https://github.com/vubacktracking/coursera-machine-learning-specialization

Contains Optional Labs and Solutions of Programming Assignment for the Machine Learning Specialization By Stanford University and Deeplearning.ai - Coursera (2023) by Prof. Andrew NG
https://github.com/vubacktracking/coursera-machine-learning-specialization

andrew-ng andrew-ng-machine-learning coursera-specialization decision-trees gradient-descent linear-regression logistic-regression machine-learning neural-network python softmax-regression supervised-learning tensorflow unsupervised-learning xgboost

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Contains Optional Labs and Solutions of Programming Assignment for the Machine Learning Specialization By Stanford University and Deeplearning.ai - Coursera (2023) by Prof. Andrew NG

• Host: GitHub
• URL: https://github.com/vubacktracking/coursera-machine-learning-specialization
• Owner: VuBacktracking
• License: mit
• Created: 2023-07-04T07:27:49.000Z (almost 2 years ago)
• Default Branch: main
• Last Pushed: 2023-09-24T01:48:11.000Z (over 1 year ago)
• Last Synced: 2025-03-26T08:13:57.353Z (about 1 month ago)
• Topics: andrew-ng, andrew-ng-machine-learning, coursera-specialization, decision-trees, gradient-descent, linear-regression, logistic-regression, machine-learning, neural-network, python, softmax-regression, supervised-learning, tensorflow, unsupervised-learning, xgboost
• Language: Jupyter Notebook
• Homepage:
• Size: 35.4 MB
• Stars: 32
• Watchers: 2
• Forks: 17
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • Contributing: CONTRIBUTING.md
  • License: LICENSE

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README

        
# [Machine Learning Specialization](https://www.coursera.org/specializations/machine-learning-introduction?#courses)

  

Contains Optional Labs and Solutions for Programming Assignments for the Machine Learning Specialization (Updated) by Prof. Andrew NG

---

## Skill you'll gain:

- _Python_

- _Regression_

- _Classification_

- _Recommendation System_

- _Artificial Neural Network_

- _...

And more!!!_

---

## What will you learn?

* 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

---

## Applied Learning Project

By the end of this Specialization, you will be ready to:

* Build machine learning models in Python using popular machine learning libraries NumPy and scikit-learn.

* Build and train supervised machine learning models for prediction and binary classification tasks, including linear regression and logistic regression.

* Build and train a neural network with TensorFlow to perform multi-class classification.

* Apply best practices for machine learning development so that your models generalize to data and tasks in the real world.

* Build and use decision trees and tree ensemble methods, including random forests and boosted trees.

* Use unsupervised learning techniques for unsupervised learning: including clustering and anomaly detection.

* Build recommender systems with a collaborative filtering approach and a content-based deep learning method.

* Build a deep reinforcement learning model.

---

## Outline of Machine Learning Specialization Course

### [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)

In the first course of the specialization, you'll:

* Have a good understanding of the concepts of Supervised Learning, Unsupervised Learning, Regression, Classification, Clustering, Gradient Descent,...

* Build simple machine learning models in Python using popular machine learning libraries NumPy & scikit-learn.

* Build & train supervised machine learning models for prediction & binary classification tasks, including linear regression & logistic regression.

### [Course 2 - Advanced Learning Algorithms:](https://github.com/vhoang1206/Coursera-Machine-Learning-Specialization/tree/main/Course%202%20-%20Advanced%20Learning%20Algorithms)

In the second course of the specialization, you'll able to:

* Build and train a neural network with TensorFlow to perform multi-class classification.

* Apply best practices for machine learning development so that your models generalize to data and tasks in the real world.

* Build and use decision trees and tree ensemble methods, including random forests and boosted trees.

### [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)

In the last course of the specialization, you'll be able to:

* Use unsupervised learning techniques for unsupervised learning: including clustering and anomaly detection

* Build a deep reinforcement learning model

* Build recommender systems with a collaborative filtering approach and a content-based deep learning method

---

## Certificates

1. [Machine Learning Specialization](https://github.com/vhoang1206/Coursera-Machine-Learning-Specialization/blob/main/Certificates/Machine%20Learning.pdf)

2. [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)

3. [Advanced Learning Algorithms](https://github.com/vhoang1206/Coursera-Machine-Learning-Specialization/blob/main/Certificates/Advanced%20Learning%20Algorithms.pdf)

4. [Unsupervised Learning, Recommenders, Reinforcement Learning](https://github.com/vhoang1206/Coursera-Machine-Learning-Specialization/blob/main/Certificates/Unsupervised%20Learning%2C%20Recommenders%2C%20Reinforcement%20Learning.pdf)