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| Item Details | Price | ||
|---|---|---|---|
ML Algorithm
Instructor: CampusX
Language: Hindi
Validity Period: Lifetime
This course teaches you the core machine learning algorithms from scratch. You'll learn how they work, when to use them, and how to apply them in real projects.
By the end, you'll be able to:
- Understand how key ML algorithms work
- Choose the right algorithm for a problem
- Train, tune, and evaluate models confidently
No black boxes. Just clear explanations, real examples, and code that makes sense.
Course Duration: 38+ hours
| Introduction | |||
| Introduction to Machine Learning 206:00 | |||
| Machine Learning Algorithms | |||
| Simple Linear Regression 132:00 | |||
| Multiple Linear Regression 110:00 | |||
| Gradient Descent 118:00 | |||
| Batch Gradient Descent 65:00 | |||
| Stochastic Gradient Descent 50:00 | |||
| Mini-Batch Gradient Descent 22:00 | |||
| Advanced Linear Regression | |||
| Regression Analysis 1 136:00 | |||
| Regression Analysis 2 114:00 | |||
| Polynomial Regression 27:00 | |||
| Assumptions of Linear Regression 145:00 | |||
| Multicollinearity 132:00 | |||
| Regularized Linear Models | |||
| Regularization Part 1 | Bias Variance Trade-off 122:00 | |||
| Regularization Part 2 | What is Regularization | Paid Zoom Session | 19th May 116:00 | |||
| Ridge Regression Part 1 | Geometric Intuition and Code | Regularized Linear Models 20:00 | |||
| Ridge Regression Part 2 | Mathematical Formulation & Code from scratch | Regularized Linear Models 44:00 | |||
| Ridge Regression Part 3 | Gradient Descent | Regularized Linear Models 19:00 | |||
| Ridge Regression Part 4 | 5 Key Points | Regularized Linear Models 30:00 | |||
| Lasso Regression | Intuition and Code Sample | Regularized Linear Models 29:00 | |||
| Why Lasso Regression creates sparsity? 25:00 | |||
| ElasticNet Regression | Intuition and Code Example | Regularized Linear Models 12:00 | |||
| K Nearest Neighbors | |||
| K-Nearest Neighbors 52:00 | |||
| Coding K Nearest Neighbors from Scratch 57:00 | |||
| How to draw Decision Boundary for classification algorithms 41:00 | |||
| Advanced KNN 126:00 | |||
| Classification Metrics Part 1 | Accuracy and Confusion Matrix | Type 1 and Type 2 Errors 34:00 | |||
| Classification Metrics Part 2 | Precision, Recall and F1 Score 43:00 | |||
| Principle Component Analysis | |||
| Curse of Dimensionality 15:00 | |||
| PCA Part 1 | Geometric Intuition 34:00 | |||
| PCA Part 2 | Problem Formulation and Step by Step Solution 56:00 | |||
| PCA Part 3 | Code Example and Visualization 43:00 | |||
| Eigen Vectors and Eigen Values 100:00 | |||
| Eigen Decomposition + PCA Variants 127:00 | |||
| Singular Value Decomposition 133:00 | |||
| Model Evaluation and Selection | |||
| ROC Curve in Machine Learning 71:00 | |||
| Cross Validation 68:00 | |||
| Data Leakage 145:00 | |||
| Hyperparameter Tuning 106:00 | |||
| Naive Bayes | |||
| Session 1 on Naive Bayes 120:00 | |||
| Session 2 on Naive Bayes 133:00 | |||
| Session 3 on Naive Bayes 118:00 | |||
| Logistic Regression | |||
| Logistic Regression 128:00 | |||
| Multiclass Classification using Logistic Regression 112:00 | |||
| Maximum Likelihood Estimation 127:00 | |||
| Logistic Regression 126:00 | |||
| Logistic Regression Hyperparameters 13:00 | |||
| Support Vector Machine | |||
| SVM Part 1 - Hard Margin SVM 102:00 | |||
| SVM Part 2 | Soft Margin SVM 106:00 | |||
| Constrained Optimization Problem 120:00 | |||
| SVM Dual Problem 105:00 | |||
| Maths Behind SVM Kernels 119:00 | |||
| Decision Trees | |||
| Session 1 on Decision Trees 127:00 | |||
| Session 2 on Decision Trees 118:00 | |||
| Session 3 on Decision Trees | Pruning 100:00 | |||
| Awesome Decision Tree Visualization using dtreeviz library 19:00 | |||
| Random Forest | |||
| Introduction to Ensemble Learning 38:00 | |||
| Bagging | Introduction | Part 1 31:00 | |||
| Bagging Ensemble | Part 2 | Bagging Classifiers 23:00 | |||
| Bagging Ensemble | Part 3 | Bagging Regressor 11:00 | |||
| Session 1 on Random Forest 123:00 | |||
| Session 2 on Random Forest 105:00 | |||
| Gradient Boosting | |||
| Session 1 on Gradient Boosting for Regression 139:00 | |||
| Session 2 on Gradient Boosting | Perspectives 134:00 | |||
| Gradient Boosting Regression Part 2 | Regression Maths Formulation 57:00 | |||
| Gradient Boosting for Classification Part 1 47:00 | |||
| Gradient Boosting for Classification | Geometric Intuition 14:00 | |||
| Gradient Boosting Classification | Maths Formulation 54:00 | |||
| XGBoost | |||
| Introduction to XGBoost | XGBoost Part 1 | |||
| XGBoost for Regression | XGBoost Part 2 47:00 | |||
| XGBoost For Classification | XGBoost Part 3 39:00 | |||
| The Complete Maths of XGBoost | XGBoost Part 3 117:00 | |||
| Advanced XGBoost | |||
| Session on Revisiting XGBoost 89:00 | |||
| Session on XGBoost Regularization 138:00 | |||
| Session 2 on XGBoost Regularization 128:00 | |||
| Session on XGBoost Optimizations 121:00 | |||
| How XGBoost Handles Missing Values 25:00 | |||
| KMeans Clustering | |||
| Session 1 on K Means Clustering 137:00 | |||
| Session 2 on KMeans Clustering 148:00 | |||
| Session 3 on KMeans Clustering 133:00 | |||
| K-Means Clustering Algorithm From Scratch In Python 34:00 | |||
| Unsupervised Machine Learning | |||
| DBSCAN 121:00 | |||
| Hierarchical Clustering 130:00 | |||
| Gaussian Mixture Models 141:00 | |||
| Gaussian Mixture Models 2 136:00 | |||
| Session on T-SNE 131:00 | |||
| Session 2 on T-SNE 115:00 | |||
| Other Boosting Techniques | |||
| Introduction to LightGBM 127:00 | |||
| LightGBM (GOSS & EFB) 139:00 | |||
| Catboost Practical Introduction | |||
| CatBoost - Practical Introduction 123:00 | |||
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