/MLSpecialization_DeepLearningIA

This repo contains the files implemented thorough the Machine Learning Specialization offered by DeepLearning.IA

Primary LanguagePython

MLSpecialization_DeepLearningIA

Contains Solutions and Notes for the Machine Learning Specialization by Andrew NG on Coursera

Course 1 : Supervised Machine Learning: Regression and Classification

  • 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

    • Cost function and gradient descent for logistic regression
    • Classification
    • Sigmoid Function
    • Decision Boundary
    • Logistic Loss
    • Overfitting
    • Regularization
    • Logistic Regression

Certificate Of Completion


Course 2 : Advanced Learning Algorithms

  • 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


Course 3 : Unsupervised Learning, Recommenders, Reinforcement Learning


  • 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

Specialization Certificate

Course Review :

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

Special thanks to Professor Andrew Ng 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 :
lunar_lander.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

Bugs and feature requests

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Creators

Copyright and license

Code and documentation copyright 2022 the authors. Code released under the MIT License.