From f0dccea0f0a16ac2454ae95e9ea986046b19c15b Mon Sep 17 00:00:00 2001 From: Paras-96 Date: Fri, 28 Mar 2025 13:08:33 +0530 Subject: [PATCH] Added three useful resource Added the How to begin your journey as a machine learning engineer? , Decision Tree in Machine Learning, RNN - Full Guide URLs, to the section - Getting Started, ML fundamentals, DL fundamentals respectively, providing useful insights to the aspiring tech professionals in the field of Machine Learning. Please let me know if further changes are required. Thank you! --- README.md | 3 +++ 1 file changed, 3 insertions(+) diff --git a/README.md b/README.md index 684df2e..07aebcd 100644 --- a/README.md +++ b/README.md @@ -50,6 +50,7 @@ Follow [News about AI projects](https://news.llmlab.io/) | FAANG companies actual MLE interviews | Read [interview stories](https://mlengineer.io/mlengineer-io-interview/home) | | Practice coding | [Leetcode questions by categories for MLE](https://mlengineer.io/common-leetcode-questions-by-categories-532b301130b) | | Advance topics | Read [advance topics](extra.md) | +| How to begin your journey as a machine learning engineer?| Read [Machine Learning Roadmap](https://www.scaler.com/blog/machine-learning-roadmap/) | @@ -111,6 +112,7 @@ Follow [News about AI projects](https://news.llmlab.io/) * [Quantile regression](https://www.youtube.com/watch?v=s203ScTy4xQ&t=954s) * [L1/L2 intuition](https://www.linkedin.com/pulse/intuitive-visual-explanation-differences-between-l1-l2-xiaoli-chen/) * [Decision tree and Random Forest fundamental](https://people.csail.mit.edu/dsontag/courses/ml16/slides/lecture11.pdf) +* [Decision tree in Machine Learning](https://www.appliedaicourse.com/blog/decision-tree-in-machine-learning/) * [Explain boosting](https://web.stanford.edu/~hastie/TALKS/boost.pdf) * [Least Square as Maximum Likelihood Estimator](https://www.youtube.com/watch?v=_-Gnu498s3o) * [Maximum Likelihood Estimator introduction](https://www.youtube.com/watch?v=WflqTUOvdik&t=15s) @@ -130,6 +132,7 @@ Follow [News about AI projects](https://news.llmlab.io/) * [Loss and optimization](http://cs231n.stanford.edu/slides/2020/lecture_3.pdf) * [Convolution Neural network notes](https://cs231n.github.io/convolutional-networks/) * [Recurrent Neural Networks](http://cs231n.stanford.edu/slides/2020/lecture_10.pdf) +* [RNN - Full Guide](https://www.appliedaicourse.com/blog/what-is-recurrent-neural-network-rnn/)