Enroll Course: https://www.udemy.com/course/ittensive-python-machine-learning-classification/
Are you looking to elevate your machine learning skills, particularly in the realm of classification and ensemble methods? The Udemy course, “Машинное обучение: классификация и ансамбли на Python” (Machine Learning: Classification and Ensembles in Python), offers a comprehensive journey into building robust predictive models. This course is designed to take you from the fundamentals to advanced techniques, using a practical Kaggle competition (Prudential Insurance Scoring) as its guiding example.
The course is expertly divided into two parts. The first part lays a solid foundation. It begins with understanding different types of machine learning tasks and how to frame them. You’ll delve into the core principles of building ML models, focusing on minimizing prediction errors. Essential concepts like basic metrics and foundational models such as linear and logistic regression are thoroughly explained. This section also introduces various classification metrics, models, and the power of ensembles.
The second part transitions into hands-on application, covering a wide array of critical techniques:
* **Exploratory Data Analysis (EDA):** Discovering hidden patterns and dependencies within your data.
* **Classification Metrics:** Mastering accuracy, precision, recall, F1-score, quadratic kappa, and confusion matrices to evaluate model performance effectively.
* **Data Cleaning & Optimization:** Learning to clean your data and optimize memory usage for efficiency.
* **Clustering & Nearest Neighbors:** Exploring data segmentation and the k-Nearest Neighbors (k-NN) algorithm.
* **Regression Models:** Understanding simple and hierarchical logistic regression.
* **Model Selection:** Finding the optimal model through k-NN and other methods.
* **Support Vector Machines (SVM):** Harnessing the power of SVMs for classification.
* **Tree-Based Methods:** Diving into Decision Trees, Random Forests (Bagging), XGBoost, Gradient Boosting, LightGBM, and CatBoost – understanding their strengths and applications.
* **Stacking Ensembles:** Combining multiple models for superior predictive power through stacking and voting.
* **Kaggle Submission:** Learning how to prepare and submit your results for competitions.
This course is highly recommended for anyone serious about mastering classification and ensemble techniques in Python. Whether you’re preparing for Kaggle competitions or aiming to build sophisticated predictive systems, this course provides the knowledge and practical experience needed to succeed. The instructor’s approach, grounded in real-world application, ensures that you’re not just learning theory but also how to apply it effectively.
Enroll Course: https://www.udemy.com/course/ittensive-python-machine-learning-classification/