Master Generative AI with 10+ Real-world Projects in 2025!
Tutorial on tree based algorithms, which includes decision trees, random forest, ensemble methods and its implementation in R & python.
A tutorial for convolution neural networks to identify images. Learn about deep learning for computer vision and implement CNNs using graphlab in python.
This tutorial illustrates use of recommendation engines in the banking industry with practicals done in R. It also explains types of recommendation engines.
Boruta package is a wrapper algorithm around random forest for important variables and used to perform feature selection in R for data science.
Learn how Principal Component Analysis (PCA) can help you overcome challenges in data science projects with large, correlated datasets. Read Now!
This article explains artificial neural networks and fundamentals of deep learning. Learn about forward and backward propagation.
Take your GBM models to the next level with hyperparameter tuning. Find out how to optimize the bias-variance trade-off in gradient boosting algorithms.
Learn how multinomial and ordinal logistic regression in R are used to deal with multi-level independent variables. Read Now!
Explore Ridge and Lasso Regression, their mathematical principles & practical applications in Python to enhance regression skills. Read Now!
XGBoost is an efficient gradient boosting framework. Say goodbye to lengthy feature engineering as XGBoost in R takes new heights!
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