# 《machine-learning-mindmap》
1 Machine Learning Process
（Daniel Martinez）

## Data

### Build Datasets

#### Machine Learning is math. In specific,
performing Linear Algebra on Matrices. Our
data values must be numeric.

## Model

### Select Algorithm based on question and
data available

## Cost Function

### The cost function will provide a measure of how far my algorithm and
its parameters are from accurately representing my training data.

### Sometimes referred to as Cost or Loss function when the goal is to
minimise it, or Objective function when the goal is to maximise it.

## Optimization

### Having selected a cost function, we need a method to minimise the Cost function, or
maximise the Objective function. Typically this is done by Gradient Descent or Stochastic
Gradient Descent.

## Tuning

### Different Algorithms have different Hyperparameters, which will affect the
algorithms performance. There are multiple methods for Hyperparameter
Tuning, such as Grid and Random search.

## Results and Benchmarking

### Analyse the performance of each algorithms and
discuss results.

### Are the results good enough for
production?

### Is the ML algorithm training
and inference completing in a
reasonable timeframe?

## Scaling

### How does my algorithm scales for both training and inference?

## Deployment and
Operationalisation

### How can feature manipulation be done for training and inference in real-time?

### How to make sure that the algorithm is retrained periodically and deployed into production?

### How will the ML algorithms be integrated with other systems?

## Infrastructure

### Can the infrastructure running the machine learning process scale?

### How is access to the ML algorithm provided? REST API? SDK?

### Is the infrastructure adapter to the algorithm we are running? Should GPU’s be considered rather than CPUs’?

## Question

### How much, or how many of these?

### How can these elements be grouped?

## Direction

### SaaS - Pre-built Machine Learning models

### Data Science and Applied Machine
Learning

#### Tools: Jupiter / Datalab / Zeppelin

### Machine Learning Research