Notes for Deep Learning Specialization Courses led by Andrew Ng.

A document covering machine learning basics. 🤖📊

This repository is a recommended track, designed to get started with Machine Learning.

In this repository I implemented all assignments in python for the purpose of learning python

Andrew Ng's Machine Learning Course

The official source code to: Uncertainty Estimates of Predictions via a General Bias-Variance Decomposition (AISTATS'23)

An a bias-variance tradeoff of Sarsa vs. Expected Sarsa with experiments.

Chapter 2: Machine Learning Basics

Here, we implement regularized linear regression to predict the amount of water flowing out of a dam using the change of water level in a reservoir. In the next half, we go through some diagnostics of debugging learning algorithms and examine the effects of bias v.s. variance.

Machine Learning exercises in Python (Jupyter notebooks)

Machine learning with MATLAB/Octave, coding machine learning algorithms from scratch

A Mathematical Intuition behind Linear Regression Algorithm

Generalized Ridge Trace Plots for Ridge Regression

This is a simple python example to demonstrate bias variance

📊 📈 In depth explained my assignment solutions. Grade: 97.3%

A five-course specialization covering the foundations of Deep Learning, from building CNNs, RNNs & LSTMs to choosing model configurations & paramaters like Adam, Dropout, BatchNorm, Xavier/He initialization, and others.

Notes on Machine Learning with DataSets and Examples

3D property modeling using geostatistics

Collection of my notes from Udacity's Intro to Deep Learning--> Introduction to Neural Networks course.

Machine Learning models on Anomaly detection, Recommender system on movies based on IMDB dataset, Digit Identification using Logistic regression, Neural network based facial feature recognition, PCA, SVM based Spam filter, Logistic Regression - Nelder Mead