Holberton School Machine Learning Projects
This repository contains the Machine Learning projects that we've done at Holberton School which includes:
- The important Mathematics Algorithms needed for the ML process:
- Linear Algebra
- Calculus
- Probability
- Machine Learning Algorithms:
- Classification
- Regularization
- Optimization
- Error Analysis
- Convolutional Neural Networks
- Deep Convolutional
- Architectures
- Transfer Learning
- Object Detection
- Face Verification
- Neural Style Transfer
- Recurrent Neural Networks
- Deep Recurrent Architectures
- Natural Language Processing
- Time Series Analysis
- Dimensionality Reduction
- Clustering
- Hidden Markov Models
- Neural Style Transfer
Let's define it first in a simple way as Arthur Samuel described in 1959:
👉 Field of study that gives computers the ability to learn without being explicitly programmed
The modern definition is by Tom M. Mitchell:
👉 A computer program is said to learn from experience E with respet to some class of tasks T and performane measure P, if its performance at tasks in T, as measured by P, improves with experience E
🧑🏻💻 Example: playing checkers
E is the experience of playing many games of checkers
T is the task of playing checkers
P is the probability that the program will win the next game
By Khouloud Software engineer at HolbertonSchool®️