Supervised Learning with Python: Concepts and Practical Implementation Using Python - Softcover

Über die Autorin bzw. den Autor

Vaibhav Verdhan has 12+ years of experience in Data Science, Machine Learning and Artificial Intelligence. An MBA with engineering background, he is a hands-on technical expert with acumen to assimilate and analyse data. He has led multiple engagements in ML and AI across geographies and across retail, telecom, manufacturing, energy and utilities domains. Currently he resides in Ireland with his family and is working as a Principal Data Scientist.

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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.

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.

You will:

• 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