Coursera Machine Learning course (offered by Stanford University)

Assignments, lecture slides, and files have been included in this repository.

Course Info

Machine Learning is a kind of Artificial intelligence that allows software applications to become more efficient at perdicting outcomes without being explicitly programmad. In this course, you will learn and implement machine learning algorithms from scrath.

This course provides a broad introduction to machine learning, data mining, and statistical pattern recognition. Topics include: (i) Supervised learning (parametric/non-parametric algorithms, support vector machines, kernels, neural networks). (ii) Unsupervised learning (clustering, dimensionality reduction, recommender systems, deep learning). (iii) Best practices in machine learning (bias/variance theory; innovation process in machine learning and AI). The course will also draw from numerous case studies and applications, so that you'll also learn how to apply learning algorithms to building smart robots (perception, control), text understanding (web search, anti-spam), computer vision, medical informatics, audio, database mining, and other areas.

Credits

This repository is my work for this course. This has been created to help learners when they are stuck in programming assignments. The code base, quiz questions, and diagrams are taken from the Machine Learning course.

Installation

clone this repository

  !git clone https://github.com/faizan1234567/Machine-Learning
  cd Machine-Learning

Download MATLAB-2019 or later version/Ocative, cd into the Machine Learning folder for accessing files.

Programming Assignments

• Week 1

  • Introduction to Machine Learning (lecture slides)

• Week 2

  • Linear Regression

• Week 3

  • Logistic Regression

• Week 4

  • Multi-class Classification and Neural Networks

• Week 5

  • Neural Networks Learning

• Week 6

  • Regularized Linear Regression and Bais v.s. Variance

• Week 7

  • Support Vector Machines

• Week 8

  • K-means Clustering and Principal Component Analysis

• Week 9

  • Anomaly Detection and Recommender Systems

• Week 10

  • Learning with large datasets (lecture slides)

• Week 11

  • Application Example: Photo OCR (lecture slides)

Disclaimer

Programming assignments solutions has been provided for reference. Please don't copy and paste, as Programming assignments are fairly simple if you have followed the lectures. This solution is for hints and to keep you motivated to learn. If you are stuck in your programming assignments, please take it as help.