- Where: ISME-Bangalore
- When: Oct/Nov 2019
- Who: Anush Sankaran
ML 1 - https://medium.com/@vaishakh.nambiar10/machine-learning-bcc5060e504c
ML 2 - https://medium.com/@vaishakh.nambiar10/linear-regression-vs-logistic-regression-6703f570bac7
Coding Done.
ML 3 - https://medium.com/@vaishakh.nambiar10/neural-network-900ea28eff48
ML Assignment - https://github.com/vaisakhnambiar/ISME-Machine-Learning
ML 4 - https://medium.com/@vaishakh.nambiar10/kernel-trick-svm-1f1f01f89fd
ML 5 - https://medium.com/@vaishakh.nambiar10/decision-trees-random-forest-7efc8f212536
ML End-Term Project - https://github.com/vaisakhnambiar/ISME-Machine-Learning
| Date | Topic | Slides | Notes |
|---|---|---|---|
| 11th October, 2019 | Intro to ML, Discovering ML Use Cases & ML in Business | slides | |
| 18th October, 2019 | Python- Hands On, Supervised Learning & Regression | slides | Code Ex 1 Code Ex 2 |
| Neural Network - 1, Neural Network -2 & Hands ON | slides | Code Ex 3 Code Ex 4 |
|
| Kernel Learning & SVM, Practical Advice for ML projects. | slides | ||
| Boosting, Decision Trees, Random Forest, & xgBoost | slides | ||
| Unsupervised Learning, Clustering & Dimensionality Reduction | slides | ||
| Time Series Data Analysis, Imputation & Prediction Systems | slides | ||
| ML Use Cases from Products & Research | slides |
Multiple references are borrowed from different sources of internet and different other courses, and they have better slides! With huge respects to their slides, hard work, and efforts, I acknowledge them and only makes sense to reuse some part of their slides!