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Machine & Deep Learning Compendium
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The Machine & Deep Learning Compendium
The Ops Compendium
Types Of Machine Learning
Data Science
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Data Science Management
Calculus
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Probability
Feature Types
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Calibration
Multi Label Classification
Distribution
Distribution Transformation
Information Theory
Game Theory
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Dataset Confidence
Normalization & Scaling
Regularization
Datasets Reliability & Correctness
Data & Model Tests
Fairness, Accountability, and Transparency
Interpretable & Explainable AI (XAI)
Meta Learning
Evaluation Metrics
Benchmarking
Hyper Parameter Optimization
Multi CPU Processing
Algorithms 101
Training Strategies
Classic Machine Learning
Label Algorithms
Clustering Algorithms
Anomaly Detection
Decision Trees
Active Learning Algorithms
Linear Separator Algorithms
Ensembles
Reinforcement Learning
Incremental Learning
Dimensionality Reduction Methods
Genetic Algorithms & Genetic Programming
Learning Classifier Systems
Recommender Systems
Timeseries
Fourier Transform
Digital Signal Processing (DSP)
Propensity Score Matching
Diffusion models
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Business Domains For Data Science
MLOps (www.OpsCompendium.com)
DataOps (www.OpsCompendium.com)
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Data Science Management
INTERVIEW Qs
1.
40 questions on ensembles
2.
30 on trees
3.
30 on knns
Politics
1.
The most difficult thing in ds, politics
HIRING / RECRUITING
1.
Data engineer skills
on medium
1.
Coding (Typically Python)
2.
SQL
3.
Database design
4.
Data architecture/big data technologies
5.
Soft skills
WRITING DOCS
1.
Design docs at google
LEGAL & CONTRACTS
1.
(FAST) Advisory board saas agreement
General
1.
The Care and Feeding of Data Scientists - O'reilly
Due Diligence
1.
by Inbal Budowski Tal
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1yr ago