Presented herein are my solutions to the programming assignments and quizzes for the Machine Learning (ML) course offered by Stanford University on Coursera, instructed by the esteemed Andrew Ng. Upon successful completion of this course, a comprehensive understanding of various ML algorithms can be acquired. The projects were implemented using both Matlab and Python programming languages, with both implementations available in this repository, accompanied by the corresponding source codes in their respective formats. I encourage attempting to solve the assignments independently at first, but should any challenges arise, you are welcome to refer to the provided code.
- About this Course
- What You Will Learn
- Applied Learning Project
- Certificate
- References
- License
This course provides a broad introduction to machine learning, datamining, and statistical pattern recognition. Topics include: Supervised learning (parametric/non-parametric algorithms, support vector machines, kernels, neural networks). Unsupervised learning (clustering, dimensionality reduction, recommender systems, deep learning). 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.
- Build ML models with NumPy & scikit-learn, build & train supervised models for prediction & binary classification tasks (linear, logistic regression)
- Build & train a neural network with TensorFlow to perform multi-class classification, & build & use decision trees & tree ensemble methods
- Apply best practices for ML development & use unsupervised learning techniques for unsupervised learning including clustering & anomaly detection
- Build recommender systems with a collaborative filtering approach & a content-based deep learning method & build a deep reinforcement learning model
Upon completion of this Specialization, you will possess the skills to:
- Construct machine learning models in Python utilizing well-known libraries such as NumPy and scikit-learn.
- Develop and train supervised machine learning models for prediction and binary classification tasks, including linear regression and logistic regression.
- Design and train a neural network using TensorFlow to perform multi-class classification.
- Implement best practices in machine learning development to ensure that your models are capable of generalizing to real-world data and tasks.
- Construct and utilize decision trees and tree ensemble methods, including random forests and boosted trees.
- Employ unsupervised learning techniques, such as clustering and anomaly detection, for unsupervised learning scenarios.
- Create recommender systems using collaborative filtering approaches and content-based deep learning methods.
- Construct a deep reinforcement learning model."
Machine Learning by Stanford University (Final Certificate)
- Supervised Machine Learning: Regression and Classification
- Advanced Learning Algorithms
- Unsupervised Learning, Recommenders, Reinforcement Learning
This repository is licensed under the MIT License,which covers the terms and conditions for its use.