010-ml-interview-resources

ML resources

1. Machine Learning Algorithms

General resources: (revise these after completing the below resources)

• Krish Naik ML playlist
• link

List of algorithms:

1. Linear Regression
   1. Videos
   2. Article
   3. Questions
2. Multiple Linear Regression
   1. Videos
   2. Questions
3. Lasso and Ridge
   1. Article)
   2. Questions
4. Logistic Regression
   1. Videos
   2. Article
   3. Questions
   4. additional Videos
5. KNN
   1. Video
   2. [Article](https://blog.usejournal.com/a-quick-introduction-to-k-nearest-neighbors-algorithm-62214cea29c7#:~:text=KNN algorithm is one of,the most used learning algorithms.&text=KNN is a non-parametric,of a new sample point.))
   3. Questions
6. SVM
   1. Video
   2. Videos
   3. Article
   4. Questions
   5. Questions - 2
7. Decision Tree
   1. Videos
   2. Article
   3. Article-2
   4. Questions
8. PCA and Dimensionality reduction
   1. Video
   2. Article
   3. Questions
   4. Questions - 2
9. Ensemble Learning
   1. Article
   2. Videos
   3. Questions
10. Random Forest
    1. Video
    2. Article
    3. [Article-2](https://builtin.com/data-science/random-forest-algorithm#:~:text=Put simply%3A random forest builds,more accurate and stable prediction.&text=Random forest has nearly the,tree or a bagging classifier.&text=Random forest adds additional randomness,model%2C while growing the trees.))
    4. Question
11. Adaboost - Article
12. GBM - Article
13. XGBM - Article
14. LightGBM - Article
15. CatBoost - Article
16. K-Means Clustering
    1. Video
    2. Questions
    3. Questions-2
17. EM algorithm - Video

2. Machine Learning concepts

• EDA
• Data Exploration
• **Data Exploration-2**
• Feature Encoding
• Feature Engineering
• Feature selection
• Feature selection -2
• Imbalanced Data
  • Article - 1
  • Article - 2
  • Article - 3
  • Article - 4
• **Evaluation Metrics , Video**
• Bias variance Tradeoff
• Tuning with GridSearch and RandomizedSearch
• Cross-Validation

3. Statistics and Probability

• Article
• All the concepts mentioned in the video → **link →** enough for interview preperation
• Search the concept on Reddit - eli5 → example: Search “z-score eli5”
• Questions
• Questions - 2

5. SQL

(very important part of the whole interview process)

• Article
• Practice all the 12 tutorials here - **https://sqlzoo.net/**
• Questions
• Questions - 2
• Questions - 3

6. Deep learning basics

• Understand this playlist completely → link
• What is CNN?
• What is RNN?
• What is LSTM?

7. Product and Experiment design

1. A/B Testing
   1. Article
   2. Questions

8. Python & Data structures and algorithms

• **Python Interview questions → imporatnt**
• Numpy - Video, **Questions → imporatnt**
• Pandas - Video, **Questions, Questions → imporatnt**
• Matplotlib - Video-1, **Video-2 → imporatnt**
• Arrays, Linkedlists, Trees, Heaps, Stacks and Queues (only if time is left) - HackerRank YouTube
• DFS, BFS, Binary search, recursion, bubble sort, merge sort, quick sort (only if time is left)
• **Coding Interview questions - only if time left**

Additional Resources:- (read these after completing the below resources)

• Interview questions (all-at-one place)
• Questions
• Questions - 2
• Questions - 3
• KDnuggets study guide
• 100 page ML book