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Data Analysis from Scratch with Python: Step-By-Step Guide
***** BUY NOW (will soon return to 19.77 $) ********* MONEY BACK GUARANTEE BY AMAZON *****Are you thinking of becoming a data analyst using Python? (For Beginners) If you are looking for a complete guide to data analysis using Python language 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 ExamplesThe 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.What's Inside This Book?
Introduction
Why Choose Python for Data Science & Machine Learning
Prerequisites & Reminders
Python Quick Review
Overview & Objectives
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.***** MONEY BACK GUARANTEE BY AMAZON *****Editorial Reviews"Peters managed to combine theory with practice for absolute beginners. I was impressed to see how easy itis to implement the more simple algorithms from scratch, and while I would notdo that normally, it was very helpful to understand what's going on behind thescenes.
I would recommend this for beginners in data analysisand machine learning."
- Mike Beaunier, Data Scientist at Google.
Introduction
Why Choose Python for Data Science & Machine Learning
Prerequisites & Reminders
Python Quick Review
Overview & Objectives
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.***** MONEY BACK GUARANTEE BY AMAZON *****Editorial Reviews"Peters managed to combine theory with practice for absolute beginners. I was impressed to see how easy itis to implement the more simple algorithms from scratch, and while I would notdo that normally, it was very helpful to understand what's going on behind thescenes.
I would recommend this for beginners in data analysisand machine learning."
- Mike Beaunier, Data Scientist at Google.
152 pages, Paperback
Published August 18, 2017
166 people are currently reading
67 people want to read
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Displaying 1 of 1 review
February 7, 2019
Good book to begin with. Nice examples
This is a very good book for beginners to understand different algorithms in Python. Code has been provided and the explanation is simply superb
This is a very good book for beginners to understand different algorithms in Python. Code has been provided and the explanation is simply superb
Displaying 1 of 1 review