Machine Learning

Interview Questions

Everything - basic maths, ML, DL

• Questions on Linear Algebra, Numerical Optimization, Probability, Confidence Interval, Bias-Variance, Emperical Risk, VC Dimension, Model, Feature selection, Curse of Dimensionality, MLP, DL motivation, SVM, Bayesian, Regularization, ROC-AUC, Clustering, Dimensionality Reduction, Optimization, RNN, LSTM, Autoencoder, Representation Learning, Monte Carlo

Overview ML questions

• SQL, ML, Company related, General Knowledge ML

ML

• Top 10 FAQ on ML

Python, Coding

• Python Coding, Time Complexity and Case Studies

Preparation Guide

• How to answer questions on Machine Learning Basics, Machine Learning Coding, Applied Machine Learning Problems, Project-Based Machine Learning Questions

DL, Case Study

• CNN, NLP, LSTM, GANs, MLP, Case Study Questions

Resources

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Topics to Study

basic machine learning algorithms - summary

• Summary of supervised, semi-supervised, unsupervised and reinforcement
• All ML algorithms with basic explanation and code
• List of ML algorithms
• Advantages/Disadvantages/Uses of ML algorithms
• ML algorithms outline

cheat sheet

• Pyspark

cheat sheet

• Probability

cheat sheet

• Python, Pandas, Matplotlib, Scikit Learn

Machine Learning

Statistics

• Type I and Type II error
• Understanding AUC ROC

ML Algorithms

• Linear Regression
• Polynomial regression
• Linear Regression Assumptions
• Gradient Descent in Linear Regression
• [Video] Linear Regression
• Logistic Regression
• [Video] Logistic Regression
• Decision Tree
• [Video] Decision Tree
• SVM
• SVM loss function - hinge loss
• SVM loss function - hinge loss part 2
• Kernel trick in SVM
• [Video] SVM
• Naive Bayes
• [Video] Naive Bayes
• [Video] kNN
• kNN
• K-Means
• How to choose k in k-means?
• [Video] kMeans

Ensemble Algorithms

• Random Forest
• [Video] Random Forest
• Limitations of Random Forest Regression
• GBM regression
• GBM classification
• XGBoost - what, why, benefits
• XGBoost - intuition, performance, hyperparameters
• XGBoost - part I
• XGBoost - part II
• LightGBM
• LightGBM parameters and implementation
• Bootstrapped Aggregation (Bagging)
• AdaBoost
• Weighted Average (Blending)
• Stacked Generalization (Stacking)
• Gradient Boosting Machines (GBM)
• Gradient Boosted Regression Trees (GBRT)
• CatBoost

Dimensionality Reduction Algorithms

• Principal Component Analysis (PCA)
• Principal Component Analysis (PCA) part 2
• Principal Component Regression (PCR)
• t-SNE
• [Video] t-SNE
• Linear Discriminant Analysis (LDA)

Feature selection algorithms

• Summary: Filter - Information Gain, Chi Square, Fisher score, correlation coefficient, varience threshold, Mean Absolute Difference, dispersion ratio Wrapper - forward, backward, exhaustive feature selection, recursive elimination Embedded - L1, Random Forest Importance
• chi square
• chi square part 2
• information gain
• information gain part 2
• fisher score

Optimizers

• general guide

Model Explainability

• SHAP explanation
• LIME
• SGD

Regularization

• Regularization theory
• Regularization theory part 2
• Lasso vs Ridge theory
• [Video] Lasso vs Ridge
• [Video] Ridge
• [Video] Lasso

Cross-Validation

• CV theory
• CV code

Deep learning

Book on DL

• NN and DL

GAN

• Monitor GAN Training Progress and Identify Common Failure Modes
• How to Identify and Diagnose GAN Failure Modes
• Geometry Score

Activation Functions

• Linear
• When to use sigmoid, softmax, relu
• How to use activaion functions in hidden and output layer

Object Detection

• Object Detection using ResNet-50

NLP

• NLP course Stanford
• [SO] Common questions on NLP implementation
• History of NLP
• Latent Semantic Analysis
• Word2Vec
• BERT word embeddings
• BERT research concepts
• Positional Encoding in Transformer Models
• Transformer positional encoding layer - keras code
• attention mechanisms
• attention mechanisms from scratch
• LSTM vs RNN
• Embeddings
• Word Embeddings, Continuous bag of words, skip grams, word2vec, negative sampling
• Evaluation of translation quality - - BLEU for machine translation and ROUGE for summarization.
• Evaluating Language Models by OpenAI, DeepMind, Google, Microsoft
• Evaluation metrics - entropy, perplexity, bits per character/word
• Contrastive Predictive Coding
• NLP data augmentation
• handling rare words
• basic NLP questions 1
• basic NLP questions 2

RNN

• Forward pass, backward pass, gradients, loss, implementation
• Forward pass explained
• Backpropagation explained
• Sequene modelling interview questions
• teacher forcing
• NN vanishing gradient
• time series vanishing gradient

RetinaNet

• The intuition behind RetinaNet - Focal Loss
• [Paper] Feature Pyramid Networks for Object detection
• [Paper] Focal loss for Dense object detection

ResNet

• Concept

Time Series

• Time Series with ML

Courses

• MIT 6.034 Artificial Intelligence by Patrick Winston (23 lectures + 7 Mega-Recitations)
• Coursera - Machine Learning by Stanford University - online class by Andrew Ng (highly recommended)
• Undergraduate machine learning at UBC 2012 - by Nando de Freitas (33 lectures)
• Deep Learning at Oxford 2015 - by Nando de Freitas (16 lectures)
• CS231n - CNNs for Visual Recognition - by Fei-Fei Li and mainly Andrej Karpathy (Overview)
• Neural networks class - Université de Sherbrook by Hugo Larochelle (92 mostly short videos)
• Deep Learning Talk MLSS 2014 - by Yoshua Bengio at MLSS 2014 (3 Parts)

Videos

• Visualizing Data Using t-SNE - GoogleTechTalk by Laurens van der Maaten presenting t-SNE
• Visualizing and Understanding DNNs by Matt Zeiler presenting deconv-nets for visualizing DNNs
• Fun demo of CNN from ‘93 - Yann LeCun presenting his CNN for handwritten digits.
• MarI/O - NEAT applied on Super Mario World by SethBling
• The Art of neural networks - Mike Tyka talking about the evolution of art and programming

Blogs/Sites

• Andrej Karpathys Blog - Blog with a lot of well written articles mainly about CV and CNNs
• CS231n - corresponding site to the CS231n course from Standford university. Each topic covered in an article.
• UFLDL Tutorial - Unsupervised Feature Learning and Deep Learning Tutorials by Standford University
• Understanding LSTMs - Some nice write up about LSTM-Nets by Christopher Olah

Programming

Videos

• Introduction to DL with Python - Presentation by Alec Radford giving an overview of Deep Learning with Theano

Blogs/Sites

• Undocumented Matlab - One of the best Matlab related Sites I know.
• CheckiO - Online game-based learning of Python
• Theano Tutorial - Introduction to Theano (Python libary)
• Theano Examples - some more applied, though advanced tutorials.

General Scientific stuff

Videos

• How to write a Scientific Paper - Talk by Simon Peyton Jones