Learning scikit-learn: Machine Learning in Python Free PDF Downloading

Ecommercetutors is providing the programming world with quality books that are not easily available free of cost. In this guide, you guys will learn a number of methods to build Machine Learning applications which resolve dissimilar real world problems, from document organization to image recognition. In this guide, with the help of Python, a simple, famous, and broadly used programming language, and scikit learn, an free source Machine Learning library. In each portion, this guide will provide a different Machine Learning setting and a couple of well-studied procedures. It also shows step-by-step samples that use Python and scikit learn to solve different tasks. Furthermore, we will also present you tips and techniques to improve algorithm efficiency.

In the first portion of this guide, we’ll learn about the Introduction of Machine Learning which gives you the main idea to solve a simple classification problem like cultivated flower species based on its physiognomies.

In the second portion of this guide, Supervised Learning is well explained that introduces four classification approaches:

  1. Support Vector Machines
  2. Naive Bayes
  3. Decision trees
  4. Random Forests

These approaches are used to identify faces, categorize texts, and describe the sources for surviving from the deadly Titanic accident. It also provides Linear Models and reconsiders Support Vector Machines and Random Forests, by using them you can predict house prices in Boston city.

Thirdly, Unsupervised Learning which describes approaches for dimensionality reduction with Main Component Study to envisage higher dimensional data in merely two dimensions. It also presents clustering methods to group illustrations of handwritten digits rendering to a similarity measure by using the k-means algorithm.

In the final chapter of this guide, Advanced Features are described which shows the method to preprocess the data and choose the best structures for learning, a process called Feature Selection. In this, you’ll also get to know about the Model Selection which means selecting the best technique parameters, using the offered data and equivalent computation.

To run examples of the book, you will require a running Python atmosphere, in which scikit-learn libraries, NumPy, and SciPy mathematical libraries are included.

Consequently, this book is envisioned for those programmers who need to add Machine Learning and data based approaches to their programming abilities.

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