Here are 25 most common ML interview screening questions for each category:


1. Machine Learning fundamentals:
- Explain the difference between supervised, unsupervised, and reinforcement learning. Provide an example for each.
- What is the bias-variance tradeoff? How does it affect model performance?
- Describe the process of cross-validation. Why is it important in model evaluation?
- What is overfitting, and how can you prevent it in your models?
- Explain the concept of ensemble learning. What are bagging and boosting?

2. Statistics and Probability:
- Explain the difference between frequentist and Bayesian approaches in statistics.
- What is the Central Limit Theorem, and why is it important in machine learning?
- Describe the concept of hypothesis testing and its application in A/B testing.
- What is maximum likelihood estimation? Provide an example of its use in machine learning.
- Explain the difference between correlation and causation. How does this impact model interpretation?

3. Model Evaluation and Deployment:
- What metrics would you use to evaluate a classification model? How do they differ for balanced vs. imbalanced datasets?
- Describe the process of deploying a machine learning model in a production environment.
- What is A/B testing in the context of machine learning models? How would you design an A/B test?
- Explain the concept of model drift. How can it be detected and mitigated?
- What are the key considerations when scaling a machine learning system to handle large amounts of data or traffic?

4. Python for Machine Learning:
- How would you handle missing data in a pandas DataFrame?
- Explain the difference between a list and a numpy array in Python. When would you use one over the other?
- What are lambda functions in Python? Provide an example of how they can be used in data processing.
- Describe the purpose of the scikit-learn library. How would you use it to implement a simple classification model?
- What is the difference between *args and **kwargs in Python? How might they be useful in creating flexible ML functions?

5. Data Preprocessing:
- What is feature scaling, and why is it important? Describe different methods of feature scaling.
- How do you handle categorical variables in machine learning models? Explain one-hot encoding and label encoding.
- What is dimensionality reduction? Describe PCA (Principal Component Analysis) and its applications.
- How do you deal with imbalanced datasets? Discuss various techniques to address this issue.
- What is feature selection? Describe a few methods for selecting the most important features for a model.

Technologies used in Netflix

Master Javascript :

The JavaScript Tree 👇
|
|── Variables
| ├── var
| ├── let
| └── const
|
|── Data Types
| ├── String
| ├── Number
| ├── Boolean
| ├── Object
| ├── Array
| ├── Null
| └── Undefined
|
|── Operators
| ├── Arithmetic
| ├── Assignment
| ├── Comparison
| ├── Logical
| ├── Unary
| └── Ternary (Conditional)
||── Control Flow
| ├── if statement
| ├── else statement
| ├── else if statement
| ├── switch statement
| ├── for loop
| ├── while loop
| └── do-while loop
|
|── Functions
| ├── Function declaration
| ├── Function expression
| ├── Arrow function
| └── IIFE (Immediately Invoked Function Expression)
|
|── Scope
| ├── Global scope
| ├── Local scope
| ├── Block scope
| └── Lexical scope
||── Arrays
| ├── Array methods
| | ├── push()
| | ├── pop()
| | ├── shift()
| | ├── unshift()
| | ├── splice()
| | ├── slice()
| | └── concat()
| └── Array iteration
| ├── forEach()
| ├── map()
| ├── filter()
| └── reduce()|
|── Objects
| ├── Object properties
| | ├── Dot notation
| | └── Bracket notation
| ├── Object methods
| | ├── Object.keys()
| | ├── Object.values()
| | └── Object.entries()
| └── Object destructuring
||── Promises
| ├── Promise states
| | ├── Pending
| | ├── Fulfilled
| | └── Rejected
| ├── Promise methods
| | ├── then()
| | ├── catch()
| | └── finally()
| └── Promise.all()
|
|── Asynchronous JavaScript
| ├── Callbacks
| ├── Promises
| └── Async/Await
|
|── Error Handling
| ├── try...catch statement
| └── throw statement
|
|── JSON (JavaScript Object Notation)
||── Modules
| ├── import
| └── export
|
|── DOM Manipulation
| ├── Selecting elements
| ├── Modifying elements
| └── Creating elements
|
|── Events
| ├── Event listeners
| ├── Event propagation
| └── Event delegation
|
|── AJAX (Asynchronous JavaScript and XML)
|
|── Fetch API
||── ES6+ Features
| ├── Template literals
| ├── Destructuring assignment
| ├── Spread/rest operator
| ├── Arrow functions
| ├── Classes
| ├── let and const
| ├── Default parameters
| ├── Modules
| └── Promises
|
|── Web APIs
| ├── Local Storage
| ├── Session Storage
| └── Web Storage API
|
|── Libraries and Frameworks
| ├── React
| ├── Angular
| └── Vue.js
||── Debugging
| ├── Console.log()
| ├── Breakpoints
| └── DevTools
|
|── Others
| ├── Closures
| ├── Callbacks
| ├── Prototypes
| ├── this keyword
| ├── Hoisting
| └── Strict mode
|
| END __

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Use cases of top programming languages

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