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bhupen @[email protected]

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Vision: To be the foremost AI training company committed to


18:19
Momentum: A Key Component in Modern Optimization Algorithms
11:11
Z-Test: A Key Tool in Inferential Statistics
16:21
Applying the Runs Test for Randomness in Data Sequences
19:43
Statistical Significance with Ranked Data: The Sign Test Approach
11:14
Beyond Chi-Square: When to Employ Fisher's Exact Test
20:29
Expected vs. Observed: Using Chi-Square to Analyze Data Patterns
19:51
ANOVA (Analysis of Variance) Explained
25:43
Beyond the T-Test: Exploring Non-Parametric Alternatives
56:24
Mastering the T-Test: A Comprehensive Guide for Data Scientists
21:41
Mastering Inferential Statistics for Data Science: A Comprehensive Guide
20:08
Enhancing Nearest Neighbor Search: Overcoming Brute Force Limitations with KD Trees and Ball Trees
08:06
Model persistence : Saving and loading the trained model (KNN)
08:37
K-Nearest Neighbors (KNN) for Regression: A Simple Yet Effective Approach
16:43
Enhancing KNN Performance: A Deep Dive into Hyperparameter Optimization
34:33
Precision, Recall, PR curves and Learning curves: The Cornerstones of Model Evaluation
10:58
Integrating Categorical Features in KNN
05:44
Feature Scaling : A Deep Dive into KNN Optimization
10:17
Tie Situation Strategies for Optimal KNN Predictions
10:18
KNN Precision: Choosing the Optimal 'k' for Your Dataset
15:39
K-Nearest Neighbors: Data Proximity for Effective Predictions
35:59
From Geometry to Data Science: Basic distance and similarities measures
47:18
From Supervised to Unsupervised Models: A Comprehensive ML Journey
16:01
Exploring Statistical Measures: Kurtosis, Skewness, and Symmetry
01:01:23
Data Distributions: A Comprehensive Guide, with hands on python code
36:02
From Basics to Code: Exploring Data Dispersion Measures
23:26
Central Measures in Data: From Basics to Winsorizing
40:30
Exploring Simple and Advanced Sampling Strategies: with Python Demos
16:20
Foundations of Statistical Understanding : Data Types, Tables, and Feature Types Explored
19:20
Essential stats for DS : why stats and maths - overview
30:00
Enhancing ML Model Generalization: Best Practices in Data Splitting