, etc is no longer craving for professionals who simply know programming but someone who has the nerve to adopt as well as make the most of these new-age technologies like artificial intelligence (AI) and machine learning.
No wonder the demand for data scientists, artificial intelligence engineers and machine learning engineers are increasing day by day, hinting towards becoming one of the most promising careers across the globe. So it wouldn’t be surprising at all if you are willing to apply for these kinds of jobs or if companies are looking to hire software developers to keep up with the demand. Now here is when such a post plays an amazing role. The following post focuses on some of the common yet crucial machine-learning interview questions which can assist you in taking the first step towards a highly promising future. So without further ado, let’s just get started!
Top Machine Learning Interview Questions
1. Are machine learning and general programming similar concepts?
Of course not! General programming is something where you have data and logic and with the help of both you create relevant answers whereas when it comes to machine learning, though there are data and answers but machine learning technology itself learns the logic from them just so that similar logic can be considered to answer all relevant questions right away as well as which might be faced in future.
In addition, there are times when it is not possible to write the logic in codes so yes during such crucial times machine learning acts as a saviour and learns the logic itself.
2. Can you define numerous types of machine learning?
Well, there are mainly three types of machine learning.
- Supervised Learning – Here the model makes relevant predictions as well as decisions on the basis of past, or previous data. The term labelled data is used for a wide range of data which are given tags and labels and ensured to be created more meaningfully.
- Unsupervised Learning – Here there is no term like labelled data. A model can easily identify relevant patterns, anomalies and relationships in the input data.
- Reinforcement Learning – As the name implies, this model is highly based on rewards and it is mainly received on the basis of previous actions taken.
3. Difference between Bias and Variance
Bias is said when a model successfully makes predictions, this happens on the basis of disparity between the model’s prediction values as well as actual value, this is what bias is all about. In other words, bias is mainly the incapacity of machine learning algorithms such as linear regression to successfully grasp the actual relation between data points.
Variance is an alternative training data which are successfully utilised. It is easily possible for a variance to describe the degree of variation in prediction. So in other words, variance is successfully described as a random variable which can be easily deviated from its predicted value.
4. Can you provide some real-life applications of cluster algorithms?
The clustering technique can be successfully used among different domains of data science regarding image classification, customer segmentation, and recommendation engines. Clustering algorithms are highly considered in market research and customer segmentation which can be further used to target a specific market group to expand business as well as bring some of the most profitable outcomes.
5. Explain the term Hypothesis
The term is mainly used in the supervised machine learning domain. Here you will find a wide range of independent features and target variables where it becomes easy to find an approximate function mapping from the feature space to the target variable that approximation of mapping is known as a hypothesis.
6. Difference between the Training set and Test set
| Training Set | Test Set |
| The training set is given to the model for seamless analysing and learning | The test set is mainly used to test the seamless accuracy of a hypothesis generated by the model |
| 70% of the total data is mainly taken as the training dataset | Here the remaining 30% is what matters the most when it comes to training dataset |
| This is labelled data used to train the model. | We test without labelled data and then verify results with labels |
7. Is there any way to handle missing or corrupted data in a dataset?
One of the easiest ways to consider to successfully handle missing as well as corrupted data is by dropping different types of rows and columns or replacing them with some other relevant value. In Panda,
- IsNull() and dropna() will help to find the columns/rows with missing data and drop them
- Fillna() will replace the wrong values with a placeholder value
8. Can you define deep learning?
Well, deep learning is a hardcore subset of machine learning that incorporates a wide range of systems that can easily think as well as learn as much as humans do or maybe more with the help of artificial neural networks. Now why it is called deep because it has several layers of neural networks.
Machine learning and deep learning are pretty different from each other as engineering is done manually in machine learning whereas deep learning, the model consisting of neural networks, will automatically determine which features to use.
9. What is decision tree classification?
This is said when a tree structure is considered to successfully generate different kinds of regression or classification models. Simultaneously while the decision tree is being developed, datasets are split relevantly into smaller subsets in a tree-like manner featuring numerous branches and nodes. One of the best aspects of decision tree classification is that it has the potential to manage both categories as well as numerical data.
10 . How is Amazon Able to Recommend Other Things to Buy? How Does the Recommendation Engine Work?
As soon as a user buys something from Amazon, the company successfully stores that purchase data for future reference and looks for products which are almost bought or likely to be bought. This is possible due to an association algorithm where patterns can be quickly identified in a given dataset.
11. Define Random forest.
It is a supervised machine learning algorithm which is highly considered for attending classification problems. Here numerous decision trees are developed during the training phase and random forest makes the ultimate choice.
12. What are the five popular algorithms used in machine learning?
- Neural Networks – A set of algorithms to assist machines in recognizing patterns without any explicitly programmed.
- Decision Trees – A supervised learning technique
- K-nearest neighbour – A supervised learning algorithm used for classification and regression. Here the dataset is trained and relevant predictions are made.
- Support Vector Machines – Used to develop the best line or decision boundary to incorporate new data points in the right category.
- Probabilistic networks – These are graphical models enabling a compact description of complicated stochastic relationships among different random variables.
13. Define data leakage and how it is possible to identify it.
Now this situation occurs when there is a high correlation between the target variable and the input features then this is called data leakage. This is usually done to achieve the highest accuracy. This model offers decent performance in regard to the training as well as the validation data. Moreover, the model makes relevant predictions and chances are that the performance might not go up to the mark. This is how data leakage can be identified.
14. What is one-shot learning?
Another interesting concept in the machine learning realm is where the model is successfully trained in such a manner that it can easily recognize patterns from datasets. Finding similarities and dissimilarities is possible here.
15. What level of maths is required for machine learning?
Certain statistical concepts are mandatory such as linear algebra, probability, Multivariate Calculus, and Optimization. Also, it would be great if you could incorporate programming as a part of machine learning.
Conclusion
I can go on and on when it comes to top machine-learning interview questions, such as
- How Will You Know Which Machine Learning Algorithm to Choose for Your Classification Problem?
- Can you choose classification over regression, when it is possible to do so?
- Explain the concept of performance of XGBoost and why it is better than SVM.
- What is cross-validation
- How can you measure the effectiveness of the clusters?
- What are classification reports, can you elaborate on them?
However, several reputable and reliable post-graduation courses on machine learning and artificial intelligence exist. Also, you can keep reading other relevant blogs or posts or look around for forums for more information and updates regarding the same. I hope you did find the post helpful and good luck with your upcoming interview, I am pretty sure you will crack it right away.