Аннотация
The following chapters will discuss the core concepts of “Machine Learning” models that are being developed and advanced using Python programming language. This book will provide you overarching guidance on how you can use Python to develop machine learning models using Scikit-Learn and TensorFlow machine learning libraries. You will start this book with gaining a solid understanding of the basics of the machine learning technology and types of machine learning models with an emphasis on the deep learning model called “Artificial Neural Network.” It is important to master the concepts of machine learning technology and learn how researchers are breaking the boundaries of data science to mimic human intelligence in machines using a wide variety of learning algorithms. The power of machine learning technology has already started to manifest in our environment to enhance the usability of our household objects.
In Chapter 2 titled, “Machine Learning Algorithms,” you will learn the nuances of “12 of the most popular machine learning algorithms,” in a very easy to understand language that requires no background in Python coding language and in fact might spike your interest in this field of research. You will also learn the fundamental machine learning algorithms in great detail, namely, supervised, unsupervised, and reinforcement machine learning algorithms that serve as the skeleton of hundreds of machine learning algorithms being developed everyday.
In Chapter 3 titled, “Data pre-processing and Creation of training data set,” you will learn all about the most time consuming and critical aspect of developing a machine learning model i.e. Data pre-processing and splitting the processed data set into training and testing subsets. A detailed step by step description of different stages involved in the process of creating training data set has been provided to give you an end to end understanding of this most critical aspect of the development of machine learning algorithms.
In chapter 4, titled “Scikit-Learn,” we deep dive into the functioning of Scikit-Learn library along with the pre-requisites required to develop machine learning model using Scikit-Learn library. A detailed walkthrough with an open source database using illustrations and actual Python code that you can try hands on by following the instructions in this book. There is no better way to learn than to actually get your hands dirty and get real experience of the task. There is also guidance provided on resolving nonlinear issues with “k-nearest neighbor” and “kernel trick algorithms” in this book.
You will learn the entire process of creating of Neural Network models on TensorFlow machine learning platform, using open source data set as example, along with the actual Python code used for the development, in the chapter 5, titled “Training Neural Network with TensorFlow.” Neural Networks are characterized by a “single neuron like entity of the machine that is capable of learning the expected output for a given input from training data sets.” TensorFlow is built on Python and touted as a simple and flexible architecture that supports development of machine learning ideas from “concept to code to state-of-the-art models and to publication” in short period of time. Finally, as an added bonus you will learn some Python tips and tricks to take your machine learning programming game to the next level.
So, breathe in, breathe out, and let's begin!
There are plenty of books on this subject on the market, thanks again for choosing this one! Every effort was made to ensure it is full of as much useful information as possible, please enjoy!
Комментарии к книге "Learn Python"