How To Prepare For Machine Learning Engineer Interview

What Are Some Differences Between A Linked List And An Array

Arrays and Linked lists are both used to store linear data of similar types. However, there are a few difference between them.

What Is A Pipeline

Ans. A pipeline is a sophisticated way of writing software such that each intended action while building a model can be serialized and the process calls the individual functions for the individual tasks. The tasks are carried out in sequence for a given sequence of data points and the entire process can be run onto n threads by use of composite estimators in scikit learn.

What Are Parametric Models Provide An Example

Parametric models have a finite number of parameters. You only need to know the parameters of the model to make a data prediction. Common examples are as follows: linear SVMs, linear regression, and logistic regression.

Non-parametric models have an unbounded number of parameters to offer flexibility. For data predictions, you need the parameters of the model and the state of the observed data. Common examples are as follows: k-nearest neighbors, decision trees, and topic models.

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What Is The Difference Between The Naive Bayes Classifier And The Bayes Classifier

Naive Bayes assumes conditional independence, P=P

P=P, Whereas more general Bayes Nets , will allow the user to specify which attributes are, in fact, conditionally independent.

For the Bayesian network as a classifier, the features are selected based on some scoring functions like Bayesian scoring function and minimal description length. The scoring functions mainly restrict the structure and the parameters using the data. After the structure has been learned the class is only determined by the nodes in the Markov blanket, and all variables given the Markov blanket are discarded.

Programming Is The Key

Choose one programming language, master it, and be ready to answer practical questions by writing code in this language.

The lack of knowledge of a particular programming language will not be a dealbreaker: any language can be learned fast enough, but it takes years to learn to program.

We recommend focussing on learning Python. It is a de-facto standard in the Machine Learning community. However, C++, C#, Java, Kotlin, Scala, Clojure, Lua or even R could be possible alternatives. We don’t recommend JavaScript, Perl, Ruby, and PHP.

We don’t recommend either such languages as Matlab or SAS/SPSS, as they are proprietary and quite niche. The modern ML community is open-source oriented: the best tools and frameworks that are used in the state-of-the-art ML systems are all open source, well-documented and of an excellent stability. You can rely upon them to build your AI systems.

There’s an almost infinite amount of Python code online you can inspire from to build your own Machine Learning solutions.

The most important things to know about any programming language are:

• How to work with sets, lists, and dictionaries and when to use which
• How to handle exceptions
• Being capable of building specialized data structures such as linked lists, binary or prefix trees
• Being capable of using highly optimized vectorized operations instead of loops.

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Apple Machine Learning Engineer Interview Skills And Qualifications

Below are the qualifications required to apply for a Machine Learning Engineer role at Apple:

• A degree in Computer Science, or IT, preferably a Masterâs Degree
• Working knowledge of algorithms and deep learning data models
• Working knowledge of data processing technologies and Machine Learning frameworks
• Working knowledge of predictive data models and automating predictive models
• Proven experience in building system applications and scalable ML systems
• Working knowledge of an Object-Oriented Programming Language
• 4+ years of experience in the field

What Are The Most Important Machine Learning Skills I Should Know

A lot of data engineering and machine learning roles involve working with different tech stacks, so its hard to nail down a hard and fast set of skills, as much depends on the company youre interviewing with.

For example, if its a cloud based-role, a machine learning engineer is going to want to have experience with AWS and Azure and for languages alone, Python and R are the most important, because thats what we see more and more in machine learning engineering, Sulley said. For deployment, Id say Docker, but it really depends on the persons background and what theyre looking to get into.

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What Do You Understand By Precision And Recall

In pattern recognition, The information retrieval and classification in machine learning are part of precision. It is also called as positive predictive value which is the fraction of relevant instances among the retrieved instances.

Recall is also known as sensitivity and the fraction of the total amount of relevant instances which were actually retrieved.

Both precision and recall are therefore based on an understanding and measure of relevance.

How I Got Amazon Data Scientist Offer Within 2 Months

Learn how to prepare for Amazon data scientist interview

In this interview series, I summarize interview experiences from people Ive helped with interview preparation. Patrick is a Senior data scientist working in one of the coldest place in the North America. I hope it helps you in preparing for Amazon Data scientist interview.

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Considering A Long List Of Machine Learning Algorithms Given A Data Set How Do You Decide Which One To Use

There is no master algorithm for all situations. Choosing an algorithm depends on the following questions:

• How much data do you have, and is it continuous or categorical?
• Is the problem related to classification, association, clustering, or regression?
• Predefined variables , unlabeled, or mix?
• What is the goal?

Based on the above questions, the following algorithms can be used:

Who Are The Instructors As Part Of This Ai Ml Certification And How Are They Selected

All of our highly qualified AI and Machine Learning instructors are industry experts with years of relevant experience. Each of them has gone through a rigorous selection process that includes profile screening, technical evaluation, and a training demo before they are certified to train for us. We also ensure that only those trainers with a high alumni rating remain part of our faculty.

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Explain The Difference Between Lasso And Ridge

Lasso and Ridge are the regularization techniques where we penalize the coefficients to find the optimum solution. In ridge, the penalty function is defined by the sum of the squares of the coefficients and for the Lasso, we penalize the sum of the absolute values of the coefficients. Another type of regularization method is ElasticNet, it is a hybrid penalizing function of both lasso and ridge.

Explain The Difference Between An Array And A Linked List

An array is an ordered collection of objects. It assumes that every element has the same size, since the entire array is stored in a contiguous block of memory. The size of an array is specified at the time of declaration and cannot be changed afterward.

Search options for an array are Linear search and Binary search .

A linked list is a series of objects with pointers. Different elements are stored at different memory locations, and data items can be added or removed when desired.

The only search option for a linked list is Linear.

Additional beginner questions may include:

• Which is more important: model performance or accuracy? Why?
• Whats the F1 score? How is it used?
• What is the Curse of Dimensionality?
• When should we use classification rather than regression?
• Explain Deep Learning. How does it differ from other techniques?
• Explain the difference between likelihood and probability.

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What Are Recommender Systems

A recommendation engine is a system used to predict usersâ interests and recommend products that are quite likely interesting for them.

Data required for recommender systems stems from explicit user ratings after watching a film or listening to a song, from implicit search engine queries and purchase histories, or from other knowledge about the users/items themselves.

What Is This Article About

In this article, I share an eclectic collection of interview questions that will help you in preparing for Machine Learning interviews. This is helpful to someone who is interested in one/more of the following positions in the Machine Learning group of a leading company (Google, Facebook, IBM, Amazon

What Is The Difference Between Stochastic Gradient Descent And Gradient Descent

Gradient Descent and Stochastic Gradient Descent are the algorithms that find the set of parameters that will minimize a loss function.The difference is that in Gradient Descend, all training samples are evaluated for each set of parameters. While in Stochastic Gradient Descent only one training sample is evaluated for the set of parameters identified.

State The Limitations Of Fixed Basis Function

Linear separability in feature space doesnt imply linear separability in input space. So, Inputs are non-linearly transformed using vectors of basic functions with increased dimensionality. Limitations of Fixed basis functions are:

Q14 Youre Asked To Build A Random Forest Model With 10000 Trees During Its Training You Got Training Error As 000 But On Testing The Validation Error Was 3423 What Is Going On Havent You Trained Your Model Perfectly

• The model is overfitting the data.
• Training error of 0.00 means that the classifier has mimicked the training data patterns to an extent.
• But when this classifier runs on the unseen sample, it was not able to find those patterns and returned the predictions with more number of errors.
• In Random Forest, it usually happens when we use a larger number of trees than necessary. Hence, to avoid such situations, we should tune the number of trees using cross-validation.

Q15. People who bought this also bought recommendations seen on Amazon is based on which algorithm?

E-commerce websites like Amazon make use of Machine Learning to recommend products to their customers. The basic idea of this kind of recommendation comes from collaborative filtering. Collaborative filtering is the process of comparing users with similar shopping behaviors in order to recommend products to a new user with similar shopping behavior.

Collaborative Filtering Machine Learning Interview Questions Edureka

To better understand this, lets look at an example. Lets say a user A who is a sports enthusiast bought, pizza, pasta, and a coke. Now a couple of weeks later, another user B who rides a bicycle buys pizza and pasta. He does not buy the coke, but Amazon recommends a bottle of coke to user B since his shopping behaviors and his lifestyle is quite similar to user A. This is how collaborative filtering works.

What Is Target Imbalance How Do We Fix It A Scenario Where You Have Performed Target Imbalance On Data Which Metrics And Algorithms Do You Find Suitable To Input This Data Onto

If you have categorical variables as the target when you cluster them together or perform a frequency count on them if there are certain categories which are more in number as compared to others by a very significant number. This is known as the target imbalance.

Example: Target column 0,0,0,1,0,2,0,0,1,1 0 are in majority. To fix this, we can perform up-sampling or down-sampling. Before fixing this problem lets assume that the performance metrics used was confusion metrics. After fixing this problem we can shift the metric system to AUC: ROC. Since we added/deleted data , we can go ahead with a stricter algorithm like SVM, Gradient boosting or ADA boosting.

Q3 How Does Deep Learning Differ From Machine Learning

Deep Learning vs Machine Learning Machine Learning Interview Questions Edureka

Apple Machine Learning Engineer Interview Prep Tips

At interview Kickstart, weâve trained thousands of engineers for technical interviews at top tech companies. Having understood what it takes to crack these interviews, weâve compiled a list of tips to help you ace your upcoming ML Engineer Interview:

Q7 Explain False Negative False Positive True Negative And True Positive With A Simple Example

Lets consider a scenario of a fire emergency:

• True Positive: If the alarm goes on in case of a fire.Fire is positive and prediction made by the system is true.
• False Positive: If the alarm goes on, and there is no fire.System predicted fire to be positive which is a wrong prediction, hence the prediction is false.
• False Negative: If the alarm does not ring but there was a fire.System predicted fire to be negative which was false since there was fire.
• True Negative: If the alarm does not ring and there was no fire.The fire is negative and this prediction was true.

Explain The Phrase Curse Of Dimensionality

The Curse of Dimensionality refers to the situation when your data has too many features.

The phrase is used to express the difficulty of using brute force or grid search to optimize a function with too many inputs.

It can also refer to several other issues like:

• If we have more features than observations, we have a risk of overfitting the model.
• When we have too many features, observations become harder to cluster. Too many dimensions cause every observation in the dataset to appear equidistant from all others and no meaningful clusters can be formed.

Dimensionality reduction techniques like PCA come to the rescue in such cases.

How Do You Make Sure Which Machine Learning Algorithm To Use

It completely depends on the dataset we have. If the data is discrete we use SVM. If the dataset is continuous we use linear regression.

So there is no specific way that lets us know which ML algorithm to use, it all depends on the exploratory data analysis .

EDA is like âinterviewingâ the dataset As part of our interview we do the following:

• Classify our variables as continuous, categorical, and so forth.
• Summarize our variables using descriptive statistics.
• Visualize our variables using charts.

Based on the above observations select one best-fit algorithm for a particular dataset.

What Is Linear Regression

Linear Function can be defined as a Mathematical function on a 2D plane as, Y =Mx +C, where Y is a dependent variable and X is Independent Variable, C is Intercept and M is slope and same can be expressed as Y is a Function of X or Y = F.

At any given value of X, one can compute the value of Y, using the equation of Line. This relation between Y and X, with a degree of the polynomial as 1 is called Linear Regression.

In Predictive Modeling, LR is represented as Y = Bo + B1x1 + B2x2The value of B1 and B2 determines the strength of the correlation between features and the dependent variable.

Example: Stock Value in $ = Intercept + * + *

You Are Told That Your Regression Model Is Suffering From Multicollinearity How Do Verify This Is True And Build A Better Model

You should create a correlation matrix to identify and remove variables with a correlation above 75%. Keep in mind that our threshold here is subjective.

You could also calculate VIF to check for the presence of multicollinearity. A VIF value greater than or equal to 4 suggests that there is no multicollinearity. A value less than or equal to 10 tells us there are serious multicollinearity issues.

You cant just remove variables, so you should use a penalized regression model or add random noise in the correlated variables, but this approach is less ideal.

Ace the machine learning interview with high-level thinking.

This interactive course helps you build ML system design skills, and goes over some of the most popularly asked interview problems at big tech companies. By the end, youll be able to ace the machine learning interview and impress with your ability to think about systems at a high level.