2. ML/Data Coding :robot:

ML coding module may or may not exist in particular companies interviews. The good news is that, there are only a limited number of ML algorithms that candidates are expected to be able to code. The most common ones include:

ML Algorithms

• Linear regression (code) :white_check_mark:

• Logistic regression (code) :white_check_mark:

• K-means clustering (code) :white_check_mark:

• K-nearest neighbors (code 1 - code 2) :white_check_mark:

• Decision trees (code) :white_check_mark:

• Linear SVM (code)

• Neural networks
  • Perceptron (code)
  • FeedForward NN (code)
  • Softmax (code)
  • Convolution (code)
  • CNN
  • RNN

Sampling

• stratified sampling (link)
• uniform sampling
• reservoir sampling
• sampling multinomial distribution
• random generator

NLP algorithms

• bigrams
• tf-idf

Other

• Random int in range (link1, link2)
• Triangle closing
• Meeting point

Sample codes

• You can find some sample codes under the Notebooks.