If you're looking for a way to become an expert coder and impress your friends with the programs you can make from scratch, then pay attention. Here's the deal. You've decided that one of the most in-demand skills is the best place to start when making money. However, learning how to code can be a very long and arduous process. But, not learning it and hiring a programmer can be very expensive. You may want to build an app or code a website, but the costs have always been too high, making it pointless and not very cost-effective. Sound familiar? If it does, then the information inside this book is your answer. You will be given all the tips, tricks, and practice codes you need to learn Python, the solid programming language used in hundreds of industries around the world. This information allows you to become skilled much faster and perfect your coding skills in no time. Imagine cutting months off your learning curve and getting a strong base of knowledge in no time at all. Imagine getting your project done yourself for a fraction of the cost. This all is possible with the help of this three-books bundle, featuring beginner, intermediate, and expert guides! This guidebook goes more in-depth about the Python language. This is detailed, scientific information compiled together by experts in an easy-to-listen-to fashion. In this Python guide, you will discover: Book one: The benefits of Python How to get up and running with Python Full instructions of how to code How to make predictions with algorithms Real-world examples of Python The three different examples of coding Book two: The importance of machine learning The basics of working with Python How to set up your Python environment Data preprocessing with machine learning Working with linear regression in machine learning Book three: The best benefits of Python and why programmers around the world choose it How to download the Python language on your computer, regardless of the operating system you prefer How to write your first program in Python What is means to work with an object-oriented programming language How to write conditional statements, loops, functions, variables, classes, exceptions, and more If you want to learn more about how to get the best Python training, and if you are ready to write your own codes and turn your ideas into reality, then simply click the "Buy Now" button on this page to get started.

Dive deep into the core concepts of Python KEY FEATURES ● The concepts in this book are illustrated through numerous short code snippets and more than 650 programming examples. ● The book contains a comprehensive collection of over 900 end-of-chapter exercises, including both MCQs and programming exercises. The solutions to all the exercises are also available. ● The book includes coding conventions and best practices for writing efficient, readable, and maintainable code. DESCRIPTION This book provides a comprehensive and thorough introduction to Python, a popular programming language used by various top companies across various domains. Whether you are a novice starting your programming journey or an experienced programmer looking to expand your skill set, this book is designed to assist you in mastering core Python concepts. Starting with the basics, this book guides you through the setup, basic commands, and key language rules. The book covers important ideas like different types of data, variables, and how to control the flow of your programs. You will also learn about collections for organizing data, functions for reusable code, modules for organizing bigger projects, and object-oriented programming for modeling real-world things. Advanced topics include customizing object behavior, efficient data processing, modifying function behavior, and handling errors gracefully. The book includes many figures and coding examples to give you a visual and hands-on experience. There are numerous exercises that provide opportunities to further reinforce your knowledge. By the end of this book, readers will develop a strong foundation in core Python and will gain the confidence to excel in their studies and professional work. WHAT YOU WILL LEARN ● Develop programs using procedural, object-oriented, and functional paradigms. ● Understand complex topics like iterators, generators, and decorators. ● Learn how to create and use modules and packages. ● Master the advanced concepts of object-oriented programming. ● Learn how to handle errors in Python and interact with files. ● Automate resource management patterns using context managers. WHO THIS BOOK IS FOR This book can be used by anyone who wants to learn Python from scratch. It can be a valuable resource for engineering students and students from other streams who have Python as part of their curriculum. This book facilitates a swift introduction to the language for individuals aiming to transition into data science, AI, or ML. TABLE OF CONTENTS 1. Introduction to Python 2. Getting Started 3. Strings 4. Lists and Tuples 5. Dictionaries and Sets 6. Conditional Execution 7. Loops 8. Looping Techniques 9. Comprehensions 10. Functions 11. Modules and Packages 12. Namespaces and Scope 13. Files 14. Object Oriented Programming 15. Magic Methods 16. Inheritance and Polymorphism 17. Iterators and Generators 18. Decorators 19. Lambda Expressions and Functional Programming 20. Exception Handling 21. Context Managers Solutions

DESCRIPTION In today's data-driven world, the ability to extract meaningful insights from vast datasets is crucial for success in various fields. This ultimate book for mastering open-source libraries of data science in Python equips you with the essential tools and techniques to navigate the ever-evolving field of data analysis and visualization. Discover how to use Python libraries like NumPy, Pandas, and Matplotlib for data manipulation, analysis, and visualization. This book also covers scientific computing with SciPy and integrates ChatGPT to boost your data science workflow. Designed for data scientists, analysts, and beginners, it offers a practical, hands-on approach to mastering data science fundamentals. With real-world applications and exercises, you will turn raw data into actionable insights, gaining a competitive edge. This book covers everything you need, including open-source libraries, Visual Explorer tools, and ChatGPT, making it a one-stop resource for Python-based data science. Readers will gain confidence after going through this book and we assure you that all the minute details have been taken into consideration while delivering the content. After reading, learning, and practicing from this book, we are sure that all IT professionals, novices, or job seekers will be able to work on data science projects thus proving their mettle. KEY FEATURES ● Master key Python libraries like NumPy, Pandas, and Seaborn for effective data analysis and visualization. ● Understand complex data science concepts through simple explanations and practical examples. ● Get hands-on experience with 300+ solved examples to solidify your Python data science skills. WHAT YOU WILL LEARN ● Learn to work with popular IDEs like VS Code and Jupyter Notebook for efficient Python development. ● Master open-source libraries such as NumPy, SciPy, Matplotlib, and Pandas through advanced, real-world examples. ● Utilize automated EDA tools like PyGWalker and AutoViz to simplify complex data analysis. ● Create sophisticated visualizations like heatmaps, FacetGrid, and box plots using Matplotlib and Seaborn. ● Efficiently handle missing data, outliers, and perform filtering, sorting, grouping, and aggregation using Pandas and Polars. WHO THIS BOOK IS FOR This book is ideal for diploma, undergraduate, and postgraduate students from engineering and science fields to programming and software professionals. It is also perfect for data science, ML, and AI engineers looking to expand their expertise in cutting-edge technologies. TABLE OF CONTENTS 1. Environmental Setup for Using Data Science Libraries in Python 2. Exploring Numpy Library for Data Science in Python 3. Exploring Array Manipulations in Numpy 4. Exploring Scipy Library for Data Science in Python 5. Line Plot exploration with Matplotlib Library 6. Charting Data With Various Visuals Using Matplotlib 7. Exploring Pandas Series for Data Science in Python 8. Exploring Pandas Dataframe for Data Science in Python 9. Advanced Dataframe Filtering Techniques 10. Exploring Polars Library for Data Science in Python 11. Exploring Expressions in Polars 12. Exploring Seaborn Library for Data Science in Python 13. Crafting Seaborn Plots: KDE, Line, Violin and Facets 14. Integrating Data Science Libraries with ChatGPT Prompts 15. Exploring Automated EDA Libraries for Machine Learning 16. Case Study Using Python Data Science Libraries

Master Neural Networks for Building Modern AI Systems. KEY FEATURES ● Comprehensive Coverage of Foundational AI Concepts and Theories. ● In-Depth Exploration of Maths Behind Neural Network Mathematics. ● Effective Strategies for Structuring Deep Learning Code. ● Real-World Applications of AI Principles and Techniques. DESCRIPTION This book is a practical guide to the world of Artificial Intelligence (AI), unraveling the math and principles behind applications like Google Maps and Amazon. The book starts with an introduction to Python and AI, demystifies complex AI math, teaches you to implement AI concepts, and explores high-level AI libraries. Throughout the chapters, readers are engaged with the book through practice exercises, and supplementary learnings. The book then gradually moves to Neural Networks with Python before diving into constructing ANN models and real-world AI applications. It accommodates various learning styles, letting readers focus on hands-on implementation or mathematical understanding. This book isn't just about using AI tools; it's a compass in the world of AI resources, empowering readers to modify and create tools for complex AI systems. It ensures a journey of exploration, experimentation, and proficiency in AI, equipping readers with the skills needed to excel in the AI industry. WHAT WILL YOU LEARN ● Leverage TensorFlow and Keras while building the foundation for creating AI pipelines. ● Explore advanced AI concepts, including dimensionality reduction, unsupervised learning, and optimization techniques. ● Master the intricacies of neural network construction from the ground up. ● Dive deeper into neural network development, covering derivatives, backpropagation, and optimization strategies. ● Harness the power of high-level AI libraries to develop production-ready code, allowing you to accelerate the development of AI applications. ● Stay up-to-date with the latest breakthroughs and advancements in the dynamic field of artificial intelligence. WHO IS THIS BOOK FOR? This book serves as an ideal guide for software engineers eager to explore AI, offering a detailed exploration and practical application of AI concepts using Python. AI researchers will find this book enlightening, providing clear insights into the mathematical concepts underlying AI algorithms and aiding in writing production-level code. This book is designed to enhance your skills and knowledge to create sophisticated, AI-powered solutions and advance in the multifaceted field of AI. TABLE OF CONTENTS 1. Understanding AI History 2. Setting up Python Workflow for AI Development 3. Python Libraries for Data Scientists 4. Foundational Concepts for Effective Neural Network Training 5. Dimensionality Reduction, Unsupervised Learning and Optimizations 6. Building Deep Neural Networks from Scratch 7. Derivatives, Backpropagation, and Optimizers 8. Understanding Convolution and CNN Architectures 9. Understanding the Basics of TensorFlow and Keras 10. Building End-to-end Image Segmentation Pipeline 11. Latest Advancements in AI Index

Python has various database drivers for PostgreSQL. Currently, the psycopg is the most popular PostgreSQL database adapter for the Python language. The psycopg fully implements the Python DB-API 2.0 specification. The current version of the psycopg is 2 or psycopg2. The psycopg2 database adapter implemented in C as a libpq wrapper resulting in both fast and secure. The psycopg2 provides many useful features such as client-side and server-side cursors, asynchronous notification and communication, COPY command support, etc. PostgreSQL was designed to run on UNIX-like platforms. However, PostgreSQL was then also designed to be portable so that it could run on various platforms such as Mac OS X, Solaris, and Windows. PostgreSQL is free and open source software. Its source code is available under PostgreSQL license, a liberal open source license. You are free to use, modify and distribute PostgreSQL in any form. PostgreSQL requires very minimum maintained efforts because of its stability. Therefore, if you develop applications based on PostgreSQL, the total cost of ownership is low in comparison with other database management systems. In Chapter 2, you will learn querying data from the postgresql using Python including establishing a database connection, creating a statement object, executing the query, processing the resultset object, querying data using a statement that returns multiple rows, querying data using a statement that has parameters, inserting data into a table using Python, updating data in postgresql database using Python, calling postgresql stored function using Python, deleting data from a postgresql table using Python, and postgresql Python transaction. In Chapter 3, you will learn managing table structure and views including postgresql data types, postgresql create table, postgresql select into statement, postgresql create table as, using postgresql serial to create auto-increment column, identity column, alter table, drop table, truncate table, check constraint, not-null constraint, foreign key, primary key, unique constraint, managing postgresql views, creating updatable views, materialized views, creating updatable views using the with check option clause, and recursive view. In Chapter 4, you will learn statements, operators, and clauses including select, order by, select distinct, limit, fetch, in, between, postgresql like, is null, alias, joins, inner join, postgresql left join, self-join, full outer join, cross join, natural join, group by, having, intersect operator, except operator, grouping sets, cube, and rollup. In Chapter 5, you will learn postgresql trigger, aggregate, and string functions including creating the first trigger in postgresql, managing postgresql trigger, aggregate functions, avg function, max function, min function, sum function, postgresql concat function, ascii function, trim function, length function, substring function, regexp_matches function, regexp_replace function, replace function, to_number function, and to_char function.

Test your Data Analysis skills to its fullest using Python and other no-code tools KEY FEATURES ● Comprehensive coverage of Python libraries such as Pandas, NumPy, Matplotlib, Seaborn, Julius AI for data acquisition, preparation, analysis, and visualization ● Real-world projects and practical applications for hands-on learning ● In-depth exploration of low-code and no-code tools for enhanced productivity DESCRIPTION Ultimate Data Analysis and Visualization with Python is your comprehensive guide to mastering the intricacies of data analysis and visualization using Python. This book serves as your roadmap to unlocking the full potential of Python for extracting insights from data using Pandas, NumPy, Matplotlib, Seaborn, and Julius AI. Starting with the fundamentals of data acquisition, you'll learn essential techniques for gathering and preparing data for analysis. From there, you’ll dive into exploratory data analysis, uncovering patterns and relationships hidden within your datasets. Through step-by-step tutorials, you'll gain proficiency in statistical analysis, time series forecasting, and signal processing, equipping you with the tools to extract actionable insights from any dataset. What sets this book apart is its emphasis on real-world applications. With a series of hands-on projects, you’ll apply your newfound skills to analyze diverse datasets spanning industries such as finance, healthcare, e-commerce, and more. By the end of the book, you'll have the confidence and expertise to tackle any data analysis challenge with Python. To aid your journey, the book includes a handy Python cheat sheet in the appendix, serving as a quick reference guide for common functions and syntax. WHAT WILL YOU LEARN ● Acquire data from various sources using Python, including web scraping, APIs, and databases. ● Clean and prepare datasets for analysis, handling missing values, outliers, and inconsistencies. ● Conduct exploratory data analysis to uncover patterns, trends, and relationships within your data. ● Perform statistical analysis using Python libraries such as NumPy and Pandas, including hypothesis testing and regression analysis. ● Master time series analysis techniques for forecasting future trends and making data-driven decisions. ● Apply signal processing methods to analyze and interpret signals in data, such as audio, image, and sensor data. ● Engage in real-world projects across diverse industries, from finance to healthcare, to reinforce your skills and experience. ● Utilize Python for in-depth analysis of real-world datasets, gaining practical experience and insights. ● Refer to the Python cheat sheet in the appendix for quick access to common functions and syntax, aiding your learning and development. WHO IS THIS BOOK FOR? This book is ideal for beginners, professionals, or students aiming to enhance their careers through hands-on experience in data acquisition, preparation, analysis, time series, and signal processing. Prerequisite knowledge includes basic Python and introductory statistics. Whether starting fresh or seeking to refresh skills, this comprehensive guide helps readers upskill effectively. TABLE OF CONTENTS 1. Introduction to Data Analysis and Data Visualization using Python 2. Data Acquisition 3. Data Cleaning and Preparation 4. Exploratory Data Analysis 5. Statistical Analysis 6. Time Series Analysis and Forecasting 7. Signal Processing 8. Analyzing Real-World Data Sets using Python APPENDIX A Python Cheat Sheet Index

TAGLINE Creating Next Gen Apps in Finance KEY FEATURES ● Master the Python libraries and packages essential for financial applications, enabling robust development. ● Utilize Python for developing applications that process financial information, visualize data in diverse formats, and create insightful representations. ● Derive analytical insights from mathematical models integrated into Python applications for data-driven decision-making in finance and fintech. DESCRIPTION Dive into the dynamic world where finance meets fintech with Python's versatile capabilities in this 'Ultimate Python for Fintech Solutions'. Whether you're aiming to build secure trading platforms, conduct deep statistical analysis, or pioneer next-generation financial technologies, this book quips you with the knowledge, tools, and practical insights to succeed. This book starts with Python's foundational programming techniques, essential for understanding financial principles and laying the groundwork for robust applications. You will learn to build scalable solutions that handle complex financial data with ease by using Python for analysis, forecasting, and data visualization. Next, it moves to explore advanced topics like AI/ML applications tailored for finance, enabling you to unlock predictive insights and streamline decision-making processes. You will discover how Python integrates cutting-edge technologies such as Big Data and Blockchain, to offer innovative solutions for modern fintech challenges. By the end of this expansive book, you will gain the expertise needed to develop sophisticated financial applications, visualize data effectively across desktop and web platforms, and drive innovation in fintech. WHAT WILL YOU LEARN ● Learn to build robust applications tailored for financial analysis, modeling, and fintech solutions using Python. ● Learn to analyze large volumes of financial data, and visualize insights effectively. ● Apply advanced AI/ML techniques to predict trends, optimize financial strategies, and automate decision-making processes. ● Integrate Python with Big Data platforms and Blockchain technologies to work with massive datasets and decentralized financial systems. ● Acquire the knowledge and skills to innovate in the fintech space to address modern financial challenges and opportunities. WHO IS THIS BOOK FOR? This book is for working professionals, students, business managers, consultants, technical/functional analysts, anyone wishing to improve their skills in Fintech with Python. This book will be a great start for a programmer who wants to start on the Python tech stack and make a career in Fintech space. The prerequisites for the reader will be basic mathematics and advanced math topics such as time series, derivatives, and integrals. The outcome for the reader will be to understand mathematical modeling and to have capability to develop next gen financial apps. TABLE OF CONTENTS 1. Getting Started on Python Infrastructure and Building Financial Apps 2. Learning Financial Concepts Using Python 3. Data Structures and Algorithms Using Python 4. Object Oriented Programming Using Python 5. Building Simulation and Mathematical Analysis Tools Using Python 6. Stochastic Mathematics and Building Models Using Python 7. Prediction Algorithms Using Python 8. Data Science and Statistical Algorithms Using Python 9. Desktop and Web Charting Using Python 10. AI/ML Apps Using Python 11. Big Data/Blockchain-Based Solutions Using Python 12. Next Generation FinTech Apps Using Python with Financial Singularity Index