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Home Python ebooks Data Analysis From Scratch With Python: Beginner Guide using Python, Pandas, NumPy, Scikit-Learn, IPython, TensorFlow and Matplotlib

Data Analysis From Scratch With Python: Beginner Guide using Python, Pandas, NumPy, Scikit-Learn, IPython, TensorFlow and Matplotlib

August 07, 2020 Python ebooks,

Data Analysis From Scratch With Python: Beginner Guide using Python, Pandas, NumPy, Scikit-Learn, IPython, TensorFlow and Matplotlib

Peters Morgan

******Free eBook for customers who purchase the print book from Amazon****** Are you thinking of becoming a data analyst using Python? If you are looking for a complete guide to data analysis using Python language and its library that will help you to become an effective data scientist, this book is for you. From AI Sciences Publisher Our books may be the best one for beginners; it's a step-by-step guide for any person who wants to start learning Artificial Intelligence and Data Science from scratch. It will help you in preparing a solid foundation and learn any other high-level courses. To get the most out of the concepts that would be covered, readers are advised to adopt hands on approach, which would lead to better mental representations. Step By Step Guide and Visual Illustrations and Examples The Book give complete instructions for manipulating, processing, cleaning, modeling and crunching datasets in Python. This is a hands-on guide with practical case studies of data analysis problems effectively. You will learn pandas, NumPy, IPython, and Jupiter in the Process. Target Users This book is a practical introduction to data science tools in Python. It is ideal for analyst's beginners to Python and for Python programmers new to data science and computer science. Instead of tough math formulas, this book contains several graphs and images. What's Inside This Book?
Introduction
Why Choose Python for Data Science & Machine Learning
Prerequisites & Reminders
Python Quick Review
Overview & Objectives
A Quick Example
Getting & Processing Data
Data Visualization
Supervised & Unsupervised Learning
Regression
Simple Linear Regression
Multiple Linear Regression
Decision Tree
Random Forest

Classification
Logistic Regression
K-Nearest Neighbors
Decision Tree Classification
Random Forest Classification

Clustering
Goals & Uses of Clustering
K-Means Clustering
Anomaly Detection

Association Rule Learning
Explanation
Apriori

Reinforcement Learning
What is Reinforcement Learning
Comparison with Supervised & Unsupervised Learning
Applying Reinforcement Learning

Neural Networks
An Idea of How the Brain Works
Potential & Constraints
Here's an Example

Natural Language Processing
Analyzing Words & Sentiments
Using NLTK

Model Selection & Improving Performance
Sources & References
Frequently Asked Questions

Q: Is this book for me and do I need programming experience? A: if you want to smash Python for data analysis, this book is for you. Little programming experience is required. If you already wrote a few lines of code and recognize basic programming statements, you'll be OK.

Q: Does this book include everything I need to become a data science expert? A: Unfortunately, no. This book is designed for readers taking their first steps in data analysis and further learning will be required beyond this book to master all aspects.

Q: Can I have a refund if this book is not fitted for me? A: Yes, Amazon refund you if you aren't satisfied, for more information about the amazon refund service please go to the amazon help platform. We will also be happy to help you if you send us an email at contact@aisciences.net.

AI Sciences Company offers you a free eBooks at http: //aisciences.net/free/

Categories:

Computers\\Algorithms and Data Structures: Pattern Recognition

Year:

2018

Edition:

Kindle Edition

Publisher:

AI Sciences LLC

Language:

english

Pages:

153 / 104