A summary of tools for Data Scientists going into MLOPs
The idea of this repository is to publish my summaries about tools that are useful for Machine Learning Engineers. The summaries are updated constantly.
Supoorting Topics
Supervised Models
- Linear Regression
- Lasso Regression
- Ridge Regression
- OLS - Ordinary Least Squares
- Naive Bayes
- SGD - Stochastic Gradient Descent
- SVM - Support Vector Machines
- Logistic Regression
- Decision Trees
- Perceptron
- Neural Networks
Unsupervised Models
Time Series Forecasting
- 2.1 ETS - Simple Exponential Smoothing Method
- 2.2 ETS - Holt's Linear Trend Method
- 2.3 ETS - Exponential Trend Method
- 2.4 ETS - Holt-Winters Seasonal Method
Reinforcement Learning
Causal Modelling
- Introduction
- Experimental Design and Statistical Controls
- Correlation
- Hypothesis and Probability
- Prediction and Proof
- Induction and Deduction
Systems Designs