******Free eBook for customers who purchase the print book from Amazon****** 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," Peters 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. To get the most out of the concepts that would be covered, 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 Target Users The book is designed for a variety of target audiences. The most suitable users would include:
Beginners who want to approach data science, but are too afraid of complex math to start
Newbies in computer science techniques and data science
Professionals in data science and social sciences
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
Correctness
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. We will also be happy to help you if you send us an email at contact@aisciences.net.
