Machine Learning
Machine learning in a Nutshell:
- Data: Raw Environment
- Model: Maps input --> Output
- Learning: Optimizes Models by finding better parameters to models (patterns).
- Predictor: A MLA that is a system that makes predictions
- We Represent Data as Vectors
- Choose a Model
- Optimize using numerical methods
The mathematics needed:
- Linear Algebra
- Systems of Equations
- Matrices
- Vector Spaces
- Linear Mappings
- Affine Spaces
- Analytic Geometry
- Matrix Decomposition
- Vector Calculus
- Probability Distributions
- Continuous Optimisation
- Models vs Data
- Linear Regression
- Dimensionality
- Principal Component Analysis
- Density Estimation (Gaussian Mixture Models
- Classification with Support Vector Machines
Sources, Links and Reading Lists
- The 100pg ML Book
- Python --- SciPy --- NumPy
- building-machine-learning-systems-in-python
- Metaoptimize
- Stack Exchange
- Two To Real
- PacktPub
- (Numerical Linear Algebra for Programmers | New Book Chapter Available)
- How Neural Networks Work: 3h50min
- Machine Learning Fundamentals: Bias and Variance
- machine learning and javascript - w3schools
- machinelearningjs
http://thevikidtruth.com/wiki/?machinelearning
03aug22 | admin |