The amateur’s guide to explore machine learning#

Welcome.
I created this documentation as my exploration of machine learning & data science.
I usually forget logics, methods, algorithms and code. Hence to keep everything at one place so that I can go through the topic quickly as referenced.
Source of the content mostly from a free course, video, research paper and book (most of the references are mentioned with the topic).
If I haven’t mentioned your reference correctly then let me know, I’ll add it asap.
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Basics
- Basics of Statistics
- Covariance and Correlations
- Sampling
- Probability
- Distributions
- References
- Data Distributions
- Normal(Gaussian) Distribution (Continuous)
- Chi-squared Distribution (Continuous)
- Student’s t distribition (Continuous)
- F Distribution (Continuous)
- Uniform Distribution (Continuous)
- Beta Distribution (Continuous)
- Binomial Distribution (Discrete)
- Hypergeometric Distribution (Discrete)
- Poisson Distribution (Discrete)
- Exponential Distribution (Discrete)
- Weibull Distribution (Discrete)
- Points to note
- Probability Estimation
- Bias-Variance Tradeoff
Experimentation
Linear Models
Proximity Models
Tree Based and Ensemble Models
Image & Signal Procressing
Unsupervised
Feature Engineering
Intuition & Evaluation
- Vectors
- Algorithm’s Classification Boundaries
- Classification Evaluation Metrics
- References
- Conditions
- Confusion Matrix
- Errors
- Accuracy
- Precision
- Recall / Sensitivity / True Positive Rate (TPR) / Hit Rate
- F1-score
- Classification Report
- Specificity / Selectvity / True Negative Rate (TNR)
- Fall Out / False Positive Rate (FPR)/ False Alarm
- Miss rate / False Negative Rate (FNR)
- PRC (Precision-Recall Curve)
- ROC (Receiver Operating Characteristic Curve)
- Matrix Multiplication
Visualizations
Miscellaneous
Additional Docs