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![100 Days of Machine Learning [YouTube] cover](https://d502jbuhuh9wk.cloudfront.net/courses/6918732bee601b12112865e6/6918732bee601b12112865e6_scaled_cover.jpg?v=1)
100 Days of Machine Learning [YouTube]
Go from zero to ML hero—one day at a time.
Instructor: CampusX
Language: Hindi
Validity Period: 1095 days
₹499 including 18% GST
This course is a structured 100-day journey designed to take you from beginner to confident practitioner in Machine Learning. Each day builds upon the last with focused, hands-on lessons covering the core algorithms, tools, and concepts used by data scientists and ML engineers today.
You’ll start with Python foundations, data preprocessing, and exploratory data analysis, then move through supervised and unsupervised learning, model evaluation, and feature engineering. As the days progress, you’ll master advanced topics such as ensemble methods, deep learning, and model deployment.
The course blends theory with real-world projects—ensuring that by the end of 100 days, you can confidently build, train, and deploy your own machine learning models. It’s the ultimate roadmap for learners who want consistency, clarity, and career-ready skills.
What you will learn:
- Python and data analysis for ML
- Regression, classification, and clustering algorithms
- Feature selection, scaling, and transformation techniques
- Model evaluation and performance tuning
- Deep learning fundamentals
- End-to-end ML project development and deployment
- Real-world applications across domains
| Complete Notes | |||
| Readme | |||
| 100 Days of ML - Part 1 (1350 pages) | |||
| 100 Days of ML - Part 2 (1345 pages) | |||
| Lectures | |||
| What is Machine Learning? | 100 Days of Machine Learning 20:00 | Preview | ||
| 01_What is Machine Learning_100DaysofML_CampusXocr (10 pages) | Preview | ||
| AI Vs ML Vs DL for Beginners in Hindi 16:00 | Preview | ||
| 02_AI Vs ML Vs DL for Beginners in Hindi_100DaysofML_CampusXocr (11 pages) | Preview | ||
| Types of Machine Learning for Beginners | Types of Machine learning in Hindi | Types of ML in Depth 28:00 | Preview | ||
| 03_Types of Machine Learning for Beginners_100DaysofML_CampusXocr (16 pages) | Preview | ||
| Batch Machine Learning | Offline Vs Online Learning | Machine Learning Types 11:00 | Preview | ||
| 04_Batch Machine Learning Offline Vs Online Learning_100DaysofML_CampusXocr (8 pages) | Preview | ||
| Online Machine Learning | Online Learning | Online Vs Offline Machine Learning 19:00 | Preview | ||
| 05_Online Machine Learning Online Learning Online Vs Offline Machine Learning_100DaysofML_CampusXocr (12 pages) | Preview | ||
| Instance-Based Vs Model-Based Learning | Types of Machine Learning 17:00 | Preview | ||
| 06_Instance-Based Vs Model-Based Learning_Types of Machine Learning_100DaysofML_CampusXocr (8 pages) | Preview | ||
| Challenges in Machine Learning | Problems in Machine Learning 24:00 | Preview | ||
| 07_Challenges in Machine Learning Problems in Machine Learning_100DaysofML_CampusXocr (55 pages) | Preview | ||
| Application of Machine Learning | Real Life Machine Learning Applications 29:00 | Preview | ||
| 08_Application of Machine Learning Real Life Machine Learning Applications_100DaysofML_CampusXocr (14 pages) | Preview | ||
| Machine Learning Development Life Cycle | MLDLC in Data Science 25:00 | Preview | ||
| 09_Machine Learning Development Life Cycle MLDLC in Data Science_100DaysofML_CampusXocr (19 pages) | Preview | ||
| Data Engineer Vs Data Analyst Vs Data Scientist Vs ML Engineer | Data Science Job Roles 26:00 | Preview | ||
| 10_Data Engineer Vs Data Analyst Vs Data Scientist Vs ML Engineer Data Science Job Roles_100DaysofML_CampusXocr (32 pages) | Preview | ||
| What are Tensors | Tensor In-depth Explanation | Tensor in Machine Learning 41:00 | |||
| 11_What are Tensors Tensor In-depth Explanation Tensor in Machine Learning_100DaysofML_CampusXocr (36 pages) | |||
| Installing Anaconda For Data Science | Jupyter Notebook for Machine Learning | Google Colab for ML 37:00 | |||
| 12_Installing Anaconda For Data Science Jupyter Notebook for Machine Learning Google Colab for ML_100DaysofML_CampusXocr (23 pages) | |||
| End to End Toy Project | Day 13 | 100 Days of Machine Learning 31:00 | |||
| 13_End to End Toy Project Day 13_100 Days of Machine Learning_100DaysofML_CampusXocr (18 pages) | |||
| How to Frame a Machine Learning Problem | How to plan a Data Science Project Effectively 22:00 | |||
| 14_How to Frame a Machine Learning Problem How to plan a Data Science Project Effectively_100DaysofML_CampusXocr (25 pages) | |||
| Working with CSV files | Day 15 | 100 Days of Machine Learning 37:00 | |||
| 15_Working with CSV files Day 15 100 Days of Machine Learning_100DaysofML_CampusXocr (18 pages) | |||
| Working with JSON/SQL | Day 16 | 100 Days of Machine Learning 17:00 | |||
| 16_Working with JSONSQL Day 16 100 Days of Machine Learning_100DaysofML_CampusXocr (20 pages) | |||
| Fetching Data From an API | Day 17 | 100 Days of Machine Learning 23:00 | |||
| 17_Fetching Data From an API Day 17 100 Days of Machine Learning_100DaysofML_CampusXocr (14 pages) | |||
| Fetching data using Web Scraping | Day 18 | 100 Days of Machine Learning 38:00 | |||
| 18_Fetching data using Web Scraping Day 18 100 Days of Machine Learning_100DaysofML_CampusXocr (21 pages) | |||
| Understanding Your Data | Day 19 | 100 Days of Machine Learning 15:00 | |||
| 19_Understanding Your Data Day 19 100 Days of Machine Learning_100DaysofML_CampusXocr (33 pages) | |||
| EDA using Univariate Analysis | Day 20 | 100 Days of Machine Learning 31:00 | |||
| 20_EDA using Univariate Analysis Day 20 100 Days of Machine Learning_100DaysofML_CampusXocr (42 pages) | |||
| EDA using Bivariate and Multivariate Analysis | Day 21 | 100 Days of Machine Learning 38:00 | |||
| 21_EDA using Bivariate and Multivariate Analysis_Day 21_100 Days of Machine Learning_100DaysofML_CampusXocr (19 pages) | |||
| Pandas Profiling | Day 22 | 100 Days of Machine Learning 13:00 | |||
| 22_Pandas Profiling_Day 22_100 Days of Machine Learning_100DaysofML_CampusXocr (19 pages) | |||
| What is Feature Engineering | Day 23 | 100 Days of Machine Learning 25:00 | |||
| 23_What is Feature Engineering_100DaysofML_CampusXocr (15 pages) | |||
| Feature Scaling - Standardization | Day 24 | 100 Days of Machine Learning 33:00 | |||
| 24_Feature Scaling - Standardization_Day 24_100 Days of Machine Learning_100DaysofML_CampusXocr (28 pages) | |||
| Feature Scaling - Normalization | MinMaxScaling | MaxAbsScaling | RobustScaling 24:00 | |||
| 25_Feature Scaling_ Normalization_MinMaxScaling_MaxAbsScaling_RobustScaling_100DaysofML_CampusXocr (29 pages) | |||
| Encoding Categorical Data | Ordinal Encoding | Label Encoding 20:00 | |||
| 26_Encoding Categorical Data Ordinal Encoding Label Encoding_100DaysofML_CampusXocr (21 pages) | |||
| One Hot Encoding | Handling Categorical Data | Day 27 | 100 Days of Machine Learning 30:00 | |||
| 27_One Hot Encoding_Handling Categorical Data _Day 27_100 Days of Machine Learning_100DaysofML_CampusXocr (25 pages) | |||
| Column Transformer in Machine Learning | How to use ColumnTransformer in Sklearn 16:00 | |||
| 28_Column Transformer in Machine Learning How to use ColumnTransformer in Sklearn_100DaysofML_CampusXocr (27 pages) | |||
| Machine Learning Pipelines A-Z | Day 29 | 100 Days of Machine Learning 46:00 | |||
| 29_Machine Learning Pipelines A-Z _Day 29_100 Days of Machine Learning_100DaysofML_CampusXocr (27 pages) | |||
| Function Transformer | Log Transform | Reciprocal Transform | Square Root Transform 32:00 | |||
| 30_Function Transformer Log Transform Reciprocal Transform Square Root Transform_100DaysofML_CampusXocr (40 pages) | |||
| Power Transformer | Box - Cox Transform | Yeo - Johnson Transform 21:00 | |||
| 31_Power Transformer Box - Cox Transform Yeo - Johnson Transform_100DaysofML_CampusXocr (37 pages) | |||
| Binning and Binarization | Discretization | Quantile Binning | KMeans Binning 38:00 | |||
| 32_Binning and Binarization Discretization Quantile Binning KMeans Binning_100DaysofML_CampusXocr (39 pages) | |||
| Handling Mixed Variables | Feature Engineering 12:00 | |||
| 33_Handling Mixed Variables Feature Engineering_100DaysofML_CampusXocr (10 pages) | |||
| Handling Date and Time Variables | Day 34 | 100 Days of Machine Learning 14:00 | |||
| 34_Handling Date and Time Variables Day 34 100 Days of Machine Learning_100DaysofML_CampusXocr (31 pages) | |||
| Handling Missing Data | Part 1 | Complete Case Analysis 25:00 | |||
| 35_Handling Missing Data Part 1 Complete Case Analysis_100DaysofML_CampusXocr (24 pages) | |||
| Handling missing data | Numerical Data | Simple Imputer 31:00 | |||
| 36_Handling missing data Numerical Data Simple Imputer_100DaysofML_CampusXocr (38 pages) | |||
| Handling Missing Categorical Data | Simple Imputer | Most Frequent Imputation | Missing Category Imp 14:00 | |||
| 37_Handling Missing Categorical Data Simple Imputer Most Frequent Imputation Missing Category Imp_100DaysofML_CampusXocr (29 pages) | |||
| Missing Indicator | Random Sample Imputation | Handling Missing Data Part 4 37:00 | |||
| 38_Missing Indicator Random Sample ImputationHandling Missing Data Part 4_100DaysofML_CampusXocr (37 pages) | |||
| KNN Imputer | Multivariate Imputation | Handling Missing Data Part 5 24:00 | |||
| 39_KNN Imputer Multivariate Imputation Handling Missing Data Part 5_100DaysofML_CampusXocr (35 pages) | |||
| Multivariate Imputation by Chained Equations for Missing Value | MICE Algorithm | Iterative Imputer 19:00 | |||
| 40_Multivariate Imputation by Chained Equations for Missing Value MICE Algorithm Iterative Imputer_100DaysofML_CampusXocr (41 pages) | |||
| What are Outliers | Outliers in Machine Learning 17:00 | |||
| 41_What are Outliers Outliers in Machine Learning_100DaysofML_CampusXocr (20 pages) | |||
| Outlier Detection and Removal using Z-score Method | Handling Outliers Part 2 18:00 | |||
| 42_Outlier Detection and Removal using Z-score Method Handling Outliers Part 2_100DaysofML_CampusXocr (18 pages) | |||
| Outlier Detection and Removal using the IQR Method | Handing Outliers Part 3 14:00 | |||
| 43_Outlier Detection and Removal using the IQR Method Handing Outliers Part _100DaysofML_CampusXocr (29 pages) | |||
| Outlier Detection using the Percentile Method | Winsorization Technique 16:00 | |||
| 44_ Outlier Detection using the Percentile Method Winsorization Technique_100DaysofML_CampusXocr (32 pages) | |||
| Feature Construction | Feature Splitting 12:00 | |||
| 45_Feature ConstructionFeature Splitting_100DaysofML_CampusXocr (13 pages) | |||
| Curse of Dimensionality 15:00 | |||
| 46_Curse of Dimensionality_100DaysofML_CampusXocr (12 pages) | |||
| Principle Component Analysis (PCA) | Part 1 | Geometric Intuition 34:00 | |||
| 47_Principle Component Analysis (PCA) Part 1 Geometric Intuition_100DaysofML_CampusXocr (11 pages) | |||
| Principle Component Analysis (PCA) | Part 2 | Problem Formulation and Step by Step Solution 56:00 | |||
| 48_Principle Component Analysis (PCA)Part 2Problem Formulation and Step by Step Solution_100DaysofML_CampusXocr (19 pages) | |||
| Principle Component Analysis(PCA) | Part 3 | Code Example and Visualization 43:00 | |||
| 49_Principle Component Analysis(PCA)Part 3Code Example and Visualization_100DaysofML_CampusXocr (23 pages) | |||
| Simple Linear Regression | Code + Intuition | Simplest Explanation in Hindi 34:00 | |||
| 50_Simple Linear Regression Code + Intuition Simplest Explanation in Hindi_100DaysofML_CampusXocr (23 pages) | |||
| Simple Linear Regression | Mathematical Formulation | Coding from Scratch 54:00 | |||
| 51_Simple Linear Regression Mathematical Formulation Coding from Scratch_100DaysofML_CampusXocr (20 pages) | |||
| Regression Metrics | MSE, MAE & RMSE | R2 Score & Adjusted R2 Score 44:00 | |||
| 52_Regression Metrics MSE, MAE & RMSE R2 Score & Adjusted R2 Score_100DaysofML_CampusXocr (15 pages) | |||
| Multiple Linear Regression | Geometric Intuition & Code 21:00 | |||
| 53_ Multiple Linear Regression Geometric Intuition & Code_100DaysofML_CampusXocr (14 pages) | |||
| Multiple Linear Regression | Part 2 | Mathematical Formulation From Scratch 48:00 | |||
| 54_ Multiple Linear Regression Part 2 Mathematical Formulation From Scratch_100DaysofML_CampusXocr (18 pages) | |||
| Multiple Linear Regression | Part 3 | Code From Scratch 16:00 | |||
| 55_ Multiple Linear Regression Part 3 Code From Scratch_100DaysofML_CampusXocr (13 pages) | |||
| Gradient Descent From Scratch | End to End Gradient Descent | Gradient Descent Animation 118:00 | |||
| 56_ Gradient Descent From Scratch End to End Gradient Descent Gradient Descent Animation_100DaysofML_CampusXocr (21 pages) | |||
| Batch Gradient Descent with Code Demo | Simple Explanation in Hindi 65:00 | |||
| 57_Batch Gradient Descent with Code Demo Simple Explanation in Hindi_100DaysofML_CampusXocr (16 pages) | |||
| Stochastic Gradient Descent 50:00 | |||
| 58_Stochastic Gradient Descent_100DaysofML_CampusXocr (22 pages) | |||
| Mini-Batch Gradient Descent 22:00 | |||
| 59_Mini-Batch Gradient Descent_100DaysofML_CampusXocr (22 pages) | |||
| Polynomial Regression | Machine Learning 27:00 | |||
| 60_ Polynomial Regression Machine Learning_100DaysofML_CampusXocr (16 pages) | |||
| Bias Variance Trade-off | Overfitting and Underfitting in Machine Learning 8:00 | |||
| 61_Bias Variance Trade-off Overfitting and Underfitting in Machine Learning_100DaysofML_CampusXocr (20 pages) | |||
| Ridge Regression Part 1 | Geometric Intuition and Code | Regularized Linear Models 20:00 | |||
| 62_Ridge Regression Part 1 Geometric Intuition and Code Regularized Linear Models_100DaysofML_CampusXocr (29 pages) | |||
| Ridge Regression Part 2 | Mathematical Formulation & Code from scratch | Regularized Linear Models 44:00 | |||
| 63_Ridge Regression Part 2 Mathematical Formulation Code from scratch Regularized Linear Models_100DaysofML_CampusXocr (18 pages) | |||
| Ridge Regression Part 3 | Gradient Descent | Regularized Linear Models 19:00 | |||
| 64_Ridge Regression Part 3 Gradient Descent Regularized Linear Models_100DaysofML_CampusXocr (29 pages) | |||
| 5 Key Points - Ridge Regression | Part 4 | Regularized Linear Models 30:00 | |||
| 65_5 Key Points - Ridge Regression Part 4 Regularized Linear Models_100DaysofML_CampusXocr (19 pages) | |||
| Lasso Regression | Intuition and Code Sample | Regularized Linear Models 29:00 | |||
| 66_Lasso Regression Intuition and Code Sample Regularized Linear Models_100DaysofML_CampusXocr (18 pages) | |||
| Why Lasso Regression creates sparsity? 25:00 | |||
| 67_Why Lasso Regression creates sparsity_100DaysofML_CampusXocr (17 pages) | |||
| ElasticNet Regression | Intuition and Code Example | Regularized Linear Models 12:00 | |||
| 68_ ElasticNet RegressionIntuition and Code Example Regularized Linear Models_100DaysofML_CampusXocr (14 pages) | |||
| Logistic Regression Part 1 | Perceptron Trick 47:00 | |||
| 69_Logistic Regression Part 1 Perceptron Trick_100DaysofML_CampusXocr (15 pages) | |||
| Logistic Regression Part 2 | Perceptron Trick Code 17:00 | |||
| 70_ Logistic Regression Part 2 Perceptron Trick Code_100DaysofML_CampusXocr (7 pages) | |||
| Logistic Regression Part 3 | Sigmoid Function | 100 Days of ML 41:00 | |||
| 71_Logistic Regression Part 3 Sigmoid Function 100 Days of ML_100DaysofML_CampusXocr (17 pages) | |||
| Logistic Regression Part 4 | Loss Function | Maximum Likelihood | Binary Cross Entropy 29:00 | |||
| 72_Logistic Regression Part 4 Loss Function Maximum Likelihood Binary Cross Entropy_100DaysofML_CampusXocr (16 pages) | |||
| Derivative of Sigmoid Function 6:00 | |||
| 73_Derivative of Sigmoid Function_100DaysofML_CampusXocr (13 pages) | |||
| Logistic Regression Part 5 | Gradient Descent & Code From Scratch 37:00 | |||
| 74_Logistic Regression Part 5 Gradient Descent & Code From Scratch_100DaysofML_CampusXocr (19 pages) | |||
| Accuracy and Confusion Matrix | Type 1 and Type 2 Errors | Classification Metrics Part 1 34:00 | |||
| 75_Accuracy and Confusion Matrix Type 1 and Type 2 Errors Classification Metrics Part 1_100DaysofML_CampusXocr (23 pages) | |||
| Precision, Recall and F1 Score | Classification Metrics Part 2 43:00 | |||
| 76_Precision, Recall and F1 Score Classification Metrics Part 2_100DaysofML_CampusXocr (17 pages) | |||
| Softmax Regression || Multinomial Logistic Regression || Logistic Regression Part 6 38:00 | |||
| 77_Softmax Regression Multinomial Logistic Regression Logistic Regression Part 6_100DaysofML_CampusXocr (20 pages) | |||
| Polynomial Features in Logistic Regression | Non Linear Logistic Regression | Logistic Regression 7 9:00 | |||
| 78_Polynomial Features in Logistic Regression Non Linear Logistic Regression Logistic Regression 7_100DaysofML_CampusXocr (13 pages) | |||
| Logistic Regression Hyperparameters || Logistic Regression Part 8 13:00 | |||
| 79_Logistic Regression Hyperparameters Logistic Regression Part 8_100DaysofML_CampusXocr (19 pages) | |||
| Decision Trees Geometric Intuition | Entropy | Gini impurity | Information Gain 58:00 | |||
| 80_Decision Trees Geometric Intuition Entropy Gini impurity Information Gain_100DaysofML_CampusXocr (44 pages) | |||
| Decision Trees - Hyperparameters | Overfitting and Underfitting in Decision Trees 27:00 | |||
| 81_Decision Trees - Hyperparameters Overfitting and Underfitting in Decision Trees_100DaysofML_CampusXocr (11 pages) | |||
| Regression Trees | Decision Trees Part 3 35:00 | |||
| 82_Regression Trees Decision Trees Part 3_100DaysofML_CampusXocr (12 pages) | |||
| Awesome Decision Tree Visualization using dtreeviz library 19:00 | |||
| 83_Awesome Decision Tree Visualization using dtreeviz library_100DaysofML_CampusXocr (15 pages) | |||
| Introduction to Ensemble Learning | Ensemble Techniques in Machine Learning 38:00 | |||
| 84_Introduction to Ensemble Learning Ensemble Techniques in Machine Learning_100DaysofML_CampusXocr (37 pages) | |||
| Voting Ensemble | Introduction and Core Idea | Part 1 17:00 | |||
| 85_Voting Ensemble Introduction and Core Idea Part 1_100DaysofML_CampusXocr (11 pages) | |||
| Voting Ensemble | Classification | Voting Classifier | Hard Voting Vs Soft Voting | Part 2 24:00 | |||
| 86_Voting Ensemble Classification Voting Classifier Hard Voting Vs Soft Voting Part 2_100DaysofML_CampusXocr (23 pages) | |||
| Voting Ensemble | Regression | Part 3 11:00 | |||
| 87_Voting Ensemble Regression Part 3_100DaysofML_CampusXocr (28 pages) | |||
| Bagging | Introduction | Part 1 31:00 | |||
| 88_Bagging Introduction Part 1_100DaysofML_CampusXocr (23 pages) | |||
| Bagging Ensemble | Part 2 | Bagging Classifiers 23:00 | |||
| 89_Bagging Ensemble Part 2 Bagging Classifiers_100DaysofML_CampusXocr (20 pages) | |||
| Bagging Ensemble | Part 3 | Bagging Regressor 11:00 | |||
| 90_Bagging Ensemble Part 3 Bagging Regressor_100DaysofML_CampusXocr (12 pages) | |||
| Introduction to Random Forest | Intuition behind the Algorithm 34:00 | |||
| 91_Introduction to Random Forest Intuition behind the Algorithm_100DaysofML_CampusXocr (21 pages) | |||
| How Random Forest Performs So Well? Bias Variance Trade-Off in Random Forest 13:00 | |||
| 92_How Random Forest Performs So Wel Bias Variance Trade-Off in Random Forest_100DaysofML_CampusXocr (15 pages) | |||
| Bagging Vs Random Forest | What is the difference between Bagging and Random Forest | Very Important 12:00 | |||
| 93_Bagging Vs Random Forest What is the difference between Bagging and Random Forest Very Important_100DaysofML_CampusXocr (8 pages) | |||
| Random Forest Hyper-parameters 15:00 | |||
| 94_Random Forest Hyper-parameters_100DaysofML_CampusXocr (22 pages) | |||
| Hyperparameter Tuning Random Forest using GridSearchCV and RandomizedSearchCV | Code Example 12:00 | |||
| 95_Hyperparameter Tuning Random Forest using GridSearchCV and RandomizedSearchCV Code Example_100DaysofML_CampusXocr (17 pages) | |||
| OOB Score | Out of Bag Evaluation in Random Forest | Machine Learning 7:00 | |||
| 96_OOB Score Out of Bag Evaluation in Random Forest Machine Learning_100DaysofML_CampusXocr (18 pages) | |||
| Feature Importance using Random Forest and Decision Trees | How is Feature Importance calculated 27:00 | |||
| 97_ Feature Importance using Random Forest and Decision Trees How is Feature Importance calculated_100DaysofML_CampusXocr (20 pages) | |||
| How Adaboost Classifier Works? | Geometric Intuition 17:00 | |||
| 98_ How Adaboost Classifier WorksGeometric Intuition_100DaysofML_CampusXocr (13 pages) | |||
| AdaBoost - A Step by Step Explanation 19:00 | |||
| 99_AdaBoost - A Step by Step Explanation_100DaysofML_CampusXocr (13 pages) | |||
| AdaBoost Algorithm | Code from Scratch 16:00 | |||
| 100_AdaBoost Algorithm Code from Scratch_100DaysofML_CampusXocr (22 pages) | |||
| 100_AdaBoost Algorithm Code from Scratch_100DaysofML_CampusX (22 pages) | |||
| AdaBoost Hyperparameters | GridSearchCV in Adaboost 11:00 | |||
| 101_AdaBoost Hyperparameters GridSearchCV in Adaboost_100DaysofML_CampusX (19 pages) | |||
| Bagging Vs Boosting | What is the difference between Bagging and Boosting 6:00 | |||
| 102_Bagging Vs Boosting What is the difference between Bagging and Boosting_100DaysofML_CampusX (6 pages) | |||
| K-Means Clustering Algorithm | Geometric Intuition | Clustering | Unsupervised Learning 24:00 | |||
| 103_K-Means Clustering Algorithm Geometric Intuition Clustering Unsupervised Learning_100DaysofML_CampusX (20 pages) | |||
| K-Means Clustering Algorithm in Python | Practical Example | Student Clustering Example | sklearn 10:00 | |||
| 104_K-Means Clustering Algorithm in Python Practical Example Student Clustering Example sklearn_100DaysofML_CampusX (15 pages) | |||
| K-Means Clustering Algorithm From Scratch In Python | ML Algorithms From Scratch 34:00 | |||
| 105_K-Means Clustering Algorithm From Scratch In Python ML Algorithms From Scratch_100DaysofML_CampusX (22 pages) | |||
| Gradient Boosting Explained | How Gradient Boosting Works? 33:00 | |||
| 106_ Gradient Boosting Explained How Gradient Boosting Works_100DaysofML_CampusX (15 pages) | |||
| Gradient Boosting Regression Part 2 | Mathematics of Gradient Boosting 57:00 | |||
| 107_Gradient Boosting Regression Part 2 Mathematics of Gradient Boosting_100DaysofML_CampusX (18 pages) | |||
| Gradient Boosting for Classification | Geometric Intuition | CampusX 65:00 | |||
| 108_Gradient Boosting for Classification Geometric Intuition CampusX_100DaysofML_CampusX (24 pages) | |||
| Stacking and Blending Ensembles 35:00 | |||
| 109_Stacking and Blending Ensembles_100DaysofML_CampusX (26 pages) | |||
| Agglomerative Hierarchical Clustering | Python Code Example 37:00 | |||
| 110_Agglomerative Hierarchical Clustering_100DaysofML_CampusX (30 pages) | |||
| What is K Nearest Neighbors? | KNN Explained in Hindi | Simple Overview in 1 Video | CampusX 52:00 | |||
| 111_What is K Nearest Neighbor KNN Explained in Hindi Simple Overview in 1 Video CampusX_100DaysofML_CampusX (9 pages) | |||
| What are the main Assumptions of Linear Regression? | Top 5 Assumptions of Linear Regression 18:00 | |||
| 112_What are the main Assumptions of Linear RegressionTop 5 Assumptions of Linear Regression_100DaysofML_CampusX (19 pages) | |||
| Support Vector Machines | Geometric Intuition 12:00 | |||
| 113_ Support Vector Machines Geometric Intuition_100DaysofML_CampusX (16 pages) | |||
| Mathematics of SVM | Support Vector Machines | Hard margin SVM 35:00 | |||
| 114_Mathematics of SVM Support Vector Machines Hard margin SVM_100DaysofML_CampusX (15 pages) | |||
| Mathematics of Support Vector Machine | Soft Margin SVM 15:00 | |||
| 115_Mathematics of Support Vector Machine Soft Margin SVM_100DaysofML_CampusX (12 pages) | |||
| Kernel Trick in SVM | Code Example 14:00 | |||
| 116_Kernel Trick in SVM Code Example_100DaysofML_CampusX (17 pages) | |||
| Kernel Trick in SVM | Geometric Intuition 6:00 | |||
| 117_Kernel Trick in SVM Geometric Intuition_100DaysofML_CampusX (6 pages) | |||
| Naive Bayes Classifier | Part 1 | Conditional Probability 9:00 | |||
| 118_Naive Bayes Classifier Part 1 Conditional Probability_100DaysofML_CampusX (13 pages) | |||
| Naive Bayes Classifier | Part 2 | Independent Events in Probability 8:00 | |||
| 119_Naive Bayes Classifier Part 2 Independent Events in Probability_100DaysofML_CampusX (12 pages) | |||
| Naive Bayes Classifier | Part 3 | Mutually Exclusive Events 2:00 | |||
| 120_Naive Bayes Classifier Part 3 Mutually Exclusive Events_100DaysofML_CampusX (4 pages) | |||
| Naive Bayes Classifier | Part 4 | Bayes Theorem in Probability 4:00 | |||
| 121_Naive Bayes Classifier Part 4 Bayes Theorem in Probability_100DaysofML_CampusX (11 pages) | |||
| Naive Bayes Classifier | Part 5 | Problem based upon Bayes Theorem 9:00 | |||
| 122_Naive Bayes Classifier Part 5 Problem based upon Bayes Theorem_100DaysofML_CampusX (12 pages) | |||
| Naive Bayes Classifier | Part 6 | Intuition 15:00 | |||
| 123_Naive Bayes Classifier Part 6 Intuition_100DaysofML_CampusX (14 pages) | |||
| Naive Bayes Classifier | Part 7 | Mathematics behind Naive Bayes Algorithm 19:00 | |||
| 124_Naive Bayes Classifier Part 7 Mathematics behind Naive Bayes Algorithm_100DaysofML_CampusX (15 pages) | |||
| Naive Bayes Classifier | Part 8 | Simple Example Code 16:00 | |||
| 125_ Naive Bayes Classifier Part 8 Simple Example Code_100DaysofML_CampusX (16 pages) | |||
| Naive Bayes Part 9 | Handling Numerical Data 9:00 | |||
| 126_Naive Bayes Part 9 Handling Numerical Data_100DaysofML_CampusX (14 pages) | |||
| Introduction to XGBOOST | Machine Learning | CampusX 80:00 | |||
| 127_Introduction to XGBOOST Machine Learning CampusX (29 pages) | |||
| XGBoost for Regression | XGBoost Part 2 | CampusX 47:00 | |||
| 128_XGBoost for Regression XGBoost Part 2_100DaysofML_CampusX (15 pages) | |||
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