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If you’re looking to master the intricacies of neural networks and deep learning, especially in the context of image segmentation and classification, the course ‘Машинное обучение: нейросети и глубокое обучение на Python’ on Udemy is an excellent choice. This comprehensive course is divided into two parts, making it suitable for both beginners and those with some experience in machine learning.
In the first part, you’ll learn the fundamentals of working with data, from understanding different types of tasks and their formulation to minimizing prediction errors with machine learning models. It covers essential concepts such as basic metrics, linear and logistic regression, and prepares you for more advanced topics.
The second part dives into practical applications, focusing on real-world examples like exploratory data analysis (EDA), data cleaning, image processing, and model management using HDF5. You’ll explore various neural network architectures such as LeNet, AlexNet, VGG, Inception, ResNet, and DenseNet, along with techniques for image segmentation using MobileNet, Unet, PSPNet, and FPN. The course also covers ensemble methods, model evaluation metrics like F1 and D-coefficient, and concludes with preparing your models for Kaggle competitions.
This course is highly recommended for anyone interested in deepening their understanding of neural networks and deep learning on Python, especially if you’re aiming to participate in data science competitions or pursue advanced projects in image analysis. The thorough coverage of both theory and practical implementation ensures you gain valuable skills to enhance your machine learning toolkit.
Enroll Course: https://www.udemy.com/course/ittensive-python-machine-learning-neural/