Courses

Data Science

Instructor
admin
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Course Description

Building Recommender Systems with Python 

 

1) Introduction to Recommender Systems 

  • Technical requirements 
  • What is a recommender system? 
  • The prediction problem 
  • The ranking problem 
  • Types of recommender systems 
  • Collaborative filtering 
  • User-based filtering 
  • Item-based filtering 
  • Shortcomings 
  • Content-based systems 
  • Knowledge-based recommenders 
  • Hybrid recommenders 
  • Summary

 

2) Manipulating Data with the Pandas Library 

  • Technical requirements 
  • Setting up the environment 
  • The Pandas library 
  • The Pandas DataFrame 
  • The Pandas Series 
  • Summary 

 

3) Building an IMDB Top 250 Clone with Pandas 

  • Technical requirements 
  • The simple recommender 
  • The metric 
  • The prerequisties 
  • Calculating the score 
  • Sorting and output 
  • The knowledge-based recommender 
  • Genres 
  • The build_chart function 
  • Summary 

 

4) Building Content-Based Recommenders 

  • Technical requirements 
  • Exporting the clean DataFrame 
  • Document vectors 
  • CountVectorizer 
  • TF-IDF Vectorizer 
  • The cosine similarity score 
  • Plot description-based recommender 
  • Preparing the data 
  • Creating the TF-IDF matrix 
  • Computing the cosine similarity score 
  • Building the recommender function 
  • Metadata-based recommender 
  • Preparing the data 
  • The keywords and credits datasets 
  • Wrangling keywords, cast, and crew 
  • Creating the metadata soup 
  • Generating the recommendations 
  • Suggestions for improvements 
  • Summary 

 

5) Data Mining Techniques 

  • Problem statement 
  • Similarity measures 
  • Euclidean distance 
  • Pearson correlation 
  • Cosine similarity 
  • Clustering 
  • k-means clustering 
  • Choosing k 
  • Other clustering algorithms 
  • Dimensionality reduction 
  • Principal component analysis 
  • Other dimensionality reduction techniques 
  • Linear-discriminant analysis 
  • Singular value decomposition 
  • Supervised learning 
  • k-nearest neighbors 
  • Classification 
  • Regression 
  • Support vector machines 
  • Decision trees 
  • Ensembling 
  • Bagging and random forests 
  • Boosting 
  • Evaluation metrics 
  • Accuracy 
  • Root mean square error 
  • Binary classification metrics 
  • Precision 
  • Recall 
  • F1 score 
  • Summary 

 

6) Building Collaborative Filters 

  • Technical requirements 
  • The framework 
  • The MovieLens dataset 
  • Downloading the dataset 
  • Exploring the data 
  • Training and test data 
  • Evaluation 
  • User-based collaborative filtering 
  • Mean 
  • Weighted mean 
  • User demographics 
  • Item-based collaborative filtering 
  • Model-based approaches 
  • Clustering 
  • Supervised learning and dimensionality reduction 
  • Singular-value decomposition 
  • Summary 

 

7) Hybrid Recommenders 

  • Technical requirements 
  • Introduction 
  • Case study – Building a hybrid model 
  • Summary 

About Instructor

  • admin

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