Enroll Course: https://www.udemy.com/course/ittensive-python-machine-learning-classification/
If you’re venturing into the world of machine learning, especially in the context of data classification and ensemble techniques, the course “Машинное обучение: классификация и ансамбли на Python” on Coursera is an exceptional resource. Designed in two comprehensive parts, this course guides learners from fundamental concepts to advanced practical applications within the context of insurance scoring for Prudential and Kaggle competitions.
The first part of the course lays a solid foundation by covering essential steps such as understanding different types of data problems, preparing data, and minimizing prediction errors through models like linear and logistic regression. It also introduces core metrics and simple models, setting the stage for more complex techniques.
In the second part, students dive deep into exploratory data analysis (EDA), classification metrics like precision, recall, F1 score, and confusion matrices. Practical skills such as data cleaning, memory optimization, clustering, and nearest neighbors are explored thoroughly. The course then advances into sophisticated models including Support Vector Machines (SVM), decision trees, random forests, and gradient boosting algorithms like XGBoost, LightGBM, and CatBoost.
A significant highlight is the module on ensemble stacking, which demonstrates how to implement voting strategies and select the best model ensemble for competitions. The course culminates with guidance on submitting results to Kaggle, making it highly tailored to real-world data science challenges.
I highly recommend this course to data scientists and machine learning enthusiasts seeking a practical, step-by-step guide to classification and ensemble methods in Python. Its structured approach, combined with hands-on exercises and industry relevance, makes it a must-have in your learning arsenal.
Enroll Course: https://www.udemy.com/course/ittensive-python-machine-learning-classification/