• I’m just a noobie
• I’ve made 10 projects you silly
• I publish one research paper a day, dumbass.

Machine Learning interview questions

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a. I’m just a noobie (Easy)

• It’s like asking how Potato is different from vegetables! Exactly, they aren’t different. Deep Learning is just a subset of Machine Learning,
• Deep Learning is nothing but the evolution of Machine Learning.
• Yes, I agree that a Machine learning model does get progressively better over time in whatever they are being trained on, but they still need some guidance. When the ML model starts giving wrong predictions (high error), an engineer has to step in to tinker with some parameters to optimize the algorithm further, manually.
• Whereas, a Deep Learning algorithm is intelligent in its purest form. It fixes its error itself, i.e, it optimizes itself when it starts getting wrong predictions with no human intervention through its own Neural Network.
• Though, a Deep Learning model as to outperform an Machine Learning model requires a lot more training and a lot more Data. But if given the right ingredients, it can outperform Machine Learning models like crazyy!

• First of all, model accuracy is just another “evaluation metric” among thousand others in model performance.
• Model accuracy is nothing but just the percentage of how many times your model was correct out of the total number of predictions.
• There are tons of other measures too, and which evaluation metric can depend on various measures like the type of problem, requirements, type of solutions etc.
• Lastly, as model accuracy is just another part of model performance, they are directly proportional (+ve correlation) anyways.

• Classification:

1. As the name suggests, it CLASSIFies things.
2. It basically is the discrete answer to anything. Male/Female, Buy/Don’t But, 0/1
3. Understand it like this. ‘Will it rain today?’ → ‘Yes’; ‘Is he old?’ → ‘No’

• Regression:

1. As the name suggests, it REGRESS….ok wait, this won’t work with Regression.
2. It basically is the type of problem where the answers cannot be discrete but continuous. Age, Price
3. Understand it like this. ‘How much will it rain today?’ → ‘2mm’; ‘How old is he?’ → ‘22’

• The first thing we learn in Machine Learning is that age old good rule of thumb of splitting the dataset into 80% Training and 20% Testing sets. And as the legend says, it works ...most of the time.
• The thing is, it totally depends on our dataset volume. For example:

1. Assume that we’ve a dataset with 1 million samples, according to the thumb rule, 20% of our dataset would be 2 freaking hundred thousand samples allocated for the test set. You know how dumb that move is? If we’ve such a huge dataset, we can easily go with allocating even just 1% of the dataset, which still is 10,000 samples (which sounds just about right) and the performance won’t be tempered, if anything, the model will perform better. And do you realize what this move did, changed the split to 99:1!
2. Again, let’s assume that we’ve a small dataset with just 500 samples. In that case, we might have to allocate a little more than 20%, let’s say 30% of out dataset (150 samples) to test set, just so that the model evaluation is done properly. Now that makes it 70:30 split!

b. I’ve made 10 projects you silly (Medium)

• “Yeah Man, you got it!” (Positive answer)
  Listening to this, you run to her and hug her….
  

1. She hugs you back. No confusion. Trump was right. → True Positive
2. She slaps you. Trump was wrong. → False Positive

• “Sorry buddy, we’ll find someone better for you don’t worry..” (Negative answer)
  Listening to this, you run back to home and cry in your pillow…
  

1. She kept waiting for you and cried when you didn’t come. Trump was wrong. → False Negative
2. No confusion, everybody is clear. Trump was right. → True Negative

• TYPE I is just another name for False Positives, and
• TYPE II is just another name for False Negatives.

• Bias is nothing but just the difference between the predicted output and the original label. High bias tells us that our lovely model isn’t accurate.
• Variance on the other half, is the difference between the predictions of the training sets. High variance shows fluctuations, i.e, that the model is not stable.

• In the most simplest words put, Recall is nothing but simply the accuracy of our model, i.e, how many of the total answers are correct.
• Whereas Precision is simply the ratio of a number of events you can correctly recall, to the total number of events you can recall (mix of correct and wrong recalls).

Precision and Recall

• I say,”Ok so this is a line, and you only have to search for the keys on this line only!” Won’t that be super-duper easy? Just keep following the line.
• I say,”Ok, so I think you must have lost it on the third floor.” Now the work got tedious. Right? You now have to search for the small keys in the whole floor.
• I say,”Ok, man you could’ve lost them anywhere in this building. Good luck!” Now you really need that ‘Good Luck’. Now the job really got warmed up cause you’ll have to search for the keys up and down the floors! You gotta turn on the Mr.Sherlock mode now.
  

• Predictive models have a tradeoff between bias (how well the model fits the data) and variance (how much the model changes based on changes in the inputs).
• Simpler models are stable (low variance) but they don't get close to the truth (high bias).
• More complex models are more prone to being overfit (high variance) but they are expressive enough to get close to the truth (low bias).
• The best model for a given problem usually lies somewhere in the middle.
• We need to aim for Low Bias + Low Variance to build a good model.

Bias-Variance Tradeoff

c. I publish one research paper a day, you dumbass. (I’ve to tell?)

• PCA is a part of Feature Engineering wherein we try to reduce the number of features without losing the information by combining them into uncorrelated linear combinations.
• These new features (also called principal components), sequentially maximize the variance represented (i.e. the first principal component has the most variance, the second principal component has the second most, and so on).
• As a result, PCA is useful for dimensionality reduction because you can set an arbitrary variance cutoff

• How to prevent overfitting:
  Both L1 and L2 regularization prevents overfitting by shrinking (imposing a penalty) on the coefficients.
  

• Difference between L1 and L2:
  L2 (Ridge) shrinks all the coefficients by the same proportions but eliminates none, while L1 (Lasso) can shrink some coefficients to zero, performing variable selection.
  

• Which to use?
  If all the features are correlated with the label, ridge outperforms lasso, as the coefficients are never zero in ridge. If only a subset of features are correlated with the label, lasso outperforms ridge as in the lasso model some coefficient can be shrunk to zero.
  

Bonus Machine learning interview questions time..!

> My other blogs on Machine Learning:

> More Machine Learning Interview Questions/Resources to refer:

• Machine Learning Deep Learning Best Courses
• How to crack Data Science Interviews - Krish Naik
• Amazon Data Science Interview- Part 1 & Internship Opportunities - Krish Naik

> For cracking any Software Engineering Interview (very highly recommended by everybody):

> More Machine Learning Interview Questions (or in general) Books (Very Popular):