https://github.com/mvanzulli/mlspecialization_deeplearningia

This repo contains the files implemented thorough the Machine Learning Specialization offered by DeepLearning.IA
https://github.com/mvanzulli/mlspecialization_deeplearningia

coursera deeplearning-ai machine-learning python supervised-learning tensorflow unsupervised-learning

Last synced: about 2 months ago
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This repo contains the files implemented thorough the Machine Learning Specialization offered by DeepLearning.IA

• Host: GitHub
• URL: https://github.com/mvanzulli/mlspecialization_deeplearningia
• Owner: mvanzulli
• Created: 2022-08-03T22:51:28.000Z (almost 4 years ago)
• Default Branch: main
• Last Pushed: 2022-12-17T17:50:13.000Z (over 3 years ago)
• Last Synced: 2025-06-16T12:08:24.944Z (about 1 year ago)
• Topics: coursera, deeplearning-ai, machine-learning, python, supervised-learning, tensorflow, unsupervised-learning
• Language: Python
• Homepage:
• Size: 5.3 MB
• Stars: 1
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

          
# MLSpecialization_DeepLearningIA

Contains Solutions and Notes for the [Machine Learning Specialization](https://www.coursera.org/specializations/machine-learning-introduction/?utm_medium=coursera&utm_source=home-page&utm_campaign=mlslaunch2022IN) by Andrew NG on Coursera 

## Course 1 : [Supervised Machine Learning: Regression and Classification](https://www.coursera.org/learn/machine-learning?specialization=machine-learning-introduction)

- [Week 1]()

    - Regression

    - Supervised vs unsupervised learning

    - Model Representation

    - Cost Function

    - Gradient Descent




- [Week 2]() 

    - Gradient descent.

    - Multiple linear regression

    - Numpy Vectorization

    - Multi Variate Regression

    - Feature Scaling

    - Feature Engineering

    - Linear Regression




- [Week 3](/C1%20-%20Supervised%20Machine%20Learning%3A%20Regression%20and%20Classification/week3/)

    - Cost function and gradient descent for logistic regression

    - Classification

    - Sigmoid Function

    - Decision Boundary

    - Logistic Loss

    - Overfitting

    - Regularization

    - Logistic Regression

#### [Certificate Of Completion](https://www.coursera.org/account/accomplishments/certificate/6JRLLW8JRM3A)




## Course 2 : [Advanced Learning Algorithms](https://www.coursera.org/learn/advanced-learning-algorithms?specialization=machine-learning-introduction)

- [Week 1]()

    - Neural networks model and intuition

    - TensorFlow implementation

    - Neural Networks Implementation in Numpy

    - Neurons and Layers

    - Neural Networks for Binary Classification




- [Week 2]()

    - Neural Networks Training

    - Activation Functions

    - Multiclass Classification

    - RElu

    - Softmax

    - Neural Networks For Handwritten Digit Recognition 




- [Week 3]()

    - Bias and Variance

    - Machine Learning Development Process




- [Week 4]()

    - Decision Trees

    - Decision Trees Learning

    - Decision Trees Ensembles

#### [Certificate of Completion](https://www.coursera.org/account/accomplishments/certificate/US3R9FWCGTFW)        




## Course 3 : [Unsupervised Learning, Recommenders, Reinforcement Learning](https://www.coursera.org/learn/unsupervised-learning-recommenders-reinforcement-learning?specialization=machine-learning-introduction)




- [Week 1]()

    - Clustering

    - Anomaly Detection

    - K means

    - Anomaly Detection




- [Week 2]()

    - Collaborative Filtering

    - Recommender systems implementation

    - Content-based filtering

    - Collaborative Filtering RecSys

    - RecSys using Neural Networks




- [Week 3]()

    - Reinforcement learning

    - State-action value function

    - Continuous state spaces

    - Deep Q-Learning - Lunar Lander Example

#### [Certificate of Completion](https://www.coursera.org/account/accomplishments/certificate/6JRLLW8JRM3A)

### [Specialization Certificate](https://coursera.org/share/9d21590225c4716a984bd4eeb8592d85)

### Course Review :

This Course is a great place to start and get into Machine Learning algorithms. 

**Special thanks to [Professor Andrew Ng](https://www.andrewng.org/) for structuring and tailoring this Course.**






#### Some results :

* Write an unsupervised learning algorithm to **Land the Lunar Lander** Using Deep Q-Learning

    - The Rover was trained to land correctly on the surface, correctly between the flags as indicators after many unsuccessful attempts in learning how to do it.

    - The final landing after training the agent using appropriate parameters : 

https://user-images.githubusercontent.com/77543865/182395635-703ae199-ba79-4940-86eb-23dd90093ab3.mp4

* Write an algorithm for a **Movie Recommender System**

    

    - A movie database is collected based on its genre.

    - A content based filtering and collaborative filtering algorithm is trained and the movie recommender system is implemented.

    - It gives movie recommendentations based on the movie genre.

![movie_recommendation](https://user-images.githubusercontent.com/77543865/182398093-c7387754-34a9-4044-b842-0085060c3525.png)

## Bugs and feature requests

Have a bug or a feature request? Please first read the [issue guidelines](https://reponame/blob/master/CONTRIBUTING.md) and search for existing and closed issues. If your problem or idea is not addressed yet, [please open a new issue](https://reponame/issues/new).

## Creators

- 

## Copyright and license

Code and documentation copyright 2022 the authors. Code released under the [MIT License](https://reponame/blob/master/LICENSE).