Important questions to ace your machine learning interview with an approach to answer:
1. Machine Learning Project Lifecycle:
- Define the problem
- Gather and preprocess data
- Choose a model and train it
- Evaluate model performance
- Tune and optimize the model
- Deploy and maintain the model
2. Supervised vs Unsupervised Learning:
- Supervised Learning: Uses labeled data for training (e.g., predicting house prices from features).
- Unsupervised Learning: Uses unlabeled data to find patterns or groupings (e.g., clustering customer segments).
3. Evaluation Metrics for Regression:
- Mean Absolute Error (MAE)
- Mean Squared Error (MSE)
- Root Mean Squared Error (RMSE)
- R-squared (coefficient of determination)
4. Overfitting and Prevention:
- Overfitting: Model learns the noise instead of the underlying pattern.
- Prevention: Use simpler models, cross-validation, regularization.
5. Bias-Variance Tradeoff:
- Balancing error due to bias (underfitting) and variance (overfitting) to find an optimal model complexity.
6. Cross-Validation:
- Technique to assess model performance by splitting data into multiple subsets for training and validation.
7. Feature Selection Techniques:
- Filter methods (e.g., correlation analysis)
- Wrapper methods (e.g., recursive feature elimination)
- Embedded methods (e.g., Lasso regularization)
8. Assumptions of Linear Regression:
- Linearity
- Independence of errors
- Homoscedasticity (constant variance)
- No multicollinearity
9. Regularization in Linear Models:
- Adds a penalty term to the loss function to prevent overfitting by shrinking coefficients.
10. Classification vs Regression:
- Classification: Predicts a categorical outcome (e.g., class labels).
- Regression: Predicts a continuous numerical outcome (e.g., house price).
11. Dimensionality Reduction Algorithms:
- Principal Component Analysis (PCA)
- t-Distributed Stochastic Neighbor Embedding (t-SNE)
12. Decision Tree:
- Tree-like model where internal nodes represent features, branches represent decisions, and leaf nodes represent outcomes.
13. Ensemble Methods:
- Combine predictions from multiple models to improve accuracy (e.g., Random Forest, Gradient Boosting).
14. Handling Missing or Corrupted Data:
- Imputation (e.g., mean substitution)
- Removing rows or columns with missing data
- Using algorithms robust to missing values
15. Kernels in Support Vector Machines (SVM):
- Linear kernel
- Polynomial kernel
- Radial Basis Function (RBF) kernel
Data Science Interview Resources
๐๐
https://topmate.io/coding/914624
Like for more ๐
1. Machine Learning Project Lifecycle:
- Define the problem
- Gather and preprocess data
- Choose a model and train it
- Evaluate model performance
- Tune and optimize the model
- Deploy and maintain the model
2. Supervised vs Unsupervised Learning:
- Supervised Learning: Uses labeled data for training (e.g., predicting house prices from features).
- Unsupervised Learning: Uses unlabeled data to find patterns or groupings (e.g., clustering customer segments).
3. Evaluation Metrics for Regression:
- Mean Absolute Error (MAE)
- Mean Squared Error (MSE)
- Root Mean Squared Error (RMSE)
- R-squared (coefficient of determination)
4. Overfitting and Prevention:
- Overfitting: Model learns the noise instead of the underlying pattern.
- Prevention: Use simpler models, cross-validation, regularization.
5. Bias-Variance Tradeoff:
- Balancing error due to bias (underfitting) and variance (overfitting) to find an optimal model complexity.
6. Cross-Validation:
- Technique to assess model performance by splitting data into multiple subsets for training and validation.
7. Feature Selection Techniques:
- Filter methods (e.g., correlation analysis)
- Wrapper methods (e.g., recursive feature elimination)
- Embedded methods (e.g., Lasso regularization)
8. Assumptions of Linear Regression:
- Linearity
- Independence of errors
- Homoscedasticity (constant variance)
- No multicollinearity
9. Regularization in Linear Models:
- Adds a penalty term to the loss function to prevent overfitting by shrinking coefficients.
10. Classification vs Regression:
- Classification: Predicts a categorical outcome (e.g., class labels).
- Regression: Predicts a continuous numerical outcome (e.g., house price).
11. Dimensionality Reduction Algorithms:
- Principal Component Analysis (PCA)
- t-Distributed Stochastic Neighbor Embedding (t-SNE)
12. Decision Tree:
- Tree-like model where internal nodes represent features, branches represent decisions, and leaf nodes represent outcomes.
13. Ensemble Methods:
- Combine predictions from multiple models to improve accuracy (e.g., Random Forest, Gradient Boosting).
14. Handling Missing or Corrupted Data:
- Imputation (e.g., mean substitution)
- Removing rows or columns with missing data
- Using algorithms robust to missing values
15. Kernels in Support Vector Machines (SVM):
- Linear kernel
- Polynomial kernel
- Radial Basis Function (RBF) kernel
Data Science Interview Resources
๐๐
https://topmate.io/coding/914624
Like for more ๐
๐5โค1
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Natural Language Processing Projects.pdf
13.2 MB
Natural Language Processing Projects
Akshay Kulkarni, 2022
Akshay Kulkarni, 2022
Python Machine Learning Projects.pdf
871.9 KB
Python Machine Learning Projects
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5.6 MB
R Projects for Dummies
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Learning Kotlin.pdf
1.3 MB
Learning Kotlin
Stack Overflow contributors
Stack Overflow contributors
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Currently it's for working professionals only, I will update once we launch it for students as well
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Programming Languages & What Theyโre Really Good At
Python ๐ โ Data analysis, automation, AI/ML
Java โ โ Android apps, enterprise software
JavaScript โก โ Interactive websites, full-stack apps
C++ โ๏ธ โ Game development, system-level software
C# ๐ฎ โ Unity games, Windows apps
R ๐ โ Statistical analysis, data visualization
Go ๐ โ Fast APIs, cloud-native apps
PHP ๐ โ WordPress, backend for websites
Swift ๐ โ iOS/macOS apps
Kotlin ๐ฑ โ Modern Android development
Python ๐ โ Data analysis, automation, AI/ML
Java โ โ Android apps, enterprise software
JavaScript โก โ Interactive websites, full-stack apps
C++ โ๏ธ โ Game development, system-level software
C# ๐ฎ โ Unity games, Windows apps
R ๐ โ Statistical analysis, data visualization
Go ๐ โ Fast APIs, cloud-native apps
PHP ๐ โ WordPress, backend for websites
Swift ๐ โ iOS/macOS apps
Kotlin ๐ฑ โ Modern Android development
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List of AI Project Ideas ๐จ๐ปโ๐ป๐ค -
Beginner Projects
๐น Sentiment Analyzer
๐น Image Classifier
๐น Spam Detection System
๐น Face Detection
๐น Chatbot (Rule-based)
๐น Movie Recommendation System
๐น Handwritten Digit Recognition
๐น Speech-to-Text Converter
๐น AI-Powered Calculator
๐น AI Hangman Game
Intermediate Projects
๐ธ AI Virtual Assistant
๐ธ Fake News Detector
๐ธ Music Genre Classification
๐ธ AI Resume Screener
๐ธ Style Transfer App
๐ธ Real-Time Object Detection
๐ธ Chatbot with Memory
๐ธ Autocorrect Tool
๐ธ Face Recognition Attendance System
๐ธ AI Sudoku Solver
Advanced Projects
๐บ AI Stock Predictor
๐บ AI Writer (GPT-based)
๐บ AI-powered Resume Builder
๐บ Deepfake Generator
๐บ AI Lawyer Assistant
๐บ AI-Powered Medical Diagnosis
๐บ AI-based Game Bot
๐บ Custom Voice Cloning
๐บ Multi-modal AI App
๐บ AI Research Paper Summarizer
Join for more: https://t.me/machinelearning_deeplearning
Beginner Projects
๐น Sentiment Analyzer
๐น Image Classifier
๐น Spam Detection System
๐น Face Detection
๐น Chatbot (Rule-based)
๐น Movie Recommendation System
๐น Handwritten Digit Recognition
๐น Speech-to-Text Converter
๐น AI-Powered Calculator
๐น AI Hangman Game
Intermediate Projects
๐ธ AI Virtual Assistant
๐ธ Fake News Detector
๐ธ Music Genre Classification
๐ธ AI Resume Screener
๐ธ Style Transfer App
๐ธ Real-Time Object Detection
๐ธ Chatbot with Memory
๐ธ Autocorrect Tool
๐ธ Face Recognition Attendance System
๐ธ AI Sudoku Solver
Advanced Projects
๐บ AI Stock Predictor
๐บ AI Writer (GPT-based)
๐บ AI-powered Resume Builder
๐บ Deepfake Generator
๐บ AI Lawyer Assistant
๐บ AI-Powered Medical Diagnosis
๐บ AI-based Game Bot
๐บ Custom Voice Cloning
๐บ Multi-modal AI App
๐บ AI Research Paper Summarizer
Join for more: https://t.me/machinelearning_deeplearning
โค2๐2๐1
List of most asked Programming Interview Questions.
Arrays
- How is an array sorted using quicksort?
- How do you reverse an array?
- How do you remove duplicates from an array?
- How do you find the 2nd largest number in an unsorted integer array?
Linked Lists
- How do you find the length of a linked list?
- How do you reverse a linked list?
- How do you find the third node from the end?
- How are duplicate nodes removed in an unsorted linked list?
Strings
- How do you check if a string contains only digits?
- How can a given string be reversed?
- How do you find the first non-repeated character?
- How do you find duplicate characters in strings?
Binary Trees
- How are all leaves of a binary tree printed?
- How do you check if a tree is a binary search tree?
- How is a binary search tree implemented?
- Find the lowest common ancestor in a binary tree?
Graph
- How to detect a cycle in a directed graph?
- How to detect a cycle in an undirected graph?
- Find the total number of strongly connected components?
- Find whether a path exists between two nodes of a graph?
- Find the minimum number of swaps required to sort an array.
Dynamic Programming
1. Find the longest common subsequence?
2. Find the longest common substring?
3. Coin change problem?
4. Box stacking problem?
5. Count the number of ways to cover a distance?
React with โค๏ธ for the detailed answers
Arrays
- How is an array sorted using quicksort?
- How do you reverse an array?
- How do you remove duplicates from an array?
- How do you find the 2nd largest number in an unsorted integer array?
Linked Lists
- How do you find the length of a linked list?
- How do you reverse a linked list?
- How do you find the third node from the end?
- How are duplicate nodes removed in an unsorted linked list?
Strings
- How do you check if a string contains only digits?
- How can a given string be reversed?
- How do you find the first non-repeated character?
- How do you find duplicate characters in strings?
Binary Trees
- How are all leaves of a binary tree printed?
- How do you check if a tree is a binary search tree?
- How is a binary search tree implemented?
- Find the lowest common ancestor in a binary tree?
Graph
- How to detect a cycle in a directed graph?
- How to detect a cycle in an undirected graph?
- Find the total number of strongly connected components?
- Find whether a path exists between two nodes of a graph?
- Find the minimum number of swaps required to sort an array.
Dynamic Programming
1. Find the longest common subsequence?
2. Find the longest common substring?
3. Coin change problem?
4. Box stacking problem?
5. Count the number of ways to cover a distance?
React with โค๏ธ for the detailed answers
โค5๐5
List of Frontend Project Ideas ๐ก๐จ๐ปโ๐ป
Beginner Projects
๐น Personal Portfolio Website
๐น Responsive Landing Page
๐น Simple Calculator
๐น To-Do List App
๐น Weather App
Intermediate Projects
๐ธ Blog Website
๐ธ E-commerce Product Page
๐ธ Recipe Finder App
๐ธ Interactive Chat App
๐ธ Music Player
Advanced Projects
๐บ Social Media Dashboard
๐บ Real-time Chat Application
๐บ Multi-page E-commerce Website
๐บ Dynamic Data Visualization Dashboard
React โค๏ธ for more
Beginner Projects
๐น Personal Portfolio Website
๐น Responsive Landing Page
๐น Simple Calculator
๐น To-Do List App
๐น Weather App
Intermediate Projects
๐ธ Blog Website
๐ธ E-commerce Product Page
๐ธ Recipe Finder App
๐ธ Interactive Chat App
๐ธ Music Player
Advanced Projects
๐บ Social Media Dashboard
๐บ Real-time Chat Application
๐บ Multi-page E-commerce Website
๐บ Dynamic Data Visualization Dashboard
React โค๏ธ for more
โค4๐1
Tips for solving leetcode codings interview problems
If input array is sorted then
- Binary search
- Two pointers
If asked for all permutations/subsets then
- Backtracking
If given a tree then
- DFS
- BFS
If given a graph then
- DFS
- BFS
If given a linked list then
- Two pointers
If recursion is banned then
- Stack
If must solve in-place then
- Swap corresponding values
- Store one or more different values in the same pointer
If asked for maximum/minimum subarray/subset/options then
- Dynamic programming
If asked for top/least K items then
- Heap
If asked for common strings then
- Map
- Trie
Else
- Map/Set for O(1) time & O(n) space
- Sort input for O(nlogn) time and O(1) space
If input array is sorted then
- Binary search
- Two pointers
If asked for all permutations/subsets then
- Backtracking
If given a tree then
- DFS
- BFS
If given a graph then
- DFS
- BFS
If given a linked list then
- Two pointers
If recursion is banned then
- Stack
If must solve in-place then
- Swap corresponding values
- Store one or more different values in the same pointer
If asked for maximum/minimum subarray/subset/options then
- Dynamic programming
If asked for top/least K items then
- Heap
If asked for common strings then
- Map
- Trie
Else
- Map/Set for O(1) time & O(n) space
- Sort input for O(nlogn) time and O(1) space
๐4โค1