guillaumelgf / online-courses

This repository contains all the codes that I made while following online courses on Machile Learning and correlated subjects.

Online-Courses

This repository contains all the codes that I made while following online courses on Machile Learning and correlated subjects.

List of Online Courses

• Machine Leaning - by Andrew Nguyen for the Stanford University
• Deep Learning - by Andrew Nguyen for the Stanford University

Machine Learning

This is a 11 week online course made by Andrew Nguyen for the Stanford University on the Coursera's website.

Here are a few words on this online course from the author himself:

By the time you finish this class, you'll know how to apply the most advanced machine learning algorithms to such problems as anti-spam, image recognition, clustering, building recommender systems, and many other problems. You'll also know how to select the right algorithm for the right job, as well as become expert at 'debugging' and figuring out how to improve a learning algorithm's performance.

The following subjects were covered:

• Linear Regression
• Logistic Regression
• Regularization
• Neural Networks ex3 and ex4
• Advice for applying Machine Learning
• Support Vector Machines
• Unsupervised Learning
• Dimensionnality Reduction
• Anomaly Detection
• Recommender Systems
• Photo OCR (Optical Character Recognition)

By clicking on the links above you will get more details on the subjet I have learned and the codes for the exercices corresponding to each subjects. The exercises were made using the Octave software.

Link to my certification

Conditions to get the certification: This course is made of videos. It is divided into 11 weeks during which 1 to 3 subjects were covered. Each subject contains a quizz. I needed to get a grade higher or equal to 80% to pass the quizz. Most of the subjects also include a coding exercice to apply what I had just learned. The codes where also graded by the website. To get the certification, I needed to pass all the quizzes and the exercices. A final examination containing code and questions was required to get the certification.

Deep Learning

This is a collection of 5 courses totalizing 16 weeks of work. It was made by Andrew Nguyen and his assistants for the Stanford University on the website Coursera. This collection of courses is regrouped into a single project named: deeplearning.ai This is a very detailed online course which deals with different area of Deep Learning.

It is organized as follow:

• Course 1 - Neural Networks and Deep Learning
  • Week 2 - EX 1 - Logistic Regression with a Neural Network mindset
  • Week 3 - EX 1 - Planar data classification with one hidden layer
  • Week 4 - EX 1 - Building your Deep Neural Network - Step by Step
  • Week 4 - EX 2 - Deep Neural Network - Application
• Course 2 - Improving Deep Neural Networks: Hyperparameter tuning, Regularization and Optimization
  • Week 1 - EX 1 - Initialization
  • Week 1 - EX 2 - Regularization
  • Week 1 - EX 3 - Gradient Checking
  • Week 2 - EX 1 - Optimization methods
  • Week 3 - EX 1 - Tensorflow Tutorial
• Course 3 - Structuring Machine Learning Projects
  • Week 1 - ML Strategy (1)
  • Week 2 - ML Strategy (2)
• Course 4 - Convolutional Neural Networks
  • Week 1 - EX 1 - Convolutional model - Step by step
  • Week 1 - EX 2 - Convolutional model - Application
  • Week 2 - EX 1 - Residual Networks
  • Week 3 - EX 1 - Keras - Tutorial - Happy House
• Course 5 - Sequence Models
  • Week 1 - EX 1 - Building a Recurrent Neural Network - Step by Step
  • Week 1 - EX 2 - Dinosaurus Island -- Character level language model final
  • Week 1 - EX 3 - Improvise a Jazz Solo with an LSTM Network
  • Week 2 - EX 1 - Operations on word vectors
  • Week 2 - EX 2 -Emojify
  • Week 3 - EX 1 - Neural machine translation with attention
  • Week 3 - EX 2 -Trigger word detection

The exercices were made using Python and Jupyter Notebooks.

Link to my certification