Supervised Learning with Python

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Gain a thorough understanding of supervised learning algorithms by developing use cases with Python. This book provides an in-depth review of Python code, datasets, best practices, resolution of common issues and pitfalls, and practical knowledge of implementing algorithms. You'll start with an introduction to machine learning, highlighting the differences between supervised, semi-supervised and unsupervised learning. Next, you'll review regression problems such as the mathematics behind regression, algorithms like linear regression, decision tree, random forest, and neural networks. Python implementation is provided for all the algorithms, including using logistic regression, naïve Bayes, knn, SVM, decision tree, random forest, and neural networks. We finally conclude with an end-to-end model deployment. After reading Supervised Learning with Python you'll have a broad understanding of supervised learning and its implementation, and be to run the code and extend it in innovative manner. What You'll LearnReview the fundamental building blocks and concepts of supervised learning using Python Apply best practices for debugging and improving supervised learning modelsSolve issues around overfitting, feature engineering, data cleansing, and cross-validation for building best fit modelsInterpret supervised learning models and understand what is happening behind the scenesAvoid the common pitfalls while creating a supervised learning modelWho This Book Is For Data scientists or data analysts interested in best practices and standards for supervised learning, and using classification algorithms and regression techniques to develop predictive models.