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Data Analysis from Scratch with Python: Beginner Guide for Data Science, Data Visualization, Regression, Decision Tree, Random Forest, Reinforcement Learning, Neural Network and NLP using Python
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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 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.
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.
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 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.
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.
153 pages, Kindle Edition
Published August 13, 2018
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