Machine Learning Projects: Recommendation system website

Course Curriculum

Chapter 1: Introduction

Lecture 1: Introduction

Lecture 2: Requierment

Chapter 2: Recommendation systems with python

Lecture 1: 1 – Recommendation system

Lecture 2: 1 – 1 different Recommendation system

Lecture 3: 1 – 1 – 1 Collaborative Filtering recommender systems

Lecture 4: 1 – 1 – 2 Content-based recommender systems

Lecture 5: 1 – 1 – 3 context aware RS

Chapter 3: RS with python

Lecture 1: 1 – 2 User Based Collaborative Filtering with python

Lecture 2: 1 – 2 – 1 cosinus similarity

Lecture 3: 1 – 2 – 2 what is MatrixFactorisation

Lecture 4: 1 – 2 – 3 Matrix Factorisation UBCF

Lecture 5: 1 – 2 – 4 what is KNN

Lecture 6: 1 – 2 – 5 UBCF with KNN

Lecture 7: 1 – 3 Item Based Collaborative Filtering with python

Lecture 8: 1 – 3 – 1 cosinus similarity

Lecture 9: 1 – 3 – 2 Matrix Factorisation IBCF

Lecture 10: 1 – 3 – 3 IBCF with KNN

Lecture 11: 1 – 4 Content Based filtering with python

Lecture 12: 1 – 4 – 1 CB Methode 1

Lecture 13: 1 – 4 – 2 CB Methode 2

Lecture 14: 1 – 5 Context aware recommender system in Python

Chapter 4: Django Recommendation website

Lecture 1: 2-Django website Introduction

Lecture 2: 2 – 1 what is Django

Lecture 3: 2 – 2 creating our project and App

Lecture 4: 2 – 3 Add our models

Lecture 5: 2 – 4 create admin user

Lecture 6: 2 – 5 adding views and urls

Lecture 7: 2 – 6 adding templates

Lecture 8: 2 – 7 creating Forms

Lecture 9: 2 – 8 Bootstrap

Lecture 10: 2 – 9 adding login,logout and Signup pages

Lecture 11: 2 – 10 loading data from CSV

Lecture 12: 2 – 11 create our Recommendation view, urls and template

Lecture 13: 2 – 12 using a machine learning model

Lecture 14: 3- Cold-start problem what to do

Lecture 15: notes and what to do next