Machine-Learning-Specialization-Coursera and Deep-Learning-Specialization-Coursera
These are complements that together cover the full Andrew Ng curriculum on Coursera—one focuses on the foundational Machine Learning Specialization while the other covers the subsequent Deep Learning Specialization, allowing learners to reference solutions across both courses sequentially.
About Machine-Learning-Specialization-Coursera
greyhatguy007/Machine-Learning-Specialization-Coursera
Contains Solutions and Notes for the Machine Learning Specialization By Stanford University and Deeplearning.ai - Coursera (2022) by Prof. Andrew NG
Organizes three specialization courses into structured Jupyter notebooks covering linear/logistic regression, neural networks, and unsupervised learning with hands-on labs implementing algorithms using NumPy, scikit-learn, and TensorFlow. Each week includes practice quizzes, optional labs demonstrating core concepts (gradient descent, vectorization, feature scaling), and graded programming assignments with complete solutions. The implementation emphasizes vectorized NumPy operations and comparison between manual gradient descent implementations and scikit-learn's optimized solvers.
About Deep-Learning-Specialization-Coursera
abdur75648/Deep-Learning-Specialization-Coursera
This repo contains the updated version of all the assignments/labs (done by me) of Deep Learning Specialization on Coursera by Andrew Ng. It includes building various deep learning models from scratch and implementing them for object detection, facial recognition, autonomous driving, neural machine translation, trigger word detection, etc.
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