Supervised Learning with Python
- Paperback: 392 pages
- Publisher: WOW! eBook (October 22, 2020)
- Language: English
- ISBN-10: 1484261550
- ISBN-13: 978-1484261552
Supervised Learning with Python: Concepts and Practical Implementation Using Python
Gain a thorough understanding of supervised learning algorithms by developing use cases with Python. You will study supervised learning concepts, Python code, datasets, best practices, resolution of common issues and pitfalls, and practical knowledge of implementing algorithms for structured as well as text and images datasets.
You’ll start with an introduction to machine learning, highlighting the differences between supervised, semi-supervised and unsupervised learning. In the following chapters you’ll study regression and classification problems, mathematics behind them, algorithms like Linear Regression, Logistic Regression, Decision Tree, KNN, Naïve Bayes, and advanced algorithms like Random Forest, SVM, Gradient Boosting and Neural Networks. Python implementation is provided for all the algorithms. You’ll conclude with an end-to-end model development process including deployment and maintenance of the model.
What You’ll Learn
- Review the fundamental building blocks and concepts of supervised learning using Python
- Develop supervised learning solutions for structured data as well as text and images
- Solve issues around overfitting, feature engineering, data cleansing, and cross-validation for building best fit models
- Understand the end-to-end model cycle from business problem definition to model deployment and model maintenance
- Avoid the common pitfalls and adhere to best practices while creating a supervised learning model using Python
After reading Supervised Learning with Python you’ll have a broad understanding of supervised learning and its practical implementation, and be able to run the code and extend it in an innovative manner.