{"id":19316765,"url":"https://github.com/vubacktracking/coursera-machine-learning-specialization","last_synced_at":"2025-04-12T13:40:55.347Z","repository":{"id":192938761,"uuid":"662019976","full_name":"VuBacktracking/Coursera-Machine-Learning-Specialization","owner":"VuBacktracking","description":"Contains Optional Labs and Solutions of Programming Assignment for the Machine Learning Specialization By Stanford University and Deeplearning.ai - Coursera (2023) by Prof. Andrew 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Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"# [Machine Learning Specialization](https://www.coursera.org/specializations/machine-learning-introduction?#courses)\n\n\u003ccenter\u003e\n  \u003cimg src=\"https://github.com/vhoang1206/Coursera-Machine-Learning-Specialization/blob/main/Resources/Title.png\" alt=\"The title\"\u003e\n\u003c/center\u003e\nContains Optional Labs and Solutions for Programming Assignments for the \u003ca href = \"https://www.coursera.org/specializations/machine-learning-introduction?#outcomes\" target = \"_blank\"\u003eMachine Learning Specialization\u003c/a\u003e (Updated) by Prof. Andrew NG\n\n---\n## Skill you'll gain:\n- _Python_\n- _Regression_\n- _Classification_\n- _Recommendation System_\n- _Artificial Neural Network_\n- _...\nAnd more!!!_\n\n---\n\n## What will you learn?\n\n* Build ML models with NumPy \u0026 scikit-learn, build \u0026 train supervised models for prediction \u0026 binary classification tasks (linear, logistic regression)\n* Build \u0026 train a neural network with TensorFlow to perform multi-class classification, \u0026 build \u0026 use decision trees \u0026 tree ensemble methods\n* Apply best practices for ML development \u0026 use unsupervised learning techniques for unsupervised learning including clustering \u0026 anomaly detection\n* Build recommender systems with a collaborative filtering approach \u0026 a content-based deep learning method \u0026 build a deep reinforcement learning model\n---\n## Applied Learning Project\nBy the end of this Specialization, you will be ready to:\n* Build machine learning models in Python using popular machine learning libraries NumPy and scikit-learn.\n* Build and train supervised machine learning models for prediction and binary classification tasks, including linear regression and logistic regression.\n* Build and train a neural network with TensorFlow to perform multi-class classification.\n* Apply best practices for machine learning development so that your models generalize to data and tasks in the real world.\n* Build and use decision trees and tree ensemble methods, including random forests and boosted trees.\n* Use unsupervised learning techniques for unsupervised learning: including clustering and anomaly detection.\n* Build recommender systems with a collaborative filtering approach and a content-based deep learning method.\n* Build a deep reinforcement learning model.\n---\n## Outline of Machine Learning Specialization Course\n### [Course 1 - Supervised Machine Learning: Regression and Classification:](https://github.com/vhoang1206/Coursera-Machine-Learning-Specialization/tree/main/Course%201%20-%20Supervised%20Machine%20Learning-%20Regression%20and%20Classification)\nIn the first course of the specialization, you'll:\n* Have a good understanding of the concepts of Supervised Learning, Unsupervised Learning, Regression, Classification, Clustering, Gradient Descent,...\n* Build simple machine learning models in Python using popular machine learning libraries NumPy \u0026 scikit-learn.\n* Build \u0026 train supervised machine learning models for prediction \u0026 binary classification tasks, including linear regression \u0026 logistic regression.\n### [Course 2 - Advanced Learning Algorithms:](https://github.com/vhoang1206/Coursera-Machine-Learning-Specialization/tree/main/Course%202%20-%20Advanced%20Learning%20Algorithms)\nIn the second course of the specialization, you'll able to:\n* Build and train a neural network with TensorFlow to perform multi-class classification.\n* Apply best practices for machine learning development so that your models generalize to data and tasks in the real world.\n* Build and use decision trees and tree ensemble methods, including random forests and boosted trees.\n### [Course 3 - Unsupervised Learning, Recommenders, Reinforcement Learning](https://github.com/vhoang1206/Coursera-Machine-Learning-Specialization/tree/main/Course%203%20-%20Unsupervised%20Learning%2C%20Recommenders%2C%20Reinforcement%20Learning)\nIn the last course of the specialization, you'll be able to:\n* Use unsupervised learning techniques for unsupervised learning: including clustering and anomaly detection\n* Build a deep reinforcement learning model\n* Build recommender systems with a collaborative filtering approach and a content-based deep learning method\n---\n## Certificates\n1. [Machine Learning Specialization](https://github.com/vhoang1206/Coursera-Machine-Learning-Specialization/blob/main/Certificates/Machine%20Learning.pdf)\n2. [Supervised Machine Learning: Regression and Classification](https://github.com/vhoang1206/Coursera-Machine-Learning-Specialization/blob/main/Certificates/Supervised%20Machine%20Learning%20-%20Regression%20and%20Classification%20.pdf)\n3. [Advanced Learning Algorithms](https://github.com/vhoang1206/Coursera-Machine-Learning-Specialization/blob/main/Certificates/Advanced%20Learning%20Algorithms.pdf)\n4. [Unsupervised Learning, Recommenders, Reinforcement Learning](https://github.com/vhoang1206/Coursera-Machine-Learning-Specialization/blob/main/Certificates/Unsupervised%20Learning%2C%20Recommenders%2C%20Reinforcement%20Learning.pdf)\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fvubacktracking%2Fcoursera-machine-learning-specialization","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fvubacktracking%2Fcoursera-machine-learning-specialization","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fvubacktracking%2Fcoursera-machine-learning-specialization/lists"}