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AI theory, architecture, models
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Module 3
Classical and Ensemble Learning Algorithms
1
Regularized Linear Regression
Implement linear regression and apply L1/L2 regularization to prevent overfitting
2
Implementing Logistic Regression for Binary Classification
Implement logistic regression for binary classification tasks
3
Implementing Naive Bayes Classifiers
Implement Naive Bayes classifiers for probabilistic classification
4
SVMs and Kernel Methods
Apply support vector machines (SVMs) with different kernel methods
5
Building Decision Trees with Entropy and Information Gain
Build decision trees using entropy and information gain criteria
6
Implementing K-Nearest Neighbors (KNN)
Implement the K-nearest neighbors (KNN) algorithm
7
Ensemble Learning: Bagging and Boosting
Explain the principles of ensemble learning, including bagging and boosting
8
Random Forest Classifier with Bagging
Implement a random forest classifier using bagging
9
Implementing Boosting Algorithms from Scratch
Implement AdaBoost and Gradient Boosting Machines (GBMs) from first principles
10
Mastering XGBoost, LightGBM, and CatBoost
Apply high-performance boosting libraries: XGBoost, LightGBM, and CatBoost
Previous module
Core Machine Learning Concepts
Next module
Unsupervised Learning and Representation