Wednesday, July 17, 2024

# Machine Learning Interview Cheat Sheet

## Numpy Cheat Sheet By Justin

Machine Learning Cheat sheet – video (For quick Reference) – All In One ML

We all know that machine learning is all about numbers. In fact, in machine learning, we have a large set or large arrays of numbers. Although there are inbuilt options like lists and tuples to manage this data, they are not as usable as per the requirements. Hence, most of the machine learning enthusiasts use a library dedicated to numerical computations called Numpy.

Numpy is one of the most popular libraries that can handle large arrays and manipulate them according to user needs. While playing with a broad set of data, Numpy saves a lot of time for the user and makes it easier for him/her to intuitively understand the flow and structure of the data.

This beautiful cheat sheet by Justin covers all the primary syntactical techniques used in Numpy. It includes all the primary array operations, multidimensional access, etc. A quick view of the ordinary and binomial distribution is also provided.

The Numpy machine learning cheat sheet can be accessed here.

## How Do You Handle Outliers In The Data

Outlier is an observation in the data set that is far away from other observations in the data set. We can discover outliers using tools and functions like box plot, scatter plot, Z-Score, IQR score etc. and then handle them based on the visualization we have got. To handle outliers, we can cap at some threshold, use transformations to reduce skewness of the data and remove outliers if they are anomalies or errors.

## How Is The Transformer Architecture Better Than Rnns In Deep Learning

With the use of sequential processing, programmers were up against:

• The usage of high processing power
• The difficulty of parallel execution

This caused the rise of the transformer architecture. Here, there is a mechanism called attention mechanism, which is used to map all of the dependencies between sentences, thereby making huge progress in the case of NLP models.

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## If Youre Unsure Of An Answer Its Ok To Say So

Itâs possible that you’ll get a question you donât know the answer to. A straightforward way to approach this is to say, âIâm not sure of the answer, but here is how I would go about finding out … .â

## Comparing Lasso And Ridge Regression

Why Lasso can shrink the coefficient to exactly zero?

We can explain it using geometric interpretation:

• Different regularization term result in different shapes of the constraint region. Considering model with two variables, then Lasso has a diamond shape and Ridge has a circle shape for the constraint region.
• The contour lines represent the loss function value with different values of .
• The optimal point is found when the contour line of the loss function is tangent to the constraint boundary.
• For lasso regression, the optimal point is likely to be on the corner, which means that some parameters will be 0.
• For ridge regression, the optimal point can lies in any where in the constraints boundary, so the optimal point is not likely to contain 0 values.

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## Explain Dimensionality Reduction In Machine Learning

Dimensionality Reduction is the method of reducing the number of dimensions of any dataset by reducing the number of features. It is important because as we move into higher dimensions, the datapoints start becoming equidistant from each other which can affect the performance of unsupervised ML algorithms which use euclidean distance as the similarity function to classify datapoints. This is known as the Curse of Dimensionality. Also, it is difficult to visualize data beyond 4 dimensions.

## Data Science: Bokeh Cheat Sheet

Bokeh is an interactive visualization library that targets modern web browsers for presentation. Its goal is to provide elegant, concise construction of versatile graphics, and to extend this capability with high-performance interactivity over very large or streaming datasets. Bokeh can help anyone who would like to quickly and easily create interactive plots, dashboards, and data applications. from Bokeh.Pydata.com

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## 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 interviewing 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 Oob Error And How Does It Occur

Machine Learning Interview Questions and Answers | Machine Learning Interview Preparation | Edureka

For each bootstrap sample, there is one-third of data that was not used in the creation of the tree, i.e., it was out of the sample. This data is referred to as out of bag data. In order to get an unbiased measure of the accuracy of the model over test data, out of bag error is used. The out of bag data is passed for each tree is passed through that treeand the outputs are aggregated to give out of bag error. This percentage error is quite effective in estimating the error in the testing set and does not require further cross-validation.

## What Kind Of Projects Are Included As Part Of The Training

Intellipaat is offering you the most updated, relevant, and high-value real-world projects as part of the training program. This way, you can implement the learning that you have acquired in real-world industry setup. All training comes with multiple projects that thoroughly test your skills, learning, and practical knowledge, making you completely industry-ready.

You will work on highly exciting projects in the domains of high technology, ecommerce, marketing, sales, networking, banking, insurance, etc. After completing the projects successfully, your skills will be equal to 6 months of rigorous industry experience.

## What Are The Advantages Of Using A Naive Bayes For Classification

• Very simple, easy to implement and fast.
• If the NB conditional independence assumption holds, then it will converge quicker than discriminative models like logistic regression.
• Even if the NB assumption doesnt hold, it works great in practice.
• Need less training data.
• Handles continuous and discrete data.
• Not sensitive to irrelevant features.

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## Explain The Confusion Matrix With Respect To Machine Learning Algorithms

A confusion matrix is a specific table that is used to measure the performance of an algorithm. It is mostly used in supervised learning in unsupervised learning, its called the matching matrix.

The confusion matrix has two parameters:

It also has identical sets of features in both of these dimensions.

Consider a confusion matrix shown below:

Here,

Total Yes = 12+1 = 13

Total No = 3+9 = 12

Similarly, for predicted values:

Total Yes = 12+3 = 15

Total No = 1+9 = 10

For a model to be accurate, the values across the diagonals should be high. The total sum of all the values in the matrix equals the total observations in the test data set.

For the above matrix, total observations = 12+3+1+9 = 25

Now, accuracy = sum of the values across the diagonal/total dataset

= / 25

## How Is Deep Learning Better Than Machine Learning

Machine Learning is powerful in a way that it is sufficient to solve most of the problems. However, Deep Learning gets an upper hand when it comes to working with data that has a large number of dimensions. With data that is large in size, a Deep Learning model can easily work with it as it is built to handle this.

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## What Are The Different Modes Of Training That Intellipaat Provides

At Intellipaat, you can enroll in either the instructor-led online training or self-paced training. Apart from this, Intellipaat also offers corporate training for organizations to upskill their workforce. All trainers at Intellipaat have 12+ years of relevant industry experience, and they have been actively working as consultants in the same domain, which has made them subject matter experts. Go through the sample videos to check the quality of our trainers.

## Data Science: Matplotlib Cheat Sheet

Matplotlib is a plottinglibrary for the Python programming language and its numerical mathematics extension NumPy. It provides an object-orientedAPIfor embedding plots into applications using general-purpose GUI toolkits like Tkinter, wxPython, Qt, or GTK+. There is also a procedural pylab interface based on a state machine , designed to closely resemble that of MATLAB, though its use is discouraged. SciPy makes use of matplotlib.

Pyplot is a matplotlib module which provides a MATLAB-like interface matplotlib is designed to be as usable as MATLAB, with the ability to use Python, with the advantage that it is free.

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## What Is The Main Key Difference Between Supervised And Unsupervised Machine Learning

 Supervised learning Unsupervised learning The supervised learning technique needs labelled data to train the model. For example, to solve a classification problem , you need to have label data to train the model and to classify the data into your labelled groups. Unsupervised learning does not need any labelled dataset. This is the main key difference between supervised learning and unsupervised learning.

## What Are Overfitting And Underfitting Why Does The Decision Tree Algorithm Suffer Often With Overfitting Problems

BE PREPARED Machine Learning Engineer interview questions

Overfitting is a statistical model or machine learning algorithm which captures the noise of the data. Underfitting is a model or machine learning algorithm which does not fit the data well enough and occurs if the model or algorithm shows low variance but high bias.

In decision trees, overfitting occurs when the tree is designed to perfectly fit all samples in the training data set. This results in branches with strict rules or sparse data and affects the accuracy when predicting samples that arent part of the training set.

Also Read: Overfitting and Underfitting in Machine Learning

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## 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.

## What Is The Application Procedure For This Professional Certificate Program In Ai And Machine Learning

This AIML Course has a three-step application process:

• All interested applicants must apply online for the AI and Machine Learning Course using the application form.
• Candidates will be shortlisted by an admissions panel based on their application.
• The shortlisted candidates will be offered admission, which they must accept by paying the AI and Machine Learning Certification program cost.
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## Explain The K Nearest Neighbor Algorithm

K nearest neighbor algorithm is a classification algorithm that works in a way that a new data point is assigned to a neighboring group to which it is most similar.

In K nearest neighbors, K can be an integer greater than 1. So, for every new data point, we want to classify, we compute to which neighboring group it is closest.

Let us classify an object using the following example. Consider there are three clusters:

Let the new data point to be classified is a black ball. We use KNN to classify it. Assume K = 5 .

Next, we find the K nearest data points, as shown.

Observe that all five selected points do not belong to the same cluster. There are three tennis balls and one each of basketball and football.

When multiple classes are involved, we prefer the majority. Here the majority is with the tennis ball, so the new data point is assigned to this cluster.

## What Do You Mean By The Roc Curve

Receiver operating characteristics : ROC curve illustrates the diagnostic ability of a binary classifier. It is calculated/created by plotting True Positive against False Positive at various threshold settings. The performance metric of ROC curve is AUC . Higher the area under the curve, better the prediction power of the model.

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## Whats A Fourier Transform

Fourier Transform is a mathematical technique that transforms any function of time to a function of frequency. Fourier transform is closely related to Fourier series. It takes any time-based pattern for input and calculates the overall cycle offset, rotation speed and strength for all possible cycles. Fourier transform is best applied to waveforms since it has functions of time and space. Once a Fourier transform applied on a waveform, it gets decomposed into a sinusoid.

## Who Are The Instructors For This Ai Ml Certification And How Are They Selected

Our AI and Machine Learning professors are all industry specialists with years of expertise in the field. Before they are qualified to train for us, they have undergone a thorough selection procedure that includes profile assessment, technical examination, and a training presentation. We also ensure that only instructors with a high alumnus rating stay on our ML AI Course staff.

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## Cheat Sheets You Need To Ace Data Science Interview

The only cheat you need for a job interview and data professional life. It includes SQL, web scraping, statistics, data wrangling and visualization, business intelligence, machine learning, deep learning, NLP, and super cheat sheets.

The list of 10 cheat sheets is for beginners, students, job seekers, and professionals. These are my favorite, and they are hand-picked so that you dont have to search for the best cheat sheet for every subcategory of data science.

The cheat sheets are life savers. It has helped me multiple times when I was preparing for data science and machine learning interviews. It just took me 30 minutes to review all of the old but necessary concepts and prepare for any technocal question.

The list of cheat sheets covers:

SQL by Dataquest is a blog style cheat sheet. It will give you an overview of SQL basic queries.

• Fundamentals: selecting rows and columns, comments, and limits
• Joins: inner, left, right, and outer joins
• Complex Queries: subqueries, string match, Case, With clause, creating and dropping views, Union, Intersect, and chaining

As a data scientist, you must be aware of these functions and commands to pass the SQL coding interview session. Even after that, it will be a major part of your work life. Extracting specific data, creating pipelines, processing the data, and creating analytics all using SQL commands and complex queries.

## What Is The Meaning Of Overfitting

Crack Machine Learning Problems for Data Scientists | Data Science Interviews

Overfitting is a very common issue when working with Deep Learning. It is a scenario where the Deep Learning algorithm vigorously hunts through the data to obtain some valid information.

This makes the Deep Learning model pick up noise rather than useful data, causing very high variance and low bias. This makes the model less accurate, and this is an undesirable effect that can be prevented.

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## Does The Job Assistance Program Guarantee Me A Job

Apparently, no. Our job assistance program is aimed at helping you land in your dream job. It offers a potential opportunity for you to explore various competitive openings in the corporate world and find a well-paid job, matching your profile. The final decision on hiring will always be based on your performance in the interview and the requirements of the recruiter.

## What Is The Difference Between Machine Learning And Deep Learning

Machine Learning forms a subset of Artificial Intelligence, where we use statistics and algorithms to train machines with data, thereby, helping them improve with experience.

Deep Learning is a part of Machine Learning, which involves mimicking the human brain in terms of structures called neurons, thereby, forming neural networks.

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## Visual Guide To Neural Network Infrastructures

This 1-page visual guide gives you a quick overview of all the most common neural network infrastructures that you will find in the wild. The sheet showcases 27 different architectures. As a machine learning newbie, you will not get much out of this sheet. However, if you are a practitioner in the field of neural networks, you will like it.

The cheat sheet shows 27 neural network architectures including

• Feedforward, Radial basis network, Deep feedforward,
• Recurrent neural network, long / short term memory , gated recurrent unit,
• Autoencoder, variational autoencoder, denoising autoencoder, sparse autoencoder,
• Boltzmann machine, restricted Boltzmann machine, deep belief network, and
• Finally, deep convolutional network, deconvolutional network, deep convolutional inverse graphics network, generative adversarial network, liquid state machine, extreme learning machine, echo state network, deep residual network, kohonen network, support vector machine, and neural turing machine.

Pheww, what a list!