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June 2022 – Data Science Master Class
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Course Content
Naive Bayes
NB – 1
00:00
NB – 2
00:00
Linear Regression
Linear Regrssion – 1
00:00
Linear Regression -2
00:00
Logistic Regression
Logistic Regression -1
00:00
Logistic Regression -2
00:00
Logistic Regression – 3
00:00
Support Vector Machine
SVM 1
00:00
SVM 2
00:00
Decision Tree
DT
00:00
Ensample
Random Forest
00:00
Boosting – Ada, GB, XGB
00:00
K means
K Means
01:05:00
Clustering – 2
01:05:00
Model Package
How to create package and load model
43:00
Dimensionality Reduction
PCA
59:00
tSNE
44:00
Recommender System
Content Based
58:00
CF and MF
43:00
Optimization and Feature Engineering
Gradient Decent
45:00
Feature engineering
01:04:00
Feature engineering – 2
52:00
Deep Learning
Introduction
45:00
MLP
55:00
Activation Functions
45:00
DNN
44:00
Deep Neural Network Parameters
48:12
Initialization and Normailization
53:00
Optimizers
59:00
Losses
49:00
CNN
CNN – 1
58:00
CNN- 2
58:00
CNN – 3 – Architecture
53:00
CNN 4 – Architecture
45:00
CNN 5 – Architecture
48:00
Transfer Learning
46:00
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