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ML Guide Book - Home

Probability & Statistics

  • Basics of Statistics
  • Probability Basics
  • Probability Distributions
  • Probability Estimation (MLE & MAP)

ML Basic Concepts

  • Sampling
  • Bias-Variance Tradeoff
  • Loss Functions
  • Covariance and Correlations

Experimentation

  • Experiments & Testing
  • Additional Statistics Examples

Linear Models

  • Perceptron
  • Linear Regression
  • Ridge(L2 Regularization) Regression
  • Lasso(L1 Regularization) Regression
  • Logistic Regression
  • Trend, Slope & Regression

SVM

  • Support Vector Machine
  • Kernel Methods

Proximity Models & Methods

  • Distance Metrics
  • K-Nearest Neighbors
  • Curse Of Dimensionality

Tree Based and Ensemble Models

  • Decision Tree Algorithm
  • Random Forest Algorithm
  • Boosting Methods

Unsupervised

  • Singular Value Decomposition
  • Principal Component Analysis
  • SVD,Linear Systems and Least Square
  • K-Means Clustering

RL

  • Multi-Armed Bandit (k-Armed Bandit)

Image & Signal Procressing

  • Convolution (Signal & Image)
  • Image Processing
  • Image Segmentation
  • Fourier Transform
  • Denoising data using Fast Fourier
  • Audio Processing

Feature Engineering

  • Weight of Evidence & Information Value
  • Moving Window Frame
  • Sparse Matrix
  • Feature Scaling Techniques

Intuition & Evaluation

  • Vectors
  • Algorithm’s Classification Boundaries
  • Classification Evaluation Metrics
  • Matrix Multiplication

Visualizations

  • Matplotlib Visualization
  • Live Visual
  • Plot 3D
  • Plotly Exploration

Miscellaneous

  • Statsmodels
  • Graph Network

Additional Docs

  • Reco Guide Book
  • Deep Learning Guide Book
  • NLP Guide Book
  • Python Guide Book
  • mightypy
  • graphpkg

Work In Progress

  • Bayes Estimation
  • Anomaly Detection using Scikit-Learn
  • Time Series Forecasting
  • Markov Chains

Index

H | M

H

  • HOME

M

  • MODULES

By Nishant Baheti