**GET YOUR COPY NOW, the price will be 21.99$ soon**Learn Python coding for Data Analysis from scratch very easilyWelcome to the Python Crash Course for Data Analysis!The book offers you a solid introduction to the world of Python Coding for data analysis. In this book, you'll learn fundamentals that will enable you to go further in Python Coding, launch or advance a career, and join the next generation of Data Analyst talent that will help define a beneficial, new, powered future for our world. You will study important libraries such as NumPy, Pandas and some Data Visualization libraries.Educational Objectives: This introductory book teaches the foundational skills all Python programmers use to analyze data. It is ideal for beginners who want to learn Python coding or Python for Data Analysis, make informed choices about career goals, and set themselves up for success in this path. At the end of this learning, you will become an great Python Programmer for data Analysis, and learn to analyse data using frameworks like NumPy, Pandas and Matplotlib. Prerequisites: No prior experience with programming is required. You will need to be comfortable with basic computer skills, such as managing files, running programs, and using a web browser to navigate the Internet.You will need to be self-driven and genuinely interested in the Python Coding. No matter how well structured the program is, any attempt to learn programming will involve many hours of studying, practice, and experimentation. Success in this book requires devoting at least 10 hours to your work. This requires some tenacity, and it is especially difficult to do if you don't find Python coding interesting or aren't willing to play around and tinker with your code-so drive, curiosity, and an adventurous attitude are highly recommended!You will need to be able to learn English.Contact Info: While going through the book, if you have questions about anything, you can reach us at [email protected].**GET YOUR COPY NOW, the price will be 15.99$ soon**

Are you looking for a super-fast computer programming course? Would you like to learn the Python Programming Language in 7 days? Do you want to increase your business thanks to the web applications? If so, keep reading: this bundle book is for you! Finally on launch the most complete Python guide with 3 Manuscripts in 1 book: 1-Python for beginners 2-Python for Data Science 4-Python Crash Course Python will introduce you many selected practices for coding . You will discover as a beginner the world of data science, machine learning and artificial intelligence. The following list is just a tiny fraction of what you will learn in this collection bundle. 1) Python for beginners ✓ The basics of Python programming ✓ Differences among programming languages ✓ Vba, SQL, R, Python ✓ Game creation with Pyhton ✓ Easy-to-follow steps for reading and writing codes. ✓ Control flow statements and Error handling ✓ 4 best strategies with NumPy, Pandas, Matplotlib 2) Python for Data science ◆ 4 reason why Python is fundamental for Data Science ◆ Python design patterns ◆ How to use Python Data Analysis in your business ◆ Data visualization optimal tools and techniques ◆ Analysis of popular Python projects templates ◆ How to set up the Python environment for Data Science ◆ Most important Machine Learning Algorithms ◆ How to leverage Data Science in the Cloud 3) Python Crash Course * A Proven Method to Write your First Program in 7 Days * 5 Common Mistakes to Avoid when You Start Coding * A Simple Strategy to Write Clean, Understandable and Flexible Codes * The One Thing You Need to Debug your Codes in Python * 5 Practical exercises to start programming Even if you have never written a programming code before, you will quickly grasp the basics thanks to visual charts and guidelines for coding. Examples and step-by-step guides will guide you during the code-writing learning process. The description of each topic is crystal-clear and you can easily practice with related exercises. You will also learn all the best tricks of writing codes with point by point descriptions of the code elements. If you really wish to to learn Python and master its language, please click the BUY NOW button.

For many researchers, Python is a first-class tool mainly because of its libraries for storing, manipulating, and gaining insight from data. Several resources exist for individual pieces of this data science stack, but only with the Python Data Science Handbook do you get them all—IPython, NumPy, Pandas, Matplotlib, Scikit-Learn, and other related tools. Working scientists and data crunchers familiar with reading and writing Python code will find this comprehensive desk reference ideal for tackling day-to-day issues: manipulating, transforming, and cleaning data; visualizing different types of data; and using data to build statistical or machine learning models. Quite simply, this is the must-have reference for scientific computing in Python. With this handbook, you’ll learn how to use: IPython and Jupyter: provide computational environments for data scientists using Python NumPy: includes the ndarray for efficient storage and manipulation of dense data arrays in Python Pandas: features the DataFrame for efficient storage and manipulation of labeled/columnar data in Python Matplotlib: includes capabilities for a flexible range of data visualizations in Python Scikit-Learn: for efficient and clean Python implementations of the most important and established machine learning algorithms

Python Machine Learning for BeginnersMachine 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.

The Hands-On, Example-Rich Introduction to Pandas Data Analysis in Python Today, analysts must manage data characterized by extraordinary variety, velocity, and volume. Using the open source Pandas library, you can use Python to rapidly automate and perform virtually any data analysis task, no matter how large or complex. Pandas can help you ensure the veracity of your data, visualize it for effective decision-making, and reliably reproduce analyses across multiple datasets. Pandas for Everyone brings together practical knowledge and insight for solving real problems with Pandas, even if you’re new to Python data analysis. Daniel Y. Chen introduces key concepts through simple but practical examples, incrementally building on them to solve more difficult, real-world problems. Chen gives you a jumpstart on using Pandas with a realistic dataset and covers combining datasets, handling missing data, and structuring datasets for easier analysis and visualization. He demonstrates powerful data cleaning techniques, from basic string manipulation to applying functions simultaneously across dataframes. Once your data is ready, Chen guides you through fitting models for prediction, clustering, inference, and exploration. He provides tips on performance and scalability, and introduces you to the wider Python data analysis ecosystem. Work with DataFrames and Series, and import or export data Create plots with matplotlib, seaborn, and pandas Combine datasets and handle missing data Reshape, tidy, and clean datasets so they’re easier to work with Convert data types and manipulate text strings Apply functions to scale data manipulations Aggregate, transform, and filter large datasets with groupby Leverage Pandas’ advanced date and time capabilities Fit linear models using statsmodels and scikit-learn libraries Use generalized linear modeling to fit models with different response variables Compare multiple models to select the “best” Regularize to overcome overfitting and improve performance Use clustering in unsupervised machine learning

Ready to discover the Machine Learning world? Machine learning paves the path into the future and it’s powered by Python. All industries can benefit from machine learning and artificial intelligence whether we’re talking about private businesses, healthcare, infrastructure, banking, or social media. What exactly does it do for us and what does a machine learning specialist do? Machine learning professionals create and implement special algorithms that can learn from existing data to make an accurate prediction on new never before seen data. Python Machine Learning presents you a step-by-step guide on how to create machine learning models that lead to valuable results. The book focuses on machine learning theory as much as practical examples. You will learn how to analyse data, use visualization methods, implement regression and classification models, and how to harness the power of neural networks. By purchasing this book, your machine learning journey becomes a lot easier. While a minimal level of Python programming is recommended, the algorithms and techniques are explained in such a way that you don’t need to be intimidated by mathematics. The Topics Covered Include: Machine learning fundamentals How to set up the development environment How to use Python libraries and modules like Scikit-learn, TensorFlow, Matplotlib, and NumPy How to explore data How to solve regression and classification problems Decision trees k-means clustering Feed-forward and recurrent neural networks Get your copy now

Get complete instructions for manipulating, processing, cleaning, and crunching datasets in Python. Updated for Python 3.6, the second edition of this hands-on guide is packed with practical case studies that show you how to solve a broad set of data analysis problems effectively. You’ll learn the latest versions of pandas, NumPy, IPython, and Jupyter in the process. Written by Wes McKinney, the creator of the Python pandas project, this book is a practical, modern introduction to data science tools in Python. It’s ideal for analysts new to Python and for Python programmers new to data science and scientific computing. Data files and related material are available on GitHub. Use the IPython shell and Jupyter notebook for exploratory computing Learn basic and advanced features in NumPy (Numerical Python) Get started with data analysis tools in the pandas library Use flexible tools to load, clean, transform, merge, and reshape data Create informative visualizations with matplotlib Apply the pandas groupby facility to slice, dice, and summarize datasets Analyze and manipulate regular and irregular time series data Learn how to solve real-world data analysis problems with thorough, detailed examples

Get started with Python for data analysis and numerical computing in the Jupyter notebook About This Book Learn the basics of Python in the Jupyter Notebook Analyze and visualize data with pandas, NumPy, matplotlib, and seaborn Perform highly-efficient numerical computations with Numba, Cython, and ipyparallel Who This Book Is For This book targets students, teachers, researchers, engineers, analysts, journalists, hobbyists, and all data enthusiasts who are interested in analyzing and visualizing real-world datasets. If you are new to programming and data analysis, this book is exactly for you. If you're already familiar with another language or analysis software, you will also appreciate this introduction to the Python data analysis platform. Finally, there are more technical topics for advanced readers. No prior experience is required; this book contains everything you need to know. What You Will Learn Install Anaconda and code in Python in the Jupyter Notebook Load and explore datasets interactively Perform complex data manipulations effectively with pandas Create engaging data visualizations with matplotlib and seaborn Simulate mathematical models with NumPy Visualize and process images interactively in the Jupyter Notebook with scikit-image Accelerate your code with Numba, Cython, and IPython.parallel Extend the Notebook interface with HTML, JavaScript, and D3 In Detail Python is a user-friendly and powerful programming language. IPython offers a convenient interface to the language and its analysis libraries, while the Jupyter Notebook is a rich environment well-adapted to data science and visualization. Together, these open source tools are widely used by beginners and experts around the world, and in a huge variety of fields and endeavors. This book is a beginner-friendly guide to the Python data analysis platform. After an introduction to the Python language, IPython, and the Jupyter Notebook, you will learn how to analyze and visualize data on real-world examples, how to create graphical user interfaces for image processing in the Notebook, and how to perform fast numerical computations for scientific simulations with NumPy, Numba, Cython, and ipyparallel. By the end of this book, you will be able to perform in-depth analyses of all sorts of data. Style and approach This is a hands-on beginner-friendly guide to analyze and visualize data on real-world examples with Python and the Jupyter Notebook.

Data science libraries, frameworks, modules, and toolkits are great for doing data science, but they’re also a good way to dive into the discipline without actually understanding data science. In this book, you’ll learn how many of the most fundamental data science tools and algorithms work by implementing them from scratch. If you have an aptitude for mathematics and some programming skills, author Joel Grus will help you get comfortable with the math and statistics at the core of data science, and with hacking skills you need to get started as a data scientist. Today’s messy glut of data holds answers to questions no one’s even thought to ask. This book provides you with the know-how to dig those answers out. Get a crash course in Python Learn the basics of linear algebra, statistics, and probability—and understand how and when they're used in data science Collect, explore, clean, munge, and manipulate data Dive into the fundamentals of machine learning Implement models such as k-nearest Neighbors, Naive Bayes, linear and logistic regression, decision trees, neural networks, and clustering Explore recommender systems, natural language processing, network analysis, MapReduce, and databases

Learn UML, the Unified Modeling Language, to create diagrams describing the various aspects and uses of your application before you start coding, to ensure that you have everything covered. Millions of programmers in all languages have found UML to be an invaluable asset to their craft. More than 50,000 previous readers have learned UML with Sams Teach Yourself UML in 24 Hours. Expert author Joe Schmuller takes you through 24 step-by-step lessons designed to ensure your understanding of UML diagrams and syntax. This updated edition includes the new features of UML 2.0 designed to make UML an even better modeling tool for modern object-oriented and component-based programming. The CD-ROM includes an electronic version of the book, and Poseidon for UML, Community Edition 2.2, a popular UML modeling tool you can use with the lessons in this book to create UML diagrams immediately.