Python Machine Learning: Algorithm Design & Practical Code Execution

This book will teach you how to pick the right tool for solving problems you just encountered! You will also discover how to solve a problem according to the data that you are using, based on different learning styles, characteristics or requirements using Python.

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In this book you will discover:

·       Platforms and Libraries

·       Regression & Linear Regression

·       Polynomial Regression & Lasso Regression

·       Ridge Regression & Perceptron Regression

·       Classification Algorithms & Logistic Regression

·       Naive Bayes & Support Vector Machines

·       K Nearest Neighbors

·       Decision Trees and Random Forests

·       Neural Networks

·       Convolutional Neural Networks

·       Dimensionality Reduction & Linear Discriminant Analysis

·       T Distributed Stochastic Neighbor Embedding

·       Clustering Algorithms & K Means

·       Gaussian Mixtures & Hierarchical Clustering

·       Affinity Propagation & Other Machine Learning Algorithms

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