Machine Learning
Explained to your Grandma
Quentin Fayet
What ?
"Field of study that gives computers the ability to learn without being explicitly programmed"
- Arthur Samuel
What ?
Based of mathematical fields of study :
- Probabilistics
- Statistics
- Decision theory
- Information theory
- Control theory
- Optimization
- Connectionism
- ...
Why ?
- Finance : Automated trading, fraud detection
- Insurance : Fraud detection,
- Artificial Intelligence
- Security : Facial recognition
- Aeronautics : Incident prevention
Resolving problems where human brain is not enough efficient.
How ?
Inputs
Trained model
Outputs
Model
Mathematical law / abstraction of a problem
Training
- Supervised : Each set of inputs comes with the expected outputs
- Unsupervised : Expected outputs are not provided
- Semi-supervised : Some inputs have expected outputs, some don't
Over / Under fitting
- Overfitting : The model is too specific to the training data
- Underfitting : The model is too generalistic
Features
Measurable property contained into an input
What kind ?
Machine Learning algorithms belong to different families :
- Regressions
- Clusterisations / classifications (binary/multiclass)
- Regularizations
- Decision trees
- Dimensionality reduction
- Features selection
- Neural networks
- ...
Machine / Deep Learning
Machine Learning
Deep Learning
Deep Learning
Attempt to provide high-level abstractions of the data
Dataviz
One of the most important thing to do before using machine learning is to visualize the data.
Dataviz(ualization) is almost a science itself.
Visualization of 2D/3D data is easy. What about 100D data ?
Dataviz
What tools ?
Specialized languages :
- R (language for statistical processing)
- Matlab / Octave (Language for mathematic processing)
Languages and libraries :
- Python :
- Matplotlib (plotting)
- Numpy / SciPy (science computing)
- Scikit-learn (Machine / Deep learning)
- Pandas (Data analysis)
Resources
- Andrew Ng : Machine Learning (Stanford University)
https://coursera.org/learn/machine-learning
- Machine Learning Summer School
https://www.youtube.com/playlist?list=PLZSO_6-bSqHQCIYxE3ycGLXHMjK3XV7Iz
- KDnuggets
- LyonTech Hub (Slack)
Resources
Available at my desk :
- Pattern Recognition and Machine Learning (Bishop)
- The Elements of Statistical Learning (Trevor Hastie, Robert Tibshirani, Jerome Friedman)
- Data Science from Scratch 1Ed (Joel Grus)
- Machine Learning for Hackers 1Ed (Drew Conway, John Myles White)
Machine Learning Explained to your grandma
By quentinfayet
Machine Learning Explained to your grandma
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