# Machine-Learning-Specialization

**Repository Path**: zhangfeiyue80/Machine-Learning-Specialization

## Basic Information

- **Project Name**: Machine-Learning-Specialization
- **Description**: No description available
- **Primary Language**: Python
- **License**: MIT
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2025-05-23
- **Last Updated**: 2025-05-23

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

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

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  <img src="https://github.com/A-sad-ali/Machine-Learning-Specialization/blob/master/Resources/Title.png" alt="The title">
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Contains Optional Labs and Solutions for Programming Assignments for the <a href = "https://www.coursera.org/specializations/machine-learning-introduction?#outcomes" target = "_blank">Machine Learning Specialization</a> (Updated) by Prof. Andrew NG

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## Skill you'll gain:
- _Python_
- _Regression_
- _Classification_
- _Recommendation System_
- _Artificial Neural Network_
- _...
And more!!!_

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## 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
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## 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.
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## Outline of Machine Learning Specialization Course
### [Course 1 - Supervised Machine Learning: Regression and Classification:](https://github.com/A-sad-ali/Machine-Learning-Specialization/tree/master/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/A-sad-ali/Machine-Learning-Specialization/tree/master/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/A-sad-ali/Machine-Learning-Specialization/tree/master/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
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## Certificates
1. [Machine Learning Specialization](https://github.com/A-sad-ali/Machine-Learning-Specialization/tree/master/Certificates/Machine%20Learning.pdf)