Python Programming Workbook For Machine Learning With Pytorch And Scikit-Learn
Python Programming Workbook For Machine Learning With Pytorch And Scikit-Learn : An Essential Beginners Guide To Learn How To Build Deep Learning Models With Python (The Essential Python Workbooks) by Adrian M. French
English | May 19, 2024 | ISBN: N/A | ASIN: B0D4MFHWSZ | 258 pages | EPUB | 0.48 Mb
English | May 19, 2024 | ISBN: N/A | ASIN: B0D4MFHWSZ | 258 pages | EPUB | 0.48 Mb
Empower yourself to tackle real-world machine learning challenges with this comprehensive Python workbook!This practical guide equips you with the skills and knowledge to build effective machine learning models using popular libraries like PyTorch and scikit-learn. Through a series of hands-on exercises, you'll gain a deep understanding of essential concepts and techniques, while simultaneously developing your Python programming proficiency.
Key Features:
- Master the Fundamentals: Grasp the core principles of machine learning, including data preprocessing, model selection, evaluation metrics, and project life cycle management.
- Dive into PyTorch: Explore the power of PyTorch for building neural networks. Master tensors, autograd, and the core functionalities to design and train custom deep learning architectures.
- Harness the Power of scikit-learn: Leverage scikit-learn's extensive toolkit for traditional machine learning algorithms. Learn to implement logistic regression, gradient boosting techniques like XGBoost and LightGBM, and more.
- Data Wrangling Mastery: Discover effective data transformation techniques with NumPy and Pandas, the workhorses of data manipulation in Python. Learn feature engineering to prepare your data for optimal model performance.
- Visualization Powerhouse: Utilize Matplotlib to create informative visualizations that aid in data exploration, model evaluation, and clear communication of results.
- Project Development Workflow: Gain insights into a structured approach to machine learning project development. Learn to efficiently navigate the stages of problem definition, data acquisition, model selection, training, evaluation, and deployment.
- Advanced Techniques: Delve into advanced topics like convolutional neural networks (CNNs) for image analysis, recurrent neural networks (RNNs) for sequence modeling with PyTorch, and Long Short-Term Memory (LSTM) networks for handling long-term dependencies
- Confidently build and train machine learning models using Python Implement a variety of traditional and deep learning algorithms with PyTorch and scikit-learn
- Preprocess and transform data effectively for optimal machine learning performance
- Create insightful data visualizations to better understand your models and findings
- Develop a systematic approach to machine learning project development
- Apply advanced techniques like CNNs, RNNs, and LSTMs to complex tasks