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Data Science from Scratch with Python: Step-By-Step Guide
***** BUY NOW (will soon return to 24.77 $) ***** MONEY BACK GUARANTEE BY AMAZON (See Below FAQ) *****Are you thinking of learning data science from scratch using Python? (For Beginners)If you are looking for a complete step-by-step guide to data science using Python from scratch, this book is for you. After his great success with his first book "Data Analysis from Scratch with Python," Peter Morgan publishes his second book focusing now in data science and machine learning. It is considered by practitioners as the easiest guide ever written in this domain.
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. Readers are advised to adopt a 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.
Target Users
Beginners who want to approach data science, but are too afraid of complex math to start
Newbies in computer science techniques and data science
Professors, lecturers or tutors who are looking to find better ways to explain the content to their students in the simplest and easiest way
Students and academicians, especially those focusing on data science
What's Inside This Book?Part 1: Data Science Fundamentals, Concepts and Algorithms
Introduction
Statistics
Probability
Bayes' Theorem and Na�ve Bayes Algorithm
Asking the Right Question
Data Acquisition
Data Preparation
Data Exploration
Data Modelling
Data Presentation
Supervised Learning Algorithms
Unsupervised Learning Algorithms
Semi-supervised Learning Algorithms
Reinforcement Learning Algorithms
Overfitting and Underfitting
The Bias-Variance Trade-off
Feature Extraction and Selection
Part 2: Data Science in Practice
Overview of Python Programming Language
Python Data Science Tools
Jupyter Notebook
Numerical Python (Numpy)
Pandas
Scientific Python (Scipy)
Matplotlib
Scikit-Learn
K-Nearest Neighbors
Naive Bayes
Simple and Multiple Linear Regression
Logistic Regression
GLM models
Decision Trees and Random forest
Perceptrons
Backpropagation
Clustering
Natural Language Processing
Frequently Asked Questions
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 science and machine learning using Python and further learning will be required beyond this book to master all aspects.
Q: Can I have a refund if this book doesn't fit 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.***** MONEY BACK GUARANTEE BY AMAZON ***** Editorial Reviews"This is a fantastic book on Python-based data science, data analysis, machine learning, Reinforcement learning and deep learning. As a data scientist with more than 10 years, Peter has had long experience in data science and give in this book the key elements.."
- Lei Xia, Data Scientist Expert at Facebook
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. Readers are advised to adopt a 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.
Target Users
Beginners who want to approach data science, but are too afraid of complex math to start
Newbies in computer science techniques and data science
Professors, lecturers or tutors who are looking to find better ways to explain the content to their students in the simplest and easiest way
Students and academicians, especially those focusing on data science
What's Inside This Book?Part 1: Data Science Fundamentals, Concepts and Algorithms
Introduction
Statistics
Probability
Bayes' Theorem and Na�ve Bayes Algorithm
Asking the Right Question
Data Acquisition
Data Preparation
Data Exploration
Data Modelling
Data Presentation
Supervised Learning Algorithms
Unsupervised Learning Algorithms
Semi-supervised Learning Algorithms
Reinforcement Learning Algorithms
Overfitting and Underfitting
The Bias-Variance Trade-off
Feature Extraction and Selection
Part 2: Data Science in Practice
Overview of Python Programming Language
Python Data Science Tools
Jupyter Notebook
Numerical Python (Numpy)
Pandas
Scientific Python (Scipy)
Matplotlib
Scikit-Learn
K-Nearest Neighbors
Naive Bayes
Simple and Multiple Linear Regression
Logistic Regression
GLM models
Decision Trees and Random forest
Perceptrons
Backpropagation
Clustering
Natural Language Processing
Frequently Asked Questions
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 science and machine learning using Python and further learning will be required beyond this book to master all aspects.
Q: Can I have a refund if this book doesn't fit 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.***** MONEY BACK GUARANTEE BY AMAZON ***** Editorial Reviews"This is a fantastic book on Python-based data science, data analysis, machine learning, Reinforcement learning and deep learning. As a data scientist with more than 10 years, Peter has had long experience in data science and give in this book the key elements.."
- Lei Xia, Data Scientist Expert at Facebook
168 pages, Paperback
Published August 21, 2018
92 people are currently reading
103 people want to read
About the author
Peter Morgan
99 books31 followersPeter Julian Robin Morgan, CBE is a British film writer and playwright. Morgan is best known for writing the historical films and plays The Queen, Frost/Nixon, The Damned United, and Rush. He is the creator of Netflix's drama series The Crown.
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Displaying 1 - 5 of 5 reviews
November 10, 2018
Excellent Book
A small but great book to learn fundamentals of statistics, to organize data and how to do data analysis using Python with their corresponding libraries.
A small but great book to learn fundamentals of statistics, to organize data and how to do data analysis using Python with their corresponding libraries.
December 9, 2020
This book is a very general overview of Data Science and Artifical Intellgience. It assumes that the read is already familar with python, numpy, pandas, scipy, mathplotlib, and sciki-learn. If the reader is not familar with these programs, then the reader should search for another book. The book is mostly descriptions of the procedures and option in data science. The descriptions are good.
I am not very familar with numpy, pandas, scipy etc. Therefore, I was disappointed with this book.
I am not very familar with numpy, pandas, scipy etc. Therefore, I was disappointed with this book.
November 5, 2018
Intro to data analysis
Nice intro with a Python focus. Places most Python packages in Python ecosystem for data. Would have liked more about Anaconda, but it’s a good guidebook.
Nice intro with a Python focus. Places most Python packages in Python ecosystem for data. Would have liked more about Anaconda, but it’s a good guidebook.
October 11, 2019
A hastily put together book, complete with spelling errors and typos galore
Displaying 1 - 5 of 5 reviews