Python Programming Workbook For Machine Learning With Pytorch And Scikit-Learn

✍ Scribed by French , Adrian M.

• Publisher: Independently Published
• Year: 2024
• Tongue: English
• Leaves: 258
• Category: Library

✦ Synopsis

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
By the end of this workbook, you'll be able to
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
Whether you're a beginner eager to enter the machine learning field or an experienced programmer looking to broaden your skillset, this workbook is your essential companion!

✦ Table of Contents

Chapter 1: Introduction to Machine Learning
Chapter 2: Python for Machine Learning
Chapter 3: Data Preprocessing for Machine Learning
Chapter 4: Introduction to scikit-learn
Chapter 5: Regression Analysis
Chapter 6: Classification
Chapter 7: Ensemble Methods
Chapter 8: Model Selection and Hyperparameter Tuning
Chapter 9: Dimensionality Reduction Techniques
Chapter 10: Introduction to Deep Learning
Chapter 11: Introduction to PyTorch
Chapter 12: Training Neural Networks with PyTorch
Chapter 13: Convolutional Neural Networks (CNNs) for Image Classification
Chapter 14: Recurrent Neural Networks (RNNs) for Text & Sequence Data
Chapter 15: Advanced Deep Learning Architectures
Chapter 16: Fine-tuning Pre-trained Deep Learning Models
Chapter 17: Case Studies - Applying Machine Learning to Real-World Problems
Chapter 18: Project Development Workflow
Chapter 19: Conclusion and Resources
Appendix