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Preparing for a machine learning interview as a data analyst is a great step.
Here are some common machine learning interview questions :-
1. Explain the steps involved in a machine learning project lifecycle.
2. What is the difference between supervised and unsupervised learning? Give examples of each.
3. What evaluation metrics would you use to assess the performance of a regression model?
4. What is overfitting and how can you prevent it?
5. Describe the bias-variance tradeoff.
6. What is cross-validation, and why is it important in machine learning?
7. What are some feature selection techniques you are familiar with?
8.What are the assumptions of linear regression?
9. How does regularization help in linear models?
10. Explain the difference between classification and regression.
11. What are some common algorithms used for dimensionality reduction?
12. Describe how a decision tree works.
13. What are ensemble methods, and why are they useful?
14. How do you handle missing or corrupted data in a dataset?
15. What are the different kernels used in Support Vector Machines (SVM)?
These questions cover a range of fundamental concepts and techniques in machine learning that are important for a data analyst role.
Good luck with your interview preparation!
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Some of the essential libraries of Python that are used in Data Science
Numpy
SciPy
Pandas
Matplotlib
Keras
TensorFlow
Scikit-learn
Who is Data Scientist?
He/she is responsible for collecting, analyzing and interpreting the results, through a large amount of data. This process is used to take an important decision for the business, which can affect the growth and help to face compititon in the market.
A data scientist analyzes data to extract actionable insight from it. More specifically, a data scientist:
Determines correct datasets and variables.
Identifies the most challenging data-analytics problems.
Collects large sets of data- structured and unstructured, from different sources.
Cleans and validates data ensuring accuracy, completeness, and uniformity.
Builds and applies models and algorithms to mine stores of big data.
Analyzes data to recognize patterns and trends.
Interprets data to find solutions.
Communicates findings to stakeholders using tools like visualization.
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What are the main assumptions of linear regression?
There are several assumptions of linear regression. If any of them is violated, model predictions and interpretation may be worthless or misleading.
1) Linear relationship between features and target variable.
2) Additivity means that the effect of changes in one of the features on the target variable does not depend on values of other features. For example, a model for predicting revenue of a company have of two features - the number of items a sold and the number of items b sold. When company sells more items a the revenue increases and this is independent of the number of items b sold. But, if customers who buy a stop buying b, the additivity assumption is violated.
3) Features are not correlated (no collinearity) since it can be difficult to separate out the individual effects of collinear features on the target variable.
4) Errors are independently and identically normally distributed (yi = B0 + B1*x1i + ... + errori):
i) No correlation between errors (consecutive errors in the case of time series data).
ii) Constant variance of errors - homoscedasticity. For example, in case of time series, seasonal patterns can increase errors in seasons with higher activity.
iii) Errors are normaly distributed, otherwise some features will have more influence on the target variable than to others. If the error distribution is significantly non-normal, confidence intervals may be too wide or too narrow.
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1. What are decorators in Python?
Ans: Decorators are used to add some design patterns to a function without changing its structure. Decorators generally are defined before the function they are enhancing. To apply a decorator we first define the decorator function. Then we write the function it is applied to and simply add the decorator function above the function it has to be applied to. For this, we use the @ symbol before the decorator.
2. What is the ACID property in a database?
The full form of ACID is atomicity, consistency, isolation, and durability.
β’ Atomicity refers that if any aspect of a transaction fails, the whole transaction fails and the database state remains unchanged.
β’ Consistency means that the data meets all validity guidelines.
β’ Concurrency management is the primary objective of isolation.
β’ Durability ensures that once a transaction is committed, it will occur regardless of what happens in between such as a power outage, fire, or some other kind of disturbance.
3. What is the meaning of KPI in statistics?
KPI is an acronym for a key performance indicator. It can be defined as a quantifiable measure to understand whether the goal is being achieved or not. KPI is a reliable metric to measure the performance level of an organization or individual with respect to the objectives. An example of KPI in an organization is the expense ratio.
4. Explain One-hot encoding and Label Encoding. How do they affect the dimensionality of the given dataset?
One-hot encoding is the representation of categorical variables as binary vectors. Label Encoding is converting labels/words into numeric form. Using one-hot encoding increases the dimensionality of the data set. Label encoding doesnβt affect the dimensionality of the data set. One-hot encoding creates a new variable for each level in the variable whereas, in Label encoding, the levels of a variable get encoded as 1 and 0.
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1. Can you explain how the memory cell in an LSTM is implemented computationally?
The memory cell in an LSTM is implemented as a forget gate, an input gate, and an output gate. The forget gate controls how much information from the previous cell state is forgotten. The input gate controls how much new information from the current input is allowed into the cell state. The output gate controls how much information from the cell state is allowed to pass out to the next cell state.
2. What is CTE in SQL?
A CTE (Common Table Expression) is a one-time result set that only exists for the duration of the query. It allows us to refer to data within a single SELECT, INSERT, UPDATE, DELETE, CREATE VIEW, or MERGE statement's execution scope. It is temporary because its result cannot be stored anywhere and will be lost as soon as a query's execution is completed.
3. List the advantages NumPy Arrays have over Python lists?
Pythonβs lists, even though hugely efficient containers capable of a number of functions, have several limitations when compared to NumPy arrays. It is not possible to perform vectorised operations which includes element-wise addition and multiplication. They also require that Python store the type information of every element since they support objects of different types. This means a type dispatching code must be executed each time an operation on an element is done.
4. Whatβs the F1 score? How would you use it?
The F1 score is a measure of a modelβs performance. It is a weighted average of the precision and recall of a model, with results tending to 1 being the best, and those tending to 0 being the worst.
5. Name an example where ensemble techniques might be useful?
Ensemble techniques use a combination of learning algorithms to optimize better predictive performance. They typically reduce overfitting in models and make the model more robust (unlikely to be influenced by small changes in the training data). You could list some examples of ensemble methods (bagging, boosting, the βbucket of modelsβ method) and demonstrate how they could increase predictive power.
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1. What are Different Kernels in SVM?
Linear kernel - used when data is linearly separable.
Polynomial kernel - When you have discrete data that has no natural notion of smoothness.
Radial basis kernel - Create a decision boundary able to do a much better job of separating two classes than the linear kernel.
Sigmoid kernel - used as an activation function for neural networks.
2. What is Cross-Validation?
Cross-validation is a method of splitting all your data into three parts: training, testing, and validation data. Data is split into k subsets, and the model has trained on k-1of those datasets.
The last subset is held for testing. This is done for each of the subsets. This is k-fold cross-validation. Finally, the scores from all the k-folds are averaged to produce the final score.
3. List the different types of relationships in SQL.
One-to-One - This can be defined as the relationship between two tables where each record in one table is associated with the maximum of one record in the other table.
One-to-Many & Many-to-One - This is the most commonly used relationship where a record in a table is associated with multiple records in the other table.
Many-to-Many - This is used in cases when multiple instances on both sides are needed for defining a relationship.
Self-Referencing Relationships - This is used when a table needs to define a relationship with itself.
4. What Are the Data Types Supported in Tableau?
Following data types are supported in Tableau:
Text (string) values
Date values
Date and time values
Numerical values
Boolean values (relational only)
Geographical values (used with maps)
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1. What do you understand by the term silhouette coefficient?
The silhouette coefficient is a measure of how well clustered together a data point is with respect to the other points in its cluster. It is a measure of how similar a point is to the points in its own cluster, and how dissimilar it is to the points in other clusters. The silhouette coefficient ranges from -1 to 1, with 1 being the best possible score and -1 being the worst possible score.
2. What is the difference between trend and seasonality in time series?
Trends and seasonality are two characteristics of time series metrics that break many models. Trends are continuous increases or decreases in a metricβs value. Seasonality, on the other hand, reflects periodic (cyclical) patterns that occur in a system, usually rising above a baseline and then decreasing again.
3. What is Bag of Words in NLP?
Bag of Words is a commonly used model that depends on word frequencies or occurrences to train a classifier. This model creates an occurrence matrix for documents or sentences irrespective of its grammatical structure or word order.
4. What is the difference between bagging and boosting?
Bagging is a homogeneous weak learnersβ model that learns from each other independently in parallel and combines them for determining the model average. Boosting is also a homogeneous weak learnersβ model but works differently from Bagging. In this model, learners learn sequentially and adaptively to improve model predictions of a learning algorithm
1. What are the uses of using RNN in NLP?
The RNN is a stateful neural network, which means that it not only retains information from the previous layer but also from the previous pass. Thus, this neuron is said to have connections between passes, and through time.
For the RNN the order of the input matters due to being stateful. The same words with different orders will yield different outputs.
RNN can be used for unsegmented, connected applications such as handwriting recognition or speech recognition.
2. How to remove values to a python array?
Ans: Array elements can be removed using pop() or remove() method. The difference between these two functions is that the former returns the deleted value whereas the latter does not.
3. What are the advantages and disadvantages of views in the database?
Answer: Advantages of Views:
As there is no physical location where the data in the view is stored, it generates output without wasting resources.
Data access is restricted as it does not allow commands like insertion, updation, and deletion.
Disadvantages of Views:
The view becomes irrelevant if we drop a table related to that view.
Much memory space is occupied when the view is created for large tables.
4. How to create a calculated field in Tableau?
Click the drop down to the right of Dimensions on the Data pane and select βCreate > Calculated Fieldβ to open the calculation editor.
Name the new field and create a formula.
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