100 Days of Machine Learning | CampusX

What is Machine Learning? | 100 Days of Machine Learning

AI Vs ML Vs DL for Beginners in Hindi

Types of Machine Learning for Beginners | Types of Machine learning in Hindi | Types of ML in Depth

Batch Machine Learning | Offline Vs Online Learning | Machine Learning Types

Online Machine Learning | Online Learning | Online Vs Offline Machine Learning

Instance-Based Vs Model-Based Learning | Types of Machine Learning

Challenges in Machine Learning | Problems in Machine Learning

Application of Machine Learning | Real Life Machine Learning Applications

Machine Learning Development Life Cycle | MLDLC in Data Science

Data Engineer Vs Data Analyst Vs Data Scientist Vs ML Engineer | Data Science Job Roles

What are Tensors | Tensor In-depth Explanation | Tensor in Machine Learning

Installing Anaconda For Data Science | Jupyter Notebook for Machine Learning | Google Colab for ML

End to End Toy Project | Day 13 | 100 Days of Machine Learning

How to Frame a Machine Learning Problem | How to plan a Data Science Project Effectively

Working with CSV files | Day 15 | 100 Days of Machine Learning

Working with JSON/SQL | Day 16 | 100 Days of Machine Learning

Fetching Data From an API | Day 17 | 100 Days of Machine Learning

Fetching data using Web Scraping | Day 18 | 100 Days of Machine Learning

Understanding Your Data | Day 19 | 100 Days of Machine Learning

EDA using Univariate Analysis | Day 20 | 100 Days of Machine Learning

EDA using Bivariate and Multivariate Analysis | Day 21 | 100 Days of Machine Learning

Pandas Profiling | Day 22 | 100 Days of Machine Learning

What is Feature Engineering | Day 23 | 100 Days of Machine Learning

Feature Scaling - Standardization | Day 24 | 100 Days of Machine Learning

Feature Scaling - Normalization | MinMaxScaling | MaxAbsScaling | RobustScaling

Encoding Categorical Data | Ordinal Encoding | Label Encoding

One Hot Encoding | Handling Categorical Data | Day 27 | 100 Days of Machine Learning

Column Transformer in Machine Learning | How to use ColumnTransformer in Sklearn

Machine Learning Pipelines A-Z | Day 29 | 100 Days of Machine Learning

Function Transformer | Log Transform | Reciprocal Transform | Square Root Transform

Power Transformer | Box - Cox Transform | Yeo - Johnson Transform

Binning and Binarization | Discretization | Quantile Binning | KMeans Binning

Handling Mixed Variables | Feature Engineering

Handling Date and Time Variables | Day 34 | 100 Days of Machine Learning

Handling Missing Data | Part 1 | Complete Case Analysis

Handling missing data | Numerical Data | Simple Imputer

Handling Missing Categorical Data | Simple Imputer | Most Frequent Imputation | Missing Category Imp

Missing Indicator | Random Sample Imputation | Handling Missing Data Part 4

KNN Imputer | Multivariate Imputation | Handling Missing Data Part 5

Multivariate Imputation by Chained Equations for Missing Value | MICE Algorithm | Iterative Imputer

What are Outliers | Outliers in Machine Learning

Outlier Detection and Removal using Z-score Method | Handling Outliers Part 2

Outlier Detection and Removal using the IQR Method | Handing Outliers Part 3

Outlier Detection using the Percentile Method | Winsorization Technique

Feature Construction | Feature Splitting

Curse of Dimensionality

Principle Component Analysis (PCA) | Part 1 | Geometric Intuition

Principle Component Analysis (PCA) | Part 2 | Problem Formulation and Step by Step Solution

Principle Component Analysis(PCA) | Part 3 | Code Example and Visualization

Simple Linear Regression | Code + Intuition | Simplest Explanation in Hindi

Simple Linear Regression | Mathematical Formulation | Coding from Scratch

Regression Metrics | MSE, MAE & RMSE | R2 Score & Adjusted R2 Score

Multiple Linear Regression | Geometric Intuition & Code

Multiple Linear Regression | Part 2 | Mathematical Formulation From Scratch

Multiple Linear Regression | Part 3 | Code From Scratch

What are the main Assumptions of Linear Regression? | Top 5 Assumptions of Linear Regression

Gradient Descent From Scratch | End to End Gradient Descent | Gradient Descent Animation

Batch Gradient Descent with Code Demo | Simple Explanation in Hindi

Stochastic Gradient Descent

Mini-Batch Gradient Descent

Polynomial Regression | Machine Learning

Bias Variance Trade-off | Overfitting and Underfitting in Machine Learning

Ridge Regression Part 1 | Geometric Intuition and Code | Regularized Linear Models

Ridge Regression Part 2 | Mathematical Formulation & Code from scratch | Regularized Linear Models

Ridge Regression Part 3 | Gradient Descent | Regularized Linear Models

5 Key Points - Ridge Regression | Part 4 | Regularized Linear Models

Lasso Regression | Intuition and Code Sample | Regularized Linear Models

Why Lasso Regression creates sparsity?

ElasticNet Regression | Intuition and Code Example | Regularized Linear Models

Logistic Regression Part 1 | Perceptron Trick

Logistic Regression Part 2 | Perceptron Trick Code

Logistic Regression Part 3 | Sigmoid Function | 100 Days of ML

Logistic Regression Part 4 | Loss Function | Maximum Likelihood | Binary Cross Entropy

Derivative of Sigmoid Function

Logistic Regression Part 5 | Gradient Descent & Code From Scratch

Accuracy and Confusion Matrix | Type 1 and Type 2 Errors | Classification Metrics Part 1

Precision, Recall and F1 Score | Classification Metrics Part 2

ROC Curve in Machine Learning | ROC-AUC in Machine Learning Simplified | CampusX

Softmax Regression || Multinomial Logistic Regression || Logistic Regression Part 6

Polynomial Features in Logistic Regression | Non Linear Logistic Regression | Logistic Regression 7

Logistic Regression Hyperparameters || Logistic Regression Part 8

Naive Bayes Classifier | Part 1 | Conditional Probability

Naive Bayes Classifier | Part 2 | Independent Events in Probability

Naive Bayes Classifier | Part 3 | Mutually Exclusive Events

Naive Bayes Classifier | Part 4 | Bayes Theorem in Probability

Naive Bayes Classifier | Part 5 | Problem based upon Bayes Theorem

Naive Bayes Classifier | Part 6 | Intuition

Naive Bayes Classifier | Part 7 | Mathematics behind Naive Bayes Algorithm

Naive Bayes Classifier | Part 8 | Simple Example Code

Naive Bayes Part 9 | Handling Numerical Data

What is K Nearest Neighbors? | KNN Explained in Hindi | Simple Overview in 1 Video | CampusX

Support Vector Machines | Geometric Intuition

Mathematics of SVM | Support Vector Machines | Hard margin SVM

Mathematics of Support Vector Machine | Soft Margin SVM

Kernel Trick in SVM | Geometric Intuition

Kernel Trick in SVM | Code Example

Decision Trees Geometric Intuition | Entropy | Gini impurity | Information Gain

Decision Trees - Hyperparameters | Overfitting and Underfitting in Decision Trees

Regression Trees | Decision Trees Part 3

Awesome Decision Tree Visualization using dtreeviz library

Introduction to Ensemble Learning | Ensemble Techniques in Machine Learning

Voting Ensemble | Introduction and Core Idea | Part 1

Voting Ensemble | Classification | Voting Classifier | Hard Voting Vs Soft Voting | Part 2

Voting Ensemble | Regression | Part 3

Bagging | Introduction | Part 1

Bagging Ensemble | Part 2 | Bagging Classifiers

Bagging Ensemble | Part 3 | Bagging Regressor

Introduction to Random Forest | Intuition behind the Algorithm

How Random Forest Performs So Well? Bias Variance Trade-Off in Random Forest

Bagging Vs Random Forest | What is the difference between Bagging and Random Forest | Very Important

Random Forest Hyper-parameters

Hyperparameter Tuning Random Forest using GridSearchCV and RandomizedSearchCV | Code Example

OOB Score | Out of Bag Evaluation in Random Forest | Machine Learning

Feature Importance using Random Forest and Decision Trees | How is Feature Importance calculated

How Adaboost Classifier Works? | Geometric Intuition

AdaBoost - A Step by Step Explanation

AdaBoost Algorithm | Code from Scratch

AdaBoost Hyperparameters | GridSearchCV in Adaboost

Bagging Vs Boosting | What is the difference between Bagging and Boosting

Gradient Boosting Explained | How Gradient Boosting Works?

Gradient Boosting Regression Part 2 | Mathematics of Gradient Boosting

Gradient Boosting for Classification | Geometric Intuition | CampusX

Introduction to XGBOOST | Machine Learning | CampusX

XGBoost for Regression | XGBoost Part 2 | CampusX

XGBoost For Classification | How XGBoost works on Classification Problems | CampusX

The Maths Behind XGBoost | Machine Learning | CampusX

Stacking and Blending Ensembles

K-Means Clustering Algorithm | Geometric Intuition | Clustering | Unsupervised Learning

K-Means Clustering Algorithm in Python | Practical Example | Student Clustering Example | sklearn

K-Means Clustering Algorithm From Scratch In Python | ML Algorithms From Scratch

Agglomerative Hierarchical Clustering | Python Code Example

DBSCAN Clustering Algorithms | Density Based Clustering | How DBSCAN Works | CampusX

Imbalanced Data in Machine Learning | Undersampling | Oversampling | SMOTE

Hyperparameter Tuning using Optuna | Bayesian Optimization using Optuna