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
Kernels
Implementation

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

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