Collaborative Filtering

Author: camara94

ML0101EN-RecSys-Content-Based-movies-py-v1.ipynb

@@ -530,4 +530,8 @@ g)
 ### Finding the recommendation
 ![finding](images/findingrecommendation.png)
 ### Comme back to recommended
-![choosepattern](images/choosepattern.png)
+![choosepattern](images/choosepattern.png)
+
+
+### Collaborative Filtering
+![collaborative filtering](images/collaborativefiltering.png)

README.md

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+e666daf dataset
+50da0ec en_Content-Based Recommender Systems.txt
+7362c77 Comme back to recommended
+ad28b25 Finding the recommendation
+b208dc8 Candidate movies for recommendation
+1268377 Weighing the genres
+b072124 Content-based recommender systems
+167ca13 en_Intro to Recommender Systems.txt
+dcd0311 Implementing recommender systems
+89fe206 Two types of recommender systems
+47b81ef Advantage of recommender systems
+93c275f Applications
+ed3eb85 What are recommender systems ?
+6e3dbb6 Learning Objectives
+36e1a83 exams 4 PNG
+521d474 dataset weather-stations20140101-20141231
+fcc85e0 en_DBSCAN Clustering
+7866f6e Adventage of DBSCAN
+c128df1  DBSCAN algorithm - clusters ?
+5dbe439 DBSCAN algorithm - outliers ?
+b038d4c DBSCAN algorithm - border point ?
+f4b8a69 DBSCAN algorithm - core point ?
+cbc7589 How DBSCAN works
+ecd690e WHat is DBSCAN ?
+29dc068 DBSCAN for class identification
+296a6b8 K-means vs. density-based clustering'
+069aa92 Density-based clustering
+4dc32ff hierarchical clustering model
+b5cc0aa dataset of hierachical algorithm
+9b3817d en_More on Hierarchical Clustering
+85ef8de Hierarchical clustering vs. K-means
+22bc6fa Adventages vs. disadventages
+156d43b Distance between clusters
+36c6745 How can calculate Distance
+8e1da03 Similarity  / Distance
+1b88075 Agglomerative algorithm
+5d5d7fa en_Hierarchical Clustering
+02c954e Hierarchical clustering
+77b4ac0 Agglomerative clustering
+50c98b0 Hierarchical clustering
+4f3b10d lab k-means
+28f1912 Cust_Segmentation dataset
+b5dd0df en_More on K-Means
+27f7448 K-Means recap
+0daa9d3 Choosing k
+2b65654 K-Means accuracy
+cd07f38 K-Means clustering algorithm
+de74ce2 en_K-Means Clustering
+f68195e K-Means clustering - repeat
+d9df602 K-Means clustering - compute new centroids
+266ca9e K-Means clustering - asign to centroid
+5a9e490 K-Means clustering - calculate the distance
+8dfd73c K-Means clustering - initialize K
+97b3012 How does K-means clustering work ?
+0b882ad Multi-dimensional similarity / distance
+3ea5290 2-dimensional similarity / distance
+d7af4e5 1-dimensional similarity / distance
+0611c21 Determine the simalarity or dissimilarity
+4146f5d K-means algorithms
+8675287 What is K-means clustering ?
+592e47b en_Intro to Clustering
+ecc6089 Clustering algorithms
+40d54cc Why clustering ?
+19e72bc Clustering application
+23f1430 Clustering vs classification
+0aa8ee6 What is clustering ?
+311eb09 Clustering for segmentation
+4cbf033 Module 4 - Learning Objectives
+f254820 lab SVM
+7c0d98a dataset svm
+152d88d en_Support Vector Machine (SVM)
+24a524b SVM applications
+cf3efe3 Pros and const of SVM
+56cada3 Using SVM to find the hyperplane
+a107b0d Data transformation
+683a25c What is SVM ?
+ba67a1c Classification with SVM
+268c357 lab
+f39af4a Training algorithm recap
+e6edf1b Using gradient descent to minimizing the cost
+b48bcbd Minimizing the cost function of the model
+ca15971 Logistic regression cost function
+a522870 Plotting cost function of the model
+b5cb282 General cost function
+2e1e265 Logistic Regression vs Linear Regression
+d37124a The training process
+812179e Clarification customer churn model
+a05f559 The problem with using linear regression
+fba4655 Linear regression classification problems ?
+437ffb7 Predicting churn using linear regression
+10bf03b Predicting customer income
+d7b156c en_Intro to Logistic Regression
+642c23f Building a model for customer churn
+4684c31 When is logistic regression suitables ?
+96a5ff7 Logistic regression applications
+c2f51f9 What is logistic regression ?
+fe3bd29 labo decision tree
+080375a dataset
+96f243b Building Decision Trees
+411cd7f Correct way to build a decision tree
+fec0e8e Calculating information
+8fe4d60 What is information gain ?
+431bac9 Which attribute is the best ?
+60e6c5b What about 'Sexe' ?
+bffd79f Is 'Cholesterol' the best attribute ?
+7468586 With attribute is the best one to use ?
+3537598 Entropy
+a2db01e Which attribute is the best attribute  ?
+2c318fc How to build decision tree
+9cc278a en_Intro to Decision Trees
+720e74c Decision tree learning algorithm
+6bf5b15 Building a decision tree with the training set
+2d225ed What is decision tree ?
+ca65277 lab module 3
+198fe54 dataset teleCust1000t
+eeae08a en_Evaluation Metrics in Classification
+941915b Log Loss
+73c97ba F1-score
+c6b5d97 Jaccard index
+3466ecb Classification accuracy
+249f976 en_K-Nearest Neighbors
+9de6705 Computing continuous target using KNN
+54198b8 What is the best value of K for KNN ?
+3d12680 multi-dimensional space
+dff3faa 1 -dimensional space
+f668bbc The K-Nearest Neighbors algorithm
+729d6b3 What is K-Nearest Neighbor ( or KNN ) ?
+427136f Determining the class unsing 5 KNNs
+5adcf5d Determining the class unsing 1st  KNN
+42a0b43 Intro to KNN
+8ab360c en_Intro to Classification
+39c4681 Classification algorithms in machine learning
+c6897a2 Classification applications
+95642d4 Classification use cases
+5b87669 Example of multi-class classification
+3d680fe How does classification work ?
+611a135 What is classification ?
+28a9f35 Learning Objectives
+0209858 exams 2 PDF
+add4ec6 exams 2
+4f4ab15 lab 3
+aaaf77e dataset lab 3
+f216c6a Non-Linear Regression
+a2b876d Linear vs non-linear regression
+e611195 What is non-polynomial regression ?
+dcd7032 What is polynomial regression ?
+e052f08 Different types of regression
+58bae3b Should we use linear regression ?
+5b0d5c9 en_Evaluation Metrics in Regression Models
+c8ac73e What is an error of the model ?
+f2ece35 Regression accuracy
+40268ef en_Model Evaluation in Regression Models
+7208ca3 How to use K-fold cross-validation ?
+18d04c6 Train/Test split evaluation approch
+7edc812 What is training & out-of-sample accuracy ?
+10cbe3e Train and test on the same dataset
+a842a08 Calculating the accuracy of a model
+3609484 Best approach for most accurate result ?
+66488b4 Model evaluation approaches
+b6c7a7a en_Multiple Linear Regression
+8841790 A&A - on multiple linear regression
+0c17ce5 Making prediction with multiple linear regression
+d385213 Estimating multiple linear regression parameters
+382647f Using MSE to expose the errors in the model
+c56953a Predicting continuous values with multiple linear regression
+98d47cc Examples of  multiple linear regression
+78a64dc Lab2 file python
+d77589d original dataset
+a0e0c11 dataset 2
+3a62025 dataset 1
+0acb287 en_Simple Linear Regression
+f227527 Pros of linear regression
+0e9519c Predictions with linear regression
+bc1b902 Estimating the parameters
+aab2830 How to find the best fit ?
+342e944 Linear regression model representation
+258dd76 How does linear regression works ?
+5859450 Linear regression topology
+581d536 Using linear regression to predict continuous values
+b8ff17c Introduction to Regression
+e918744 Application of regression
+36f0494 Application of regression
+b6fd78b Types of regression
+4f26097 What is a regression model ?
+39b293d What is regression ?
+bf634d4 Learning Objectives
+3dcd73c exams module 1
+09c6346 Supervised vs Unsupervised
+69b824b Supervised vs unsupervised learning
+dca26d7 What is clustering ?
+14c2b0c What is unsupervised learning
+b498cf1 What is regression ?
+88eb818 What is classification ?
+4e7691f Type of supervised learning
+f877c8e Teaching the model with labeled data
+dac6089 What is supervised learning
+4c3423a Python for Machine Learning
+2c87e26 Scikit-learn functions
+3e504b7 More about scikit-learn
+3c255e8 Python libraries for machine learning
+4e74fe3 intro ML
+2ce1ac9 intro ML
+812658d let's started ml
+5e4e8a5 difference between ai, ml and deep learning
+93dda22 Major machine learning techniques
+abc3926 Examples of machine learning
+f525bd3 How machine learning works ?
+26f0337 What is machine learning
+90a1676 Learning Objectives
+77eaaf5 Final Exam
+8628b48 Module 5 - Recommender Systems
+5b38937 Module 4 - Clustering
+4dfb00b Module 3 - Classification
+c8a0fcc Module 2 - Regression
+6ac0366 Module 1 - Machine Learning
+2855658 les algorithmes
+618382a Learning Objectives
+f3cfa46 welcome
+89cdaba welcome
+c3fe49c Initial commit