Momentum: A Key Component in Modern Optimization Algorithms
bhupen
Momentum: A Key Component in Modern Optimization Algorithms
18:19
Z-Test: A Key Tool in Inferential Statistics
bhupen
Z-Test: A Key Tool in Inferential Statistics
11:11
Applying the Runs Test for Randomness in Data Sequences
bhupen
Applying the Runs Test for Randomness in Data Sequences
16:21
Statistical Significance with Ranked Data: The Sign Test Approach
bhupen
Statistical Significance with Ranked Data: The Sign Test Approach
19:43
Beyond Chi-Square: When to Employ Fisher's Exact Test
bhupen
Beyond Chi-Square: When to Employ Fisher's Exact Test
11:14
Expected vs. Observed: Using Chi-Square to Analyze Data Patterns
bhupen
Expected vs. Observed: Using Chi-Square to Analyze Data Patterns
20:29
ANOVA (Analysis of Variance) Explained
bhupen
ANOVA (Analysis of Variance) Explained
19:51
Beyond the T-Test: Exploring Non-Parametric Alternatives
bhupen
Beyond the T-Test: Exploring Non-Parametric Alternatives
25:43
Mastering the T-Test: A Comprehensive Guide for Data Scientists
bhupen
Mastering the T-Test: A Comprehensive Guide for Data Scientists
56:24
Mastering Inferential Statistics for Data Science: A Comprehensive Guide
bhupen
Mastering Inferential Statistics for Data Science: A Comprehensive Guide
21:41
Enhancing Nearest Neighbor Search: Overcoming Brute Force Limitations with KD Trees and Ball Trees
bhupen
Enhancing Nearest Neighbor Search: Overcoming Brute Force Limitations with KD Trees and Ball Trees
20:08
Model persistence : Saving and loading the trained model (KNN)
bhupen
Model persistence : Saving and loading the trained model (KNN)
8:06
K-Nearest Neighbors (KNN) for Regression: A Simple Yet Effective Approach
bhupen
K-Nearest Neighbors (KNN) for Regression: A Simple Yet Effective Approach
8:37
Enhancing KNN Performance: A Deep Dive into Hyperparameter Optimization
bhupen
Enhancing KNN Performance: A Deep Dive into Hyperparameter Optimization
16:43
Precision, Recall, PR curves and Learning curves: The Cornerstones of Model Evaluation
bhupen
Precision, Recall, PR curves and Learning curves: The Cornerstones of Model Evaluation
34:33
Integrating Categorical Features in KNN
bhupen
Integrating Categorical Features in KNN
10:58
Feature Scaling : A Deep Dive into KNN Optimization
bhupen
Feature Scaling : A Deep Dive into KNN Optimization
5:44
Tie Situation Strategies for Optimal KNN Predictions
bhupen
Tie Situation Strategies for Optimal KNN Predictions
10:17
KNN Precision: Choosing the Optimal 'k' for Your Dataset
bhupen
KNN Precision: Choosing the Optimal 'k' for Your Dataset
10:18
K-Nearest Neighbors: Data Proximity for Effective Predictions
bhupen
K-Nearest Neighbors: Data Proximity for Effective Predictions
15:39
From Geometry to Data Science: Basic distance and similarities measures
bhupen
From Geometry to Data Science: Basic distance and similarities measures
35:59
From Supervised to Unsupervised Models: A Comprehensive ML Journey
bhupen
From Supervised to Unsupervised Models: A Comprehensive ML Journey
47:18
Exploring Statistical Measures: Kurtosis, Skewness, and Symmetry
bhupen
Exploring Statistical Measures: Kurtosis, Skewness, and Symmetry
16:01
Data Distributions: A Comprehensive Guide, with hands on python code
bhupen
Data Distributions: A Comprehensive Guide, with hands on python code
1:01:23
From Basics to Code: Exploring Data Dispersion Measures
bhupen
From Basics to Code: Exploring Data Dispersion Measures
36:02
Central Measures in Data: From Basics to Winsorizing
bhupen
Central Measures in Data: From Basics to Winsorizing
23:26
Exploring Simple and Advanced Sampling Strategies: with Python Demos
bhupen
Exploring Simple and Advanced Sampling Strategies: with Python Demos
40:30
Foundations of Statistical Understanding : Data Types, Tables, and Feature Types Explored
bhupen
Foundations of Statistical Understanding : Data Types, Tables, and Feature Types Explored
16:20
Essential stats for DS : why stats and maths - overview
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Essential stats for DS : why stats and maths - overview
19:20
Enhancing ML Model Generalization: Best Practices in Data Splitting
bhupen
Enhancing ML Model Generalization: Best Practices in Data Splitting
30:00
Overcoming Data Imbalance: The Role of SMOTE in Machine Learning
bhupen
Overcoming Data Imbalance: The Role of SMOTE in Machine Learning
24:17
Practical Guide to Implementing Data Scaling Techniques in Python
bhupen
Practical Guide to Implementing Data Scaling Techniques in Python
26:07
A Hands-On Exploration of Data Encoding Methods
bhupen
A Hands-On Exploration of Data Encoding Methods
23:13
Outlier & Cardinality assessment : Python Code Demos and Strategies
bhupen
Outlier & Cardinality assessment : Python Code Demos and Strategies
42:20
Missing values (Part 2) : Nearest Neighbor-Based Interpolation with scikit-learn
bhupen
Missing values (Part 2) : Nearest Neighbor-Based Interpolation with scikit-learn
27:44
Beyond NaN: Understanding and Tackling Missing Data in Python - part 1
bhupen
Beyond NaN: Understanding and Tackling Missing Data in Python - part 1
39:07
Numeric Insights: Basic sanity check for Data Analysis
bhupen
Numeric Insights: Basic sanity check for Data Analysis
20:11
Making sense of DATA for ML/DL modeling
bhupen
Making sense of DATA for ML/DL modeling
23:36
Python - Essential Components for Data Science/ML Projects
bhupen
Python - Essential Components for Data Science/ML Projects
7:39
Machine Learning: Concepts, Models, and Platform Insights
bhupen
Machine Learning: Concepts, Models, and Platform Insights
1:12:43
Generating Data with Gaussian Naive Bayes: from concepts to implementation
bhupen
Generating Data with Gaussian Naive Bayes: from concepts to implementation
40:23
Handling Massive Datasets: Out-of-Core Multinomial Naive Bayes Approach
bhupen
Handling Massive Datasets: Out-of-Core Multinomial Naive Bayes Approach
21:01
From Words to Documents: Understanding Doc2Vec with Gensim
bhupen
From Words to Documents: Understanding Doc2Vec with Gensim
26:39
Optimizing Word Vector Generation: A Gensim Word2Vec Approach with Negative Sampling
bhupen
Optimizing Word Vector Generation: A Gensim Word2Vec Approach with Negative Sampling
18:06
Hands-on guide on Word2Vec: Gensim's Hierarchical Softmax Strategies
bhupen
Hands-on guide on Word2Vec: Gensim's Hierarchical Softmax Strategies
28:37
Transforming Words into Vectors: Hands on with Gensim's Word2Vec
bhupen
Transforming Words into Vectors: Hands on with Gensim's Word2Vec
21:18
Understanding Text Embedding Layers in Keras/TensorFlow
bhupen
Understanding Text Embedding Layers in Keras/TensorFlow
16:54
Constructing Training Set for Word Embedding Algorithms
bhupen
Constructing Training Set for Word Embedding Algorithms
13:19
From Words to Vectors : Understanding Word2Vec Embeddings
bhupen
From Words to Vectors : Understanding Word2Vec Embeddings
31:21
Beyond TF-IDF: Exploring BM25 for Enhanced Document Ranking and Vectorization
bhupen
Beyond TF-IDF: Exploring BM25 for Enhanced Document Ranking and Vectorization
29:57
IMDB Sentiment Analysis : Hashing Vectorizer with Online SGD Classifier
bhupen
IMDB Sentiment Analysis : Hashing Vectorizer with Online SGD Classifier
24:59
Continuous ML model training: with TF-IDF Vectorizer
bhupen
Continuous ML model training: with TF-IDF Vectorizer
7:04
TF-IDF Explained clearly: The Key to Textual Understanding
bhupen
TF-IDF Explained clearly: The Key to Textual Understanding
21:50
Mastering Bag of Words and Count Vectorization
bhupen
Mastering Bag of Words and Count Vectorization
28:57
Decoding Language: Byte Pair Encoding in Large Language Models and Generative AI
bhupen
Decoding Language: Byte Pair Encoding in Large Language Models and Generative AI
16:43
Regularization Techniques in ML - Easy Explanation with hands on Ridge/Lasso implementation
bhupen
Regularization Techniques in ML - Easy Explanation with hands on Ridge/Lasso implementation
40:09
Optimizing Models: The Role of Regularization in ML/DL - Intuition
bhupen
Optimizing Models: The Role of Regularization in ML/DL - Intuition
16:45
Advanced Feature Selection with Wrapper Methods
bhupen
Advanced Feature Selection with Wrapper Methods
12:51
Selecting Optimal Features with SelectKBest
bhupen
Selecting Optimal Features with SelectKBest
10:16
Assess Categorical Relationships in Data using chi2
bhupen
Assess Categorical Relationships in Data using chi2
11:55
ANOVA for Univariate Feature Importance
bhupen
ANOVA for Univariate Feature Importance
18:25
The Role of T-Test in Feature Selection
bhupen
The Role of T-Test in Feature Selection
23:47
Individually Strong: Univariate Feature Selection Techniques Explored
bhupen
Individually Strong: Univariate Feature Selection Techniques Explored
14:20
A Guide to Information Value
bhupen
A Guide to Information Value
14:34
Exploring Correlation Measures in Data Science
bhupen
Exploring Correlation Measures in Data Science
15:14
Correlations and Multi-collinearity in Feature Engineering
bhupen
Correlations and Multi-collinearity in Feature Engineering
20:30
Covariances in feature engineering (data science/ machine learning. )
bhupen
Covariances in feature engineering (data science/ machine learning. )
13:48
variance threshold in feature engg
bhupen
variance threshold in feature engg
15:30
Feature Engineering: Central Aspect of Data Science and ML
bhupen
Feature Engineering: Central Aspect of Data Science and ML
15:15
Text classification (Sentiment Analysis) on IMDB reviews using RNN
bhupen
Text classification (Sentiment Analysis) on IMDB reviews using RNN
19:15
Text classification IMDB - Using stacked LSTMs
bhupen
Text classification IMDB - Using stacked LSTMs
8:02
Long Short-Term Memory (LSTM), Architecture
bhupen
Long Short-Term Memory (LSTM), Architecture
39:15
Exploring RNNs : Problems with Recurrent Neural Nets
bhupen
Exploring RNNs : Problems with Recurrent Neural Nets
12:41
Hands-On Guide to RNNs: Simple Text Classification with RNN and Keras
bhupen
Hands-On Guide to RNNs: Simple Text Classification with RNN and Keras
4:17
Exploring RNNs: Insight into Text Classification
bhupen
Exploring RNNs: Insight into Text Classification
4:51
Behind the Scenes: Extracting Hidden States from Simple RNN's
bhupen
Behind the Scenes: Extracting Hidden States from Simple RNN's
11:39
Behind the Scenes: RNN Hidden State Mechanics Explored
bhupen
Behind the Scenes: RNN Hidden State Mechanics Explored
10:13
Recurrent Neural Networks: Architecture
bhupen
Recurrent Neural Networks: Architecture
39:05
Overview of Sequence Models
bhupen
Overview of Sequence Models
11:20