Hyper-Parameter Tuning using Optuna
Selecting the appropriate values for the algorithmic parameters is a daunting task. Many try to get the optimal values by trial-n-error. In this tutorial, I will show…
Thilakraj DevadigaThilakraj Devadiga
7 articles
Machine Learning
Machine learning tutorials in Python: KNN, Random Forest, K-Means customer segmentation, AutoML with H2O, Optuna hyper-parameter tuning, and real-world case studies in healthcare and insurance.
Thilakraj Devadiga
A Deep Dive into KNN
Learn an efficient implementation of KNN (K-Nearest Neighbours) algorithm in Python through a real-life case study of classifying a cancer as benign or malignant.
B. Srinivas
Mall Customer Segmentation
Malls do customer profiling to target the potential customers with irresistible offers whenever they visit the mall. In this tutorial, you will learn several EDA and…
Nisha Waghela
AutoML using H2O
Do you want to show your skills in machine learning model development? And do not understand the implementation of a plethora of ML algorithms available in the…
Anjani Reddy
Random Forest — An Efficient Python Implementation
The Random Forest algorithm is considered one of the best algorithms for classification. Do you wish to learn an in-depth efficient implementation of this algorithm in…
B. Srinivas
ML for Breast Cancer Tumor Diagnosis
Medical datasets are typically small, primarily because of the patient's consent and privacy. They usually contain many dependent features. Building highly predictive…
Vedant Mane & Thilakraj Devadiga
Machine Learning in Insurance Sector
Developing a Machine Learning application in the insurance sector is the most challenging amongst BFSI groups. This article will show the application of Gradient…
Thilakraj Devadiga