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Hands-On Data Science and Python Machine Learning

Hands-On Data Science and Python Machine Learning

By : Frank Kane
3.8 (4)
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Hands-On Data Science and Python Machine Learning

Hands-On Data Science and Python Machine Learning

3.8 (4)
By: Frank Kane

Overview of this book

Join Frank Kane, who worked on Amazon and IMDb’s machine learning algorithms, as he guides you on your first steps into the world of data science. Hands-On Data Science and Python Machine Learning gives you the tools that you need to understand and explore the core topics in the field, and the confidence and practice to build and analyze your own machine learning models. With the help of interesting and easy-to-follow practical examples, Frank Kane explains potentially complex topics such as Bayesian methods and K-means clustering in a way that anybody can understand them. Based on Frank’s successful data science course, Hands-On Data Science and Python Machine Learning empowers you to conduct data analysis and perform efficient machine learning using Python. Let Frank help you unearth the value in your data using the various data mining and data analysis techniques available in Python, and to develop efficient predictive models to predict future results. You will also learn how to perform large-scale machine learning on Big Data using Apache Spark. The book covers preparing your data for analysis, training machine learning models, and visualizing the final data analysis.
Table of Contents (11 chapters)
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Conventions

In this book, you will find a number of text styles that distinguish between different kinds of information. Here are some examples of these styles and an explanation of their meaning.

Code words in text, database table names, folder names, filenames, file extensions, pathnames, dummy URLs, user input, and Twitter handles are shown as follows: "We can measure that using the r2_score() function from sklearn.metrics."

A block of code is set as follows:

import numpy as np 
import pandas as pd 
from sklearn import tree 
 
input_file = "c:/spark/DataScience/PastHires.csv" 
df = pd.read_csv(input_file, header = 0) 

When we wish to draw your attention to a particular part of a code block, the relevant lines or items are set in bold:

import numpy as np
import pandas as pd
from sklearn import tree

input_file = "c:/spark/DataScience/PastHires.csv"
df = pd.read_csv(input_file, header = 0)

Any command-line input or output is written as follows:

spark-submit SparkKMeans.py  

New terms and important words are shown in bold. Words that you see on the screen, for example, in menus or dialog boxes, appear in the text like this: "On Windows 10, you'll need to open up the Start menu and go to Windows System | Control Panel to open up Control Panel."

Warnings or important notes appear like this.
Tips and tricks appear like this.
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Hands-On Data Science and Python Machine Learning
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