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Building Practical Recommendation Engines – Part 2
Building Personalized Recommendation Engines
The Course Overview (3:02)
Personalized and Content-Based Recommender System (10:21)
Content-Based Recommendation Using Python (8:15)
Context-Aware Recommender Systems (2:23)
Creating Context Profile (4:12)
Building Real-Time Recommendation Engines with Spark
About Spark 2.0 (3:43)
Spark Core (3:32)
Setting Up Spark (5:13)
Collaborative Filtering Using Alternating Least Square (3:34)
Model Based Recommender System Using pyspark (2:18)
The Recommendation Engine Approach (9:24)
Model Evaluation and Selection with Hyper Parameter Tuning (10:25)
Recommendation with Neo4j
Discerning Different Graph Databases (7:07)
Neo4j (3:23)
Building Your First Graph (4:00)
Neo4j Windows Installation (1:06)
Installing Neo4j on the Linux Platform (1:48)
Building Recommendation Engines (3:04)
Generating Recommendations Using Neo4j (1:51)
Collaborative filtering Using the Euclidean Distance (3:38)
Collaborative Filtering Using Cosine Similarity (2:20)
Building Scalable Recommendation Engines with Mahout
Setting up Mahout with General Introduction (4:21)
Core Building Blocks of Mahout (10:16)
Item-Based Collaborative Filtering (2:50)
Evaluating Collaborative Filtering with User-Item Based Recommenders (3:41)
SVD Recommenders (1:55)
The Future of Recommendation Engines
Future and Phases of Recommendation Engines (7:52)
Using Cases to Look Out for (1:58)
Popular Methodologies (4:46)
Discerning Different Graph Databases
Learn to understand the concept of databases and where to apply them.
Understanding the concept of databases
Apply database concepts
Understanding labeled property graphs and graphDB core concepts
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