Machine Learning Made Easy Using Python

234 Pages·2020·75.45 MB·English

by  Rahul Mula

PYTHON
MACHINE LEARNING
Machine learning is one of the skills
considered must for the future yeors. As
tosks are increasing it has became
time consuming to program, machine learning
allows machines to learn on their own and
produce the same results. Machine
learning allows machines to learn
on their own through feeding them data.
I assume that you have prior
knowledge of Python programming and
data science, if you don't
you can check these books on the next page.
A complete guidebook for anyone who
wants to master machine learning
with Python.
Rahul Mula
Mochine Learning with Python
by Rahul Mula
© 2020 Machine Learning with Python
All rights reserved. No portion of this
book may be reproduced in any form
without permission from the copyright
holder, except as permitted by U.S.
copyright law.
Cover by Rahul Mula.
All the programs written in this book
are tested and verified by the author.
Cover Template from freepik.com
ISBN : 979-8-58-426755-1
/-----------------------------------------------------------------------------------------X
MAKE SURE TO CHECK THEM OUT
<_____________________________________________________>
Python For Beginner
A beginners guide to programming
with python
Data Science with Python
Learn how to perform tasks like data
processing, cleansing, analysis and
visualization
Why should you learn machine learning? or what are
its uses? Would be the questions that may come to
your mind. The answer is simple, think that you are
given a data from a online store about its products
and recommend products. The data has a product
name, its category, its quantity, and rate columns
with several hundred rows of products. If you want to
perform some analysis like the product which is most
purchased in that day, it will take a lot of time to do it
manually. To ease up these tasks, we use data
analysis, i.e. we run a program with codes to perform
a certain data analysis. The computer runs the
program and we get the output in just few seconds.
Then we classify the user and suggest it products
based on preferable categories on the basis of it's
previous search results.So, How to do that? Well, we
need to learn Data Science and Machine Learning to
perform those tasks.
Businesses S organizations are trying to deal with it
by building intelligent systems using the concepts and
methodologies from Data science, Data Mining and
Machine learning. Among them, machine learning is
the most exciting field of computer science. It would
not be wrong if we call machine learning the
application and science of algorithms that provides
sense to the data.
This book is prepared especially for beginners (at
Data Science and Machine Learning), but you should
be familiar with programming in Python. We will work
with packages and modules like NumPy, SciPy,
Pandas, Matplotlib, Scikit Learn, etc. to perform
analysis and other tasks. I kept this book open to the
basic concepts of data science to help the beginners
to understand everything but the book only covers
data science concepts prior for machine learning, as
the name suggests, the book is not for you if you're
looking for data science, you check the other books
page to find that. I also included advanced topics to
not limit you to the basics. Machine learning,
algorithms, data science, etc. moy seem tough end
boring, but as you handle more end more data, you'll
ploy with it!
( )
contents
PANDAS 1
03
• Features of Pandas Library
CHAPTER pandas • Series
• Data Frames
MATPLOTLIB 1
06
• Features of matplotlib
maltpMib . Data visualizationp
CHAPTER
• PyPlot in matplotlib
( )
contents
SCIKIT LEARN 1
• Features of Scikit-learn library
• How to work with data?
CHAPTER
k • Why use Python?
08 b? TYPES OF MACHINE LEARNING 1
• Supervised learning
CH1 AAPPTTEERR O • Unsupervised learning
k • Deep learning
MATHEMATICS FOR MACHINE LEARNING 1
IDIID • Data instances
I I I • Statistics
CHAPTER
k • Probability
SCIKIT LEARN ALGORITHMS 1
• Regression algorithm
CHAPTER • Classification algorithm
• Clustering algorithm
IMPORTING DATA I
• Importing CSV data
CHAPTER I I I I • Importing JSON data
I • Importing Excel data
r
DDAATTAA OOPPEERRAATTIIOONNSS 1
• NNuummPPyy ooppeerraattiioonnss
•• PPaannddaass ooppeerraattiioonnss
CHAPTER
•• CClleeaanniinngg ddaattaa
V
( )
contents
DATA ANALYSIS 8 PROCESSING 1
• Data analytics
• Correlations between attributes
CHAPTER
k • Skewness of the data
DATA VISUALIZATION 1
14
• Plotting data
• Univaritae plots
CHAPTER
• Multivariate plots
r
CLASSIFICATION 1
16
• Decision tree
• Linear regression
CHAPTER
• Naive Bayes
( )
contents
PERFORMANCE 8 METRICS
20
• Calculating the model
CHAPTER • Improving the model
• Saving and loading models
( )
contents