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Machine & Deep Learning Compendium
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The Machine & Deep Learning Compendium
Types Of Machine Learning
Data Science
Data Science Tools
Management
Data Science Management
Calculus
Probability & Statistics
Probability
Feature Types
Features
Calibration
Multi Label Classification
Distribution
Distribution Transformation
Information Theory
Game Theory
Datasets
Dataset Confidence
Normalization & Scaling
Regularization
Validation
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
Natural Language Processing
Graphs
Deep Learning
Experimental Design
Product
Business Domains For Data Science
MLOPs Engineering
Model Formats
Data Engineering
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GitBook
Data Science Management
INTERVIEW Qs
1.
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40 questions on ensembles
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2.
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30 on trees
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3.
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30 on knns
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Politics
1.
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The most difficult thing in ds, politics
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HIRING / RECRUITING
1.
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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.
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Design docs at google
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LEGAL & CONTRACTS
1.
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(FAST) Advisory board saas agreement
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General
1.
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The Care and Feeding of Data Scientists - O'reilly
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Due Diligence
1.
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by Inbal Budowski Tal
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Calculus
Last modified
2mo ago
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Contents
INTERVIEW Qs
Politics
HIRING / RECRUITING
WRITING DOCS
LEGAL & CONTRACTS
General
Due Diligence