What do you think?
Rate this book
Data Science from Scratch with Python: Step-by-Step Beginner Guide for Statistics, Machine Learning, Deep learning and NLP using Python, Numpy, Pandas, Scipy, Matplotlib, Sciki-Learn, TensorFlow
***** BUY NOW (will soon return to 15.77 $) *****
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 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
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?
Introduction Statistics Probability Bayes’ Theorem and Naïve 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
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.
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 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
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 Naïve 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.
169 pages, Kindle Edition
Published August 20, 2018
93 people are currently reading
103 people want to read
About the author
Peter Morgan
100 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.
Ratings & Reviews
Friends & Following
Create a free account to discover what your friends think of this book!
Community Reviews
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