The amateur’s guide to explore machine learning#
Hey there!
Welcome aboard!
So, here’s the scoop: I put together this documentation while embarking on my epic journey through the realms of machine learning and data science.
But, here’s the thing: I’ve got a bit of a scatterbrain, and I tend to misplace my logic, methods, algorithms, and, oh, the endless lines of code. 🤯
So, to keep all the genius stuff in one handy place, I’ve crafted this treasure trove. It’s like my own personal cheat sheet to quickly dive into topics when I need ‘em. 📚
Now, let me spill the beans on my secret sauce. Most of the content in here comes from a bunch of free courses, juicy videos, brainy research papers, and some good old dusty books. 📖
And, just in case I’ve messed up the credits, don’t be shy – give me a holler. I’ll make things right ASAP! 🧐
Let’s dive into the wonderful world of ML and data science together, shall we? 🚀🤖📈
Contact Me#
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- Understanding SVD
- U & V orthogonality (Unitary matrices)
- Eckart-Young Theorem (Low rank approximation)
- SVD and Correlations
- Method of Snapshots (SVD using eigen values and vectors)
- Vector outer product
- SVD and Image Compression
- Log and Cumulative plots
- Rotation matrix
- Unitary transformation
- Principal Component Analysis
- SVD,Linear Systems and Least Square
- Multi-Armed Bandit (k-Armed Bandit)
- References
- Introduction
- Stochastic Bandits
- Arms : \(K\)
- Reward Distribution : \(D_a\)
- Best Mean Reward/ Optimum Reward : \(\mu^*\)
- Gap in Arm : \(\Delta(a)\)
- Best Arm/ Optimal Arm : \(a^*\)
- Regret : \(R(T)\)
- Model
- Only Exploration
- Uniform Exploration
- Alternative Sequentially Exploration & Exploitation
- Epsilon (\(\epsilon\)) Greedy
- 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