04:40

Image Data Augmentation Explained | Boost Model Accuracy with Smart Techniques

27:29

Build a CNN from Scratch for Fashion MNIST (Code + Explanation) | Deep Learning

32:13

Building a CNN from Scratch for MNIST Digit Recognition | Step-by-Step Explanation

13:16

AI/ML Jobs for Freshers: 2 Simple Projects to Get Started!

09:45

Actual working of CNN: Understanding Convolutional Neural Networks

08:35

CNN Parameters & Hyperparameters Explained | Essential Concepts in Deep Learning

09:41

Can CNN Solve Regression, Binary Classification & Multi-Class Classification? | Explained

08:47

Fully Connected Layers & CNNs Explained | Overview of Convolutional Neural Networks

12:28

Understanding Padding in CNNs: How Stride & Padding Affect Input-Output Dimensions | CNN

11:05

Stride in Deep Learning: What It Is & How It Works! | Convolution Neural Networks

11:04

Pooling Layer in CNNs Explained | Max Pooling | Convolutional Neural Networks

07:53

Understanding ReLU Activation in CNNs | ReLU layer

15:50

👉 Convolution Layer in CNN Explained | How It Works in Detail | CNN Part 4

06:14

👉 Convolutional Neural Networks (CNN) Explained Simply | Layers & Workflow | CNN Part-3

04:13

How Biology Inspired the Creation of Convolutional Neural Networks (CNNs) 🚀| CNN part 2

11:19

Why Use CNNs Instead of ANNs for Image Processing? 🤔| Deep Learning

12:59

Master Grid Search CV in Machine Learning | Titanic Exercise(Part 2)

33:51

Solving the Titanic Dataset with Logistic Regression | Step-by-Step Tutorial

10:45

L1 & L2 Regularization Techniques Explained | Simplifying Machine Learning

26:55

Bike Assignment Solution | Linear Regression Code

19:45

Master Early Stopping & Dropout Regularization with Code | Cancer Classification(Part 2)

17:34

Cancer Classification | ANN Code Example | Binary Classification | Deep Learning

17:09

Types of Gradient Descent: Batch, Stochastic, and Mini-Batch Explained

19:46

ANN Regression Code Example | Deep Learning

09:23

🌟 Understanding Vanishing & Exploding Gradients Problem in Deep Learning 🌟

15:46

Activation Functions Explained Clearly | Deep Learning

23:52

Understanding Backpropagation in Artificial Neural Networks | Simplified Explanation

15:21

Artificial Neural Networks (Part 3)| Deep Learning

11:16

Artificial Neural Networks (Part 2)| Deep Learning

11:04

Artificial Neural Networks (Part 1)| Deep Learning

07:25

How Perceptron can be used ? | DeepLearning

08:30

Perceptron Explained Clearly | Deep Learning

03:59

Titanic Dataset Assignment | Binary Classification Exercise | Logistic Regression

06:29

What is Alpha in Gradient Descent ? | Hyper Parameters

15:56

Understanding AUC & ROC Curve in Machine Learning| Performance Metrics Explained

05:55

Code for Precision, Recall and F1-Score | Evaluation Metrics | Classification Problem.

07:05

Precision, Recall and F1-score Explained Clearly | Evaluation Metrics | Machine Learning

08:59

Precision, Recall, F1 Score Easy Explanation | Deep Learning

05:14

Precision, Recall and F1-Score | Evaluation Metrics Part2

15:59

Artificial Intelligence, Machine Learning & Deep Learning Explained | ML Pipeline Overview

09:51

Accuracy, Precision, Recall | Evaluation Metrics for Classification Problems Part1📊

11:06

Logistic Regression Code Example

18:49

LogisticRegression Part-2 | Understanding Loss Function & Optimizer | 2024

11:27

Logistic Regression Part1 | Understanding the Hypothesis of Logistic Regression | 2024

05:24

Bike Exercise | Linear Regression Assignment

13:28

Data Standardization Code | Data Scaling | Data Pre-processing Techniques

07:17

Data Standardization Explained | Data Scaling | Data Pre-processing Techniques

09:22

Data Normalization Code | Data Scaling Techniques | Data Pre-processing Techniques.

09:05

Data Normalization Explained | Data Scaling Techniques | Data Pre-processing Techniques.

16:16

Code for One Hot Encoding | Data Preprocessing Techniques

08:56

Mastering One-Hot Encoding in Machine Learning | Data Preprocessing Techniques

05:59

Data Pre-Processing Techniques(Label & Ordinal Encoding Code)

06:59

Data Pre-processing Techniques(Label & Ordinal Encoding)

12:23

Data Preprocessing Techniques(Missing Values)

17:12

Polynomial Regression with code example:)

09:55

Understanding Overfitting and Underfitting in Machine Learning | Key Concepts Explained

21:19

Linear Regression Example

28:57

Linear Regression Math Explained.

13:49

Linear Regression Theory Explained :)

09:52

Different Types of Machine Learning Algorithms Explained | Supervised, Unsupervised, Reinforcement.