Python for Data Preprocessing for Beginners: Step-by-Step Guide with Hands-on Projects and Exercises (Feature Engineering and Data Preparation techniques ... Learning & Data Science for Beginners) 21-Mar-2020

by AI Publishing

( 23 )

$9.99 $17.99

Are you looking for a hands-on approach to learn Data Preprocessing techniques fast?

Do you need to start learning Python for Data Preparation from Scratch?

This book is for you.
This book is dedicated to data preparation and explains how to perform different data preparation techniques on a variety of datasets using various data preparation libraries written in the Python programming language. It is suggested that you use this book for data preparation purposes only and not for data science or machine learning.

For the application of data preparation in data science and machine learning, read this book in conjunction with dedicated books on machine learning and data science.

This book explains the process of data preparation using various libraries from scratch. All the codes and datasets have been provided. However, to download data preparation libraries, you will need the internet.
In addition to beginners to data preparation with Python, this book can also be used as a reference manual by intermediate and experienced programmers as it contains data preparation code samples using multiple data visualization libraries.

What this book offers...

The book follows a very simple approach. It is divided into nine chapters. Chapter 1 introduces the basic concept of data preparation, along with the installation steps for the software that we will need to perform data preparation in this book. Chapter 1 also contains a crash course on Python. A brief overview of different data types is given in Chapter 2. Chapter 3 explains how to handle missing values in the data, while the categorical encoding of numeric data is explained in Chapter 4. Data discretization is presented in Chapter 5. Chapter 6 explains the process of handline outliers, while Chapter 7 explains how to scale features in the dataset. Handling of mixed and DateTime data type is explained in Chapter 8, while data balancing and resampling has been explained in Chapter 9. A full data preparation final project is also available at the end of the book.
In each chapter, different types of data preparation techniques have been explained theoretically, followed by practical examples. Each chapter also contains an exercise that students can use to evaluate their understanding of the concepts explained in the chapter.

Clear and Easy to Understand Solutions

All solutions in this book are extensively tested by a group of beta readers. The solutions provided are simplified as much as possible so that they can serve as examples for you to refer to when you are learning a new skill.

Topics Covered:

What Is Data Preparation
Python Crash Course
Different Libraries for Data Preparation
Understanding Data Types
Handling Missing Data
Encoding Categorical Data
Data Discretization
Outlier Handling
Feature Scaling
Handling Mixed and DateTime Variables
Handling Imbalanced Datasets
A Complete Data Preparation Pipeline
Project 1 - Data Preparation
Project 2 - Classification Project
Project 3 - Regression Project

Click the BUY button and download the book now to start learning Data Preprocessing Using Python.

Other Formats:: Paperback

Python Machine Learning Workbook for Beginners: 10 Machine Learning Projects Explained from Scratch (Machine Learning & Data Science for Beginners) 14-Dec-2020

by AI Publishing

( 26 )

$9.99 $25.02

10 Machine Learning Projects Explained from Scratch

Machine Learning (ML) is the lifeblood of businesses worldwide. ML tools empower organizations to identify profitable opportunities fast and help them to understand potential risks better. The ever-expanding data, cost-effective data storage, and competitively priced powerful processing continue to drive the growth of ML.
This is the best time you could enter the exciting machine learning universe. Industries are reinventing themselves constantly by developing more advanced data analysis models. These models analyze larger and more complex data than ever while delivering instantaneous and more accurate results on enormous scales.
In this backdrop, it is evident that hands-on practice is everything in machine learning. Tons of theory will amount to nothing if you don’t have enough hands-on practice. Textbooks and online classes mislead you into a false sense of mastery. The easy availability of learning resources tricks you, and you become overconfident. But when you try to apply the theoretical concepts you learned, you realize it’s not that simple.
This is where projects play a crucial role in your learning journey. Projects are doubtless the best investment of your time. You’ll not only enjoy learning, but you’ll also make quick progress. And unlike studying boring theoretical concepts, you’ll find that working on projects is easier to stay motivated.

The 10 projects in this book cover 10 different interesting topics. Each project will help you refine your ML skills and apply them in the real world. These projects also present you with an opportunity to enrich your portfolio, making it simpler to find a great job, explore interesting career paths, and even negotiate a higher pay package. Overall, this learning by doing book will help you accomplish your machine learning career goals faster.

How Is This Book Different?

This book presents you with a hands-on experience in ML. It is divided into two sections and follows a very simple approach.
The first section consists of two chapters. Chapter 1 provides a roadmap for step by step learning approach to data science and machine learning. The process for environment setup, including the software needed to run scripts in this book, is also explained in this chapter. Chapter 2 contains a crash course on Python for beginners.
The second section consists of 10 compelling machine learning and data science-based projects. In each project, a brief explanation of the theoretical concepts is given, followed by practical examples. The Python notebook for each project is provided in the Source Codes folder in the GitHub and SharePoint repositories.
The datasets used in this book are easily accessible. You can download them at runtime. Alternatively, you can access them via the Datasets folder in the GitHub and SharePoint repositories.

The projects covered include:

House Price Prediction Using Linear Regression
Filtering Spam Email Messages Using Naïve Bayes Algorithm
Predicting Used Car Sale Price Using Feedforward Artificial Neural Networks
Predicting Stock Market Trends with RNN (LSTM)
Language Translation using Seq2Seq Encoder-Decoder LSTM
Classifying Cats and Dogs Images Using Convolutional Neural Networks
Movie Recommender System Using Item-Based Collaborative Filtering
Face Detection with OpenCV in Python
Handwritten English Character Recognition with CNN
Customer Segmentation Based on Income and Spending

Other Formats:: Paperback

Python for Data Analysis: A Complete Beginner Guide for Python basics, Numpy, Pandas, Seaborn, Bokeh and Matplotlib for Data Analysis (Machine Learning & Data Science for Beginners) 7-Sep-2019

by AI Publishing

( 34 )

$9.99 $17.99

Python Coding and Python for Data Analysis for Beginners with Hands-On Projects

Are you looking for a hands-on approach to learn Python coding and Python for Data Analysis fast?

Do you need to start learning Python coding and Data Analysis from Scratch?

This book is for you.
Data analysis is the lifeblood of any business and Python is the perfect language for data analysis.
In simplest terms, data analysis is the inspection, cleansing, evaluation, and transformation of data. One of the widely used languages for data analysis today is Python. Gaining a quick understanding of Python will give you an entry to this evergreen field of study.

How Is This Book Different?

The main focus of this book is on hands-on learning. The best way you can learn Python is by practicing everything you learn in real-time, step by step. And you can certainly go beyond the basics without having to swallow the whole Python official documentation.
This book aims to shorten your learning curve by presenting you with ample hands-on tools. The primary goal is to present you with the concepts, the ideas, the intuitions, and the elementary tools needed to actually start coding and analyzing data in Python.
The main advantage of buying this book is you get easy access to all the materials provided with this book—Python codes, references, exercises, and extra PDF content—on the publisher’s website, at no extra cost. You get to experiment with the practical aspects of Python right from page 1.
This book is ideal if you are new to Python and data science. The topics covered include:

Introduction to Data Analysis
Python for Data Analysis—Basics and Advanced
IPython and Jupyter Notebooks
Numpy for Numerical Data Processing
Pandas for Data Manipulation
Data Visualization

Click the BUY button and download the book now to start learning and coding Python for Data Analysis.

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Data Science Crash Course for Beginners with Python: Fundamentals and Practices with Python (Machine Learning & Data Science for Beginners) 31-Aug-2020

by AI Publishing

( 10 )

$9.99 $17.99

Data Science Crash Course for Beginners with Python

Data Science is here to stay. The tremendous growth in the volume, velocity, and variety of data has a substantial impact on every aspect of a business. While data continues to grow exponentially, accuracy remains a problem. This is where data scientists play a decisive role.
A data scientist analyzes data, discovers new insights, paints a picture, and creates a vision. And a competent data scientist will provide a business with the competitive edge it needs and address pressing business problems.
Data Science Crash Course for Beginners with Python presents you with a hands-on approach to learn data science fast.

How Is This Book Different?

Every book by AI Publishing has been carefully crafted. This book lays equal emphasis on the theoretical sections as well as the practical aspects of data science. Each chapter provides the theoretical background behind the numerous data science techniques, and practical examples explain the working of these techniques. In the Further Reading section of each chapter, you will find the links to informative data science posts.
This book presents you with the tools and packages you need to kick-start data science projects to resolve problems of practical nature. Special emphasis is laid on the main stages of a data science pipeline—data acquisition, data preparation, exploratory data analysis, data modeling and evaluation, and interpretation of the results.
In the Data Science Resources section, links to data science resources, articles, interviews, and data science newsletters are provided. The author has also put together a list of contests and competitions that you can try on your own.
Another added benefit of buying this book is you get instant access to all the learning material presented with this book— PDFs, Python codes, exercises, and references—on the publisher’s website. They will not cost you an extra cent. The datasets used in this book can be downloaded at runtime, or accessed via the Resources/Datasets folder.

The author simplifies your learning by holding your hand through everything. The step by step description of the installation of the software you need for implementing the various data science techniques in this book is guaranteed to make your learning easier. So, right from the beginning, you can experiment with the practical aspects of data science.

You’ll also find the quick course on Python programming in the second and third chapters immensely helpful, especially if you are new to Python. This book gives you access to all the codes and datasets. So, access to a computer with the internet is sufficient to get started.

The topics covered include:

Introduction to Data Science and Decision Making
Python Installation and Libraries for Data Science
Review of Python for Data Science
Data Acquisition
Data Preparation (Preprocessing)
Exploratory Data Analysis
Data Modeling and Evaluation Using Machine Learning
Interpretation and Reporting of Findings
Data Science Projects
Key Insights and Further Avenues

Click the BUY button to start your Data Science journey.

Other Formats:: Paperback

Python Scikit-Learn for Beginners: Scikit-Learn Specialization for Data Scientist (Python for Beginners in Data Science and Data Analysis Book 3) 27-Mar-2021

by AI Publishing

( 12 )

$9.99 $22.52

Python for Data Scientists — Scikit-Learn Specialization

Scikit-Learn, also known as Sklearn, is a free, open-source machine learning (ML) library used for the Python language. In February 2010, this library was first made public. And in less than three years, it became one of the most popular machine learning libraries on Github.
Scikit-learn is the best place to start for access to easy-to-use, top-notch implementations of popular algorithms. This library speeds up the development of ML models.
The main features of the Scikit-learn library are regression, classification, and clustering algorithms (random forests, K-means, gradient boosting, DBSCAN, AND support vector machines). The Scikit-learn library also integrates well with other Python libraries, such as NumPy, Pandas, IPython, SciPy, Sympy, and Matplotlib, to fulfill different tasks.
Python for Data Scientists: Scikit-Learn Specialization presents you with a hands-on, simple approach to learn Scikit-learn fast.

How Is This Book Different?

Most Python books assume you know how to code using Pandas, NumPy, and Matplotlib. But this book does not. The author spends a lot of time teaching you how actually write the simplest codes in Python to achieve machine learning models.
In-depth coverage of the Scikit-learn library starts from the third chapter itself. Jumping straight to Scikit-learn makes it easy for you to follow along. The other advantage is Jupyter Notebook is used to write and explain the code right through this book.
You can access the datasets used in this book easily by downloading them at runtime. You can also access them through the Datasets folder in the SharePoint and GitHub repositories.
You also get to work on three hands-on mini-projects:

Spam Email Detection with Scikit-Learn
IMDB Movies Sentimental Analysis
Image Classification with Scikit-Learn

The scripts, graphs, and images in the book are clear and provide easy-to-understand visuals to the text description. If you’re new to data science, you will find this book a great option for self-study.
Overall, you can count on this learning by doing book to help you accomplish your data science career goals faster.

The topics covered include:

Introduction to Scikit-Learn and Other Machine Learning Libraries
Environment Setup and Python Crash Course
Data Preprocessing with Scikit-Learn
Feature Selection with Python Scikit-Learn Library
Solving Regression Problems in Machine Learning Using Sklearn Library
Solving Classification Problems in Machine Learning Using Sklearn Library
Clustering Data with Scikit-Learn Library
Dimensionality Reduction with PCA and LDA Using Sklearn
Selecting Best Models with Scikit-Learn
Natural Language Processing with Scikit-Learn
Image Classification with Scikit-Learn

Hit the BUY NOW button and start your Data Science Learning journey.

Other Formats:: Paperback

Python NumPy for Beginners: NumPy Specialization for Data Science (Python for Beginners in Data Science and Data Analysis Book 1) 17-Jan-2022

by AI Publishing

( 6 )

$9.99 $25.02

Python NumPy for Beginners

Python Libraries Textbook for Beginners with Codes Folder

Python is doubtless the most versatile programming language.
But are you serious enough about becoming proficient in Python?
If yes, then you need to become a master in the two essential Python libraries—NumPy and Pandas. You simply can’t overlook this truth.
In data science, NumPy and Pandas are by far the most widely used Python libraries. The main features of these libraries are powerful data analysis tools and easy-to-use structures.
Python NumPy for Beginners presents you with a hands-on, simple approach to learning Python fast. This book is refreshingly different, as there’s a lot for you to do than mere reading. Each theoretical concept you cover is followed by practical examples, making it easier to master the concept.
The step-by-step layout of this book simplifies your learning. The author has gone to great lengths to ensure what you learn sticks. You have short exercises at the end of each one of the 11 chapters to test your knowledge of the theoretical concepts you have learned.
This book presents you with:

A strong foundation in NumPy.
A deep understanding of fundamental and intermediate topics.
The essentials of coding in Python.
Links to reference materials related to the topics you study.
Quick access to external files to practice and learn advanced concepts of NumPy.
A Resources folder containing all the datasets used in the book.

The Focus of the Book Is on Learning by Doing

In this learning by doing book, you start with Python installation in the very first chapter. Then there’s a crash course in Python in the second half of the first chapter. In the second chapter, you jump straight to NumPy. Right through the book, you’ll use Jupyter Notebook to write code. You can also get fast access to the datasets used in this book.
The book is loaded with self-explanatory scripts, graphs, and images. They have been meticulously designed to help you understand new concepts easily. Hence, this book is the best choice for self-study, even if you are proficient in Python.
You can tackle new data science problems confidently and develop workable solutions in the real world. Finally, you can rely on this learning by doing book to achieve your Python career goals faster.

This book will help you to quickly master the following topics:

Environment Setup and Python Crash Course
NumPy Basics
NumPy Array Manipulation
NumPy Tips and Tricks
Arithmetic and Linear Algebra Operations with NumPy
Implementing a Deep Neural Network with NumPy
Working with Jupyter Notebook

Hit BUY NOW and start your journey of Python mastery.

Other Formats:: Paperback

Python Machine Learning for Beginners: Learning from Scratch Numpy, Pandas, Matplotlib, Seaborn, SKlearn and TensorFlow 2.0 for Machine Learning & Deep ... Learning & Data Science for Beginners) 24-Oct-2020

by AI Publishing

( 61 )

$9.99 $25.40

Python Machine Learning for Beginners

Machine Learning (ML) and Artificial Intelligence (AI) are here to stay. Yes, that’s right. Based on a significant amount of data and evidence, it’s obvious that ML and AI are here to stay.Consider any industry today. The practical applications of ML are really driving business results. Whether it’s healthcare, e-commerce, government, transportation, social media sites, financial services, manufacturing, oil and gas, marketing and salesYou name it. The list goes on. There’s no doubt that ML is going to play a decisive role in every domain in the future.But what does a Machine Learning professional do?A Machine Learning specialist develops intelligent algorithms that learn from data and also adapt to the data quickly. Then, these high-end algorithms make accurate predictions.

Python Machine Learning for Beginners presents you with a hands-on approach to learn ML fast.

How Is This Book Different?

AI Publishing strongly believes in learning by doing methodology. With this in mind, we have crafted this book with care. You will find that the emphasis on the theoretical aspects of machine learning is equal to the emphasis on the practical aspects of the subject matter.You’ll learn about data analysis and visualization in great detail in the first half of the book. Then, in the second half, you’ll learn about machine learning and statistical models for data science.Each chapter presents you with the theoretical framework behind the different data science and machine learning techniques, and practical examples illustrate the working of these techniques.When you buy this book, your learning journey becomes so much easier. The reason is you get instant access to all the related learning material presented with this book—references, PDFs, Python codes, and exercises—on the publisher’s website. All this material is available to you at no extra cost. You can download the ML datasets used in this book at runtime, or you can access them via the Resources/Datasets folder.You’ll also find the short course on Python programming in the second chapter immensely useful, especially if you are new to Python. Since this book gives you access to all the Python codes and datasets, you only need access to a computer with the internet to get started.

The topics covered include:

Introduction and Environment Setup
Python Crash Course
Python NumPy Library for Data Analysis
Introduction to Pandas Library for Data Analysis
Data Visualization via Matplotlib, Seaborn, and Pandas Libraries
Solving Regression Problems in ML Using Sklearn Library
Solving Classification Problems in ML Using Sklearn Library
Data Clustering with ML Using Sklearn Library
Deep Learning with Python TensorFlow 2.0
Dimensionality Reduction with PCA and LDA Using Sklearn

Click the BUY NOW button to start your Machine Learning journey.

Other Formats:: Paperback

Regression Models With Python For Beginners: Theory and Applications of Linear Models and Logistic Model with python from Scratch 8-Feb-2020

by AI Publishing

( 22 )

$9.99 $22.65

Linear and Logistic Regressions with Python for Beginners with Hands-On Projects

Are you looking for a hands-on approach to learn Regression fast? Or perhaps you have just completed a Data Science or Python course and are looking for data science models?

Do you need to start learning Logistic and Linear Regression from Scratch?

This book is for you.
This book will give you the chance to have a fundamental understanding of regression analysis, which is needed for any data scientist or machine learning engineer.
The book will achieve this by not only having an in-depth theoretical and analytical explanation of all concepts but also including dozens of hands-on, real-life projects that will help you understand the concepts better.

We will start by digging into Python programming as all the projects are developed using it, and it is currently the most used programming language in the world. We will also explore the most-famous libraries for data science such as Pandas, SciPy, Sklearn, and Statsmodel.

Then, we will start seeing how we can preprocess, prepare, and visualize the data, as these steps are crucial for any data science project and can take up to 80 percent of the project time. While we will focus more on the techniques normally used in regression analysis, we will also explain, in-details, all the techniques used in any data science project.

What this book offers...

You will learn all about regression analysis in three modules, one for simple linear regression, one for multiple regression, and a final one for logistic regression. All three modules will contain many hands-on projects using real-world datasets.

Clear and Easy to Understand Solutions

All solutions in this book are extensively tested by a group of beta readers. The solutions provided are simplified as much as possible so that they can serve as examples for you to refer to when you are learning new skills.

What this book aims to do...

This book is written with one goal in mind – to help beginners overcome their initial obstacles to learning data science and Artificial Intelligence.
A lot of times, newbies tend to feel intimidated by Data Science and AI.
The goal of this book is to isolate the different concepts so that beginners can gradually gain competency in the fundamentals of regression before working on a project at the end of the chapter.
Beginners in Data Science does not have to be scary or frustrating when you take one step at a time.

Ready to start practicing and building your Regression Models? Click the BUY button now to download this book

Topics Covered:

What is Regression and When to Use It?
Using Python for Regression Analysis
Data Preparation
Simple Linear Regression
Correlation Analysis
Multiple Linear Regression
Hands-On Project
..and more...

Click the BUY button and download the book now to start learning and practicing Regression with Python.

Other Formats:: Paperback

Python for Data Visualization for Beginners: Visualize Your Data using Pandas, Matplotlib and Seaborn, Step-by-Step Guide with Hands-on Projects and Exercises ... Learning & Data Science for Beginners) 12-Feb-2020

by AI Publishing

( 56 )

$9.99 $17.99

Data Visualization using Python for Beginners

Are you looking for a hands-on approach to learn Python for Data Visualization Fast?

Do you need to start learning Python for Data Visualization from Scratch?

This book is for you. This book works as guide to present fundamental Python Libraries and basis related to Data Visualization using Python.

Data science and data visualization are two different but interrelated concepts. Data science refers to the science of extracting and exploring data in order to find patterns that can be used for decision making at different levels. Data visualization can be considered as a subdomain of data science where you visualize data with the help of graphs and tables in order to find out which data is most significant and can help in the identification of important patterns.

This book is dedicated to data visualization and explains how to perform data visualization on a variety of datasets using various data visualization libraries written in the Python programming language. It is suggested that you use this book for data visualization purposes only and not for decision making. For decision making and pattern identification, read this book in conjunction with a dedicated book on machine learning and data science.

We will start by digging into Python programming as all the projects are developed using it, and it is currently the most used programming language in the world. We will also explore the most-famous libraries for Data Visualization such as Pandas, Numpy, Matplotlib, Seaborn, etc .

What this book offers...

You will learn all about python in three modules, one for Plotting with Matplotlib, one for Plotting with Seaborn, and a final one Pandas for Data Visualization. All three modules will contain hands-on projects using real-world datasets and a lot of exercises.

Clear and Easy to Understand Solutions

All solutions in this book are extensively tested by a group of beta readers. The solutions provided are simplified as much as possible so that they can serve as examples for you to refer to when you are learning a new skill.

What this book aims to do...

This book is written with one goal in mind – to help beginners overcome their initial obstacles to learning Data Visualization using Python.

A lot of times, newbies tend to feel intimidated by coding and data. The goal of this book is to isolate the different concepts so that beginners can gradually gain competency in the fundamentals of Python before working on a project.

Beginners in Python coding and Data Science does not have to be scary or frustrating when you take one step at a time.

Ready to start practicing and visualizing your data using Python? Click the BUY button now to download this book

Topics Covered:

Basic Plotting with Matplotlib
Advanced Plotting with Matplotlib
Introduction to the Python Seaborn Library
Advanced Plotting with Seaborn
Introduction to Pandas Library for Data Analysis
Pandas for Data Visualization
3D Plotting with Matplotlib
Interactive Data Visualization with Bokeh

Interactive Data Visualization with Plotly Hands-on Project Exercises

Click the BUY button and download the book now to start learning and coding Python for Data Visualization.

Other Formats:: Paperback

Python Pandas for Beginners: Pandas Specialization for Data Scientist (Python for Beginners in Data Science and Data Analysis Book 2) 17-Jan-2022

by AI Publishing

( 7 )

$9.99 $25.02

Python NumPy & Pandas for Beginners

Python Libraries Textbook for Beginners with Codes Folder

Python is doubtless the most versatile programming language.
But are you serious enough about becoming proficient in Python?
If yes, then you need to become a master in the two essential Python libraries—NumPy and Pandas. You simply can’t overlook this truth.
In data science, NumPy and Pandas are by far the most widely used Python libraries. The main features of these libraries are powerful data analysis tools and easy-to-use structures.
Python NumPy & Pandas for Beginners presents you with a hands-on, simple approach to learning Python fast. This book is refreshingly different, as there’s a lot for you to do than mere reading. Each theoretical concept you cover is followed by practical examples, making it easier to master the concept.
The step-by-step layout of this book simplifies your learning. The author has gone to great lengths to ensure what you learn sticks. You have short exercises at the end of each one of the 11 chapters to test your knowledge of the theoretical concepts you have learned.
This book presents you with:

A strong foundation in Pandas.
A deep understanding of fundamental and intermediate topics.
The essentials of coding in Python.
Links to reference materials related to the topics you study.
Quick access to external files to practice and learn advanced concepts of Pandas.
A Resources folder containing all the datasets used in the book.

The Focus of the Book Is on Learning by Doing

In this learning by doing book, you start with Python installation in the very first chapter. Then there’s a crash course in Python in the second half of the first chapter. In the second chapter, you jump straight to NumPy. Right through the book, you’ll use Jupyter Notebook to write code. You can also get fast access to the datasets used in this book.
The book is loaded with self-explanatory scripts, graphs, and images. They have been meticulously designed to help you understand new concepts easily. Hence, this book is the best choice for self-study, even if you are proficient in Python.
You can tackle new data science problems confidently and develop workable solutions in the real world. Finally, you can rely on this learning by doing book to achieve your Python career goals faster.

This book will help you to quickly master the following topics:

Environment Setup and Python Crash Course
Pandas Basics
Manipulating Pandas Dataframes
Data Grouping, Aggregation, and Merging with Pandas
Pandas for Data Visualization
Handling Time-Series Data with Pandas
Working with Jupyter Notebook

Hit BUY NOW and start your journey of Python mastery.

Other Formats:: Paperback

Computer Vision for Beginners: Theory and Applications Using Python 23-Aug-2021

by AI Publishing

( 3 )

$9.99 $22.52

Computer Vision Textbook for Beginners with 3 Hands-On Projects
Are you ready to crush your Computer Vision career goals?

The recent advances in the field of computer vision have simply been astounding. In less than a decade, the rate of accuracy for object identification and classification has risen from 50 percent to 99 percent. Today’s systems are, in fact, more accurate than humans at swiftly detecting and responding to visual inputs.
The emergence of deep learning and the advent of very large datasets in recent years have led to an increase in the number of computer vision applications. Against this backdrop, it’s worthwhile to add computer vision knowledge to your data science arsenal. Now is the perfect time to enter this dynamic field.
Computer Vision with Python for Beginners presents you with a hands-on, straightforward approach to learn computer vision fast. The step-by-step format of this book makes learning computer vision simple, fast, and easy. The exercises at the end of each chapter test your knowledge of the concepts you have covered. They also help you apply what you have learned.

This book presents you with:

A solid foundation in computer vision.
Knowledge of elementary and intermediate topics.
Basics of coding in Python.
Links to additional content related to the topics you study.
Access to external files to train and test all the knowledge you have acquired about a computer vision tool.
Three mini-projects in the concluding section of the book that help you to bring together all the theoretical concepts you’ve learned.

You begin with Python installation in the first chapter. Then you have a crash course in Python in the second chapter. Jumping straight to Python quickens your learning and makes it simpler to follow along. Throughout this book, the code is written using Jupyter Notebook. Access to the datasets used in this book is easy.
In the final section, you work on three hands-on mini-projects:

Detecting Hand Symbols for Rock, Paper, Scissors Game
Covid-19 Detection from X-Rays
Detecting Weather from Images

The scripts, images, and graphs are clear. They are designed to help you understand the visuals to the text description easily. This book is the perfect option for self-study, even if your proficiency is at the level of an intermediate learner.
You can tackle new computer vision problems confidently and develop complete solutions at your workplace. Finally, you can count on this learning by doing book to accomplish your computer vision career goals faster.
The topics covered include:

Introduction to Computer Vision
Environment Setup & Writing Your First Program in Python
Python Crash Course
Basics of Image Processing
Basics of Video Processing
Face Detection with OpenCV in Python
Introduction to Machine Learning for Computer Vision
Introduction to Deep Learning for Computer Vision
Transfer Learning for Computer Vision
Object Detection with YOLO
Introduction to GANS

Hit BUY NOW and begin your Computer Vision learning journey.

Other Formats:: Paperback

Statistics: Statistics for Beginners in Data Science: Theory and Applications of Essential Statistics Concepts using Python (Machine Learning & Data Science for Beginners) 18-Apr-2020

by AI Publishing

( 26 )

$9.99 $22.65

Statistics for Beginners in Data Science

Statistical methods are an integral part of data science. Hence, a formal training in statistics is indispensable for data scientists.
If you are keen on getting your foot into the lucrative data science and analysis universe, you need to have a fundamental understanding of statistical analysis. Besides, Python is a versatile programming language you need to master to become a career data scientist.
As a data scientist, you will identify, clean, explore, analyze, and interpret trends or possible patterns in complex data sets. The explosive growth of Big Data means you have to manage enormous amounts of data, clean it, manipulate it, and process it. Only then the most relevant data can be used.
Python is a natural data science tool as it has an assortment of useful libraries, such as Pandas, NumPy, SciPy, Matplotlib, Seaborn, StatsModels, IPython, and several more. And Python’s focus on simplicity makes it relatively easy for you to learn. Importantly, the ease of performing repetitive tasks saves you precious time. Long story short—Python is simply a high-priority data science tool.

How Is This Book Different?

The book focuses equally on the theoretical as well as practical aspects of data science. You will learn how to implement elementary data science tools and algorithms from scratch. The book contains an in-depth theoretical and analytical explanation of all data science concepts and also includes dozens of hands-on, real-life projects that will help you understand the concepts better.
The ready-to-access Python codes at various places right through the book are aimed at shortening your learning curve. The main goal is to present you with the concepts, the insights, the inspiration, and the right tools needed to dive into coding and analyzing data in Python.
The main benefit of purchasing this book is you get quick access to all the extra content provided with this book—Python codes, exercises, references, and PDFs—on the publisher’s website, at no extra price. You get to experiment with the practical aspects of Data Science right from page 1.
Beginners in Python and statistics will find this book extremely informative, practical, and helpful. Even if you aren’t new to Python and data science, you’ll find the hands-on projects in this book immensely helpful. The topics covered include:

Introduction to Statistics
Getting Familiar with Python
Data Exploration and Data Analysis
Pandas, Matplotlib, and Seaborn for Statistical Visualization
Exploring Two or More Variables and Categorical Data
Statistical Tests and ANOVA
Confidence Interval
Regression Analysis
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