Home Courses Applied Machine Learning Online Course High Level + End-End Design of a Music Recommendation system - II

High Level + End-End Design of a Music Recommendation system - II

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High Level + End-End Design of a Music Recommendation system - I
Building a simple Youtube recommendation using basic Math
1.1
What is Clustering?
10 min
1.2
Unsupervised learning
4 min
1.3
Applications
16 min
1.4
Metrics for Clustering
19 min
1.5
K-Means: Geometric intuition, Centroids
8 min
1.6
K-Means: Mathematical formulation: Objective function
11 min
1.7
K-Means Algorithm.
11 min
1.8
How to initialize: K-Means++
25 min
1.9
Failure cases/Limitations
11 min
1.10
K-Medoids
19 min
1.11
Determining the right K
5 min
1.12
Code Samples
7 min
1.13
Time and space complexity
4 min
2.1
Agglomerative & Divisive, Dendrograms
14 min
2.2
Agglomerative Clustering
9 min
2.3
Proximity methods: Advantages and Limitations.
24 min
2.4
Time and Space Complexity
4 min
2.5
Limitations of Hierarchical Clustering
5 min
2.6
Code sample
3 min
3.1
Density based clustering
5 min
3.2
MinPts and Eps: Density
6 min
3.3
Core, Border and Noise points
7 min
3.4
Density edge and Density connected points.
6 min
3.5
DBSCAN Algorithm
11 min
3.6
Hyper Parameters: MinPts and EpsA
10 min
3.7
Advantages and Limitations of DBSCAN
9 min
3.8
Time and Space Complexity
3 min
3.9
Code samples.
3 min
3.10
Revision Questions
30 min
4.1
Problem formulation: Movie reviews
23 min
4.2
Content based vs Collaborative Filtering
11 min
4.3
Similarity based Algorithms
16 min
4.4
Matrix Factorization: PCA, SVD
23 min
4.5
Matrix Factorization: NMF
3 min
4.6
Matrix Factorization for Collaborative filtering
23 min
4.7
Matrix Factorization for feature engineering
9 min
4.8
Clustering as MF
21 min
4.9
Hyperparameter tuning
10 min
4.10
Matrix Factorization for recommender systems: Netflix Prize Solution
31 min
4.11
Cold Start problem
6 min
4.12
Word vectors as MF
20 min
4.13
Eigen-Faces
15 min
4.14
Code example.
11 min
4.15
Revision Questions
30 min
5.1
Questions & Answers
30 min
6.1
Problem Statement: Recommend similar apparel products in e-commerce using product descriptions and Images
12 min
6.2
Plan of action
7 min
6.3
Amazon product advertising API
4 min
6.4
Data folders and paths
6 min
6.5
Overview of the data and Terminology
12 min
6.6
Data cleaning and understanding:Missing data in various features
22 min
6.7
Understand duplicate rows
9 min
6.8
Remove duplicates : Part 1
12 min
6.9
Remove duplicates: Part 2
15 min
6.10
Text Pre-Processing: Tokenization and Stop-word removal
10 min
6.11
Stemming
4 min
6.12
Text based product similarity :Converting text to an n-D vector: bag of words
14 min
6.13
Code for bag of words based product similarity
26 min
6.14
TF-IDF: featurizing text based on word-importance
17 min
6.15
Code for TF-IDF based product similarity
10 min
6.16
Code for IDF based product similarity
9 min
6.17
Text Semantics based product similarity: Word2Vec(featurizing text based on semantic similarity)
19 min
6.18
Code for Average Word2Vec product similarity
15 min
6.19
TF-IDF weighted Word2Vec
9 min
6.20
Code for IDF weighted Word2Vec product similarity
6 min
6.21
Weighted similarity using brand and color
9 min
6.22
Code for weighted similarity
7 min
6.23
Building a real world solution
5 min
6.24
Deep learning based visual product similarity:ConvNets: How to featurize an image: edges, shapes, parts
11 min
6.25
Using Keras + Tensorflow to extract features
8 min
6.26
Visual similarity based product similarity
6 min
6.27
Measuring goodness of our solution :A/B testing
7 min
6.28
Assignment-24: Apparel Recommendation
6 min
7.1
Business/Real world problem:Problem definition

6 min
7.2
Objectives and constraints
7 min
7.3
Mapping to an ML problem:Data overview.
4 min
7.4
Mapping to an ML problem:ML problem formulation
5 min
7.5
Exploratory Data Analysis:Data preprocessing
7 min
7.6
Exploratory Data Analysis:Temporal Train-Test split.
6 min
7.7
Exploratory Data Analysis:Preliminary data analysis.
15 min
7.8
Exploratory Data Analysis:Sparse matrix representation
8 min
7.9
Exploratory Data Analysis:Average ratings for various slices
7 min
7.10
Exploratory Data Analysis:Cold start problem
5 min
7.11
Computing Similarity matrices:User-User similarity matrix
20 min
7.12
Computing Similarity matrices:Movie-Movie similarity
6 min
7.13
Computing Similarity matrices:Does movie-movie similarity work?
6 min
7.14
ML Models:Surprise library
6 min
7.15
Overview of the modelling strategy.
8 min
7.16
Data Sampling.
5 min
7.17
Google drive with intermediate files
2 min
7.18
Featurizations for regression.
11 min
7.19
Data transformation for Surprise.
2 min
7.20
Xgboost with 13 features
6 min
7.21
Surprise Baseline model.
9 min
7.22
Xgboost + 13 features +Surprise baseline model
4 min
7.23
Surprise KNN predictors
15 min
7.24
Matrix Factorization models using Surprise
5 min
7.25
SVD ++ with implicit feedback
11 min
7.26
Final models with all features and predictors.
4 min
7.27
Comparison between various models.
4 min
7.28
Assignment-18: Netflix prize
5 min
9.1
Building a simple Youtube recommendation using basic Math

9.2
Interview Questions on Clustering and Matrix Factorization
9.3
Live Session (22nd May 2022): Interview questions on Clustering
9.4
Live Session (29th May 2022): Interview Questions on Recommender Systems
9.5
Live Session(12th June 2022): Scenario based Interview Questions on RecSys
1 min
9.6
Live Session(21st August 2022): Design a Youtube Shorts/Reels/Tiktok feed recommender system
1 min
9.7
Live Session(21st August 2022): Design a Youtube Shorts/Reels/Tiktok feed recommender system
1 min