Machine Learning with PyTorch & Scikit-Learn: Develop ML & Deep Learning Models in Python

Description

Machine Learning with PyTorch & Scikit-Learn: Develop ML & Deep Learning Models in Python (Packt, 2022)

PyTorch Scikit-Learn ML is a comprehensive course designed to teach you how to build powerful machine learning and deep learning models in Python using two of the most popular libraries—PyTorch and Scikit-Learn. Whether you’re a beginner or experienced in the field of AI, this course provides you with hands-on experience in creating and deploying machine learning models using state-of-the-art techniques.

What You’ll Learn in This Course

• Introduction to machine learning algorithms with PyTorch and Scikit-Learn
• Building and training deep learning models using PyTorch
• Exploring supervised and unsupervised learning techniques
• Implementing neural networks and CNNs for image classification tasks
• Handling large datasets and performing data preprocessing with Scikit-Learn
• Hyperparameter tuning and model optimization techniques
• Deploying machine learning models for real-world applications
• Advanced techniques in reinforcement learning and deep learning

Who Should Take This Course?

• Python developers looking to expand their knowledge in machine learning
• Data scientists and engineers who want to deepen their understanding of PyTorch and Scikit-Learn
• Students and professionals aiming to break into the field of machine learning and artificial intelligence
• Anyone interested in developing deep learning models for real-world problems

Course Description:

This course provides a step-by-step guide to mastering machine learning with PyTorch and Scikit-Learn. You will learn how to handle various machine learning tasks such as classification, regression, and clustering while exploring both classical machine learning methods and modern deep learning techniques. The course includes practical examples and projects that showcase the capabilities of these two libraries in solving real-world data science challenges.

Starting with the fundamentals, the course walks you through the process of data preprocessing, feature engineering, and model evaluation. As you progress, you will gain hands-on experience with training deep learning models, implementing Convolutional Neural Networks (CNNs) for image recognition, and optimizing hyperparameters for better performance. By the end of the course, you will have the skills to build, train, and deploy machine learning models using Python’s powerful libraries, PyTorch and Scikit-Learn.

About the Author

The author of this course is an experienced data scientist and machine learning engineer with a background in deep learning and AI development. Having worked with multiple industries to develop cutting-edge AI solutions, the author brings extensive knowledge and practical insights to help learners master machine learning concepts and tools efficiently.

Explore These Valuable Resources

• PyTorch Official Site
• Scikit-Learn Documentation
• TensorFlow – A Deep Learning Framework

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