Trending February 2024 # Top Books On Python For Beginners And Advanced (2023) # Suggested March 2024 # Top 4 Popular

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Best Python Books to Learn Business Finance

Python is a wide-spread programming language very commonly used for coding nowadays. It is an object-oriented and functional programming language. Learning python benefits students and developers in the fields of data science, machine learning, and software development. This list of python books will guide students and professionals to build a strong base in python. Even kids can start learning from them at an early age. These python books have explanations and examples for helping their readers understand the topics better.

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Let us now review python books in detail.

Book #1 Mastering Python Networking

Author: Eric Chou

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Review: Key Points:

This book comprises the methods to unlock the potential of Python libraries to address tough network problems.

It is the best choice to learn the method of leveraging Python for SDN, DevOps, and network automation.

This book is most useful for a Programmer or a Network Engineer who wants to learn Python for networking.

Book #2 Python Data Science Handbook

Author: Jake VanderPlas

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Review:

This book is an excellent choice if you want to refer to data science and data analytics tasks. The book uses Jupyter, which is very easy to read. Every chapter contains illustrations with well-designed examples. The author seems to have the gift of providing explanations with clarity.

Key Points:

This book is a great source of various pieces of the data science stack, such as Scikit-Learn, Matplotlib, Pandas, NumPy, and IPython.

This book comes in handy for data crunchers and working scientists who work in Python code.

The book is a ‘must-have’ for readers who are doing scientific computing in Python.

Book #3 Python Tricks

Author: Dan Bader

Review:

This book strikes a perfect balance between real-world solutions and in-depth explanations. The content is lucid and thorough but in an informal manner. The author drives into sufficient details while not over-explaining concepts to the extent that they become slow and frustrating.

Key Points:

After each section, there is a brief recap that explains the rules of thumb that need following. This serves to remove any uncertainty.

If you have experience working in other programming languages or you have worked with legacy versions of Python, this book is most suitable for you to come up to speed with modern features and patterns.

This book reveals the best practices in Python and the potential of the Pythonic code with a step-by-step narrative.

Book #4 Python for Kids

Author: Jason R. Briggs

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Review:

Children who use this book find programming accessible and enjoyable, and they begin to generate ideas for making games. This book is well-written, consists of excellent topics, and renders a plethora of examples. There is a very good balance between support (help and explanations) and challenge (concepts and coding tasks).

Key Points:

This book is introductory to Python programming and comprises illustrations and kid-friendly examples.

Kids can build a game and create drawings with Turtle, Python’s graphics library after reading this book.

This book is a ticket for children aged 10 and more to the amazing sphere of computer programming.

Book #5 A Smarter Way to Learn Python

Author: Mark Myers

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Review:

This is a great guide that makes Python easy to learn due to digestible chapters, which make you confident to work on projects. After each chapter, there are online quizzes to test readers. Proves exist that this is a great way for a totally-new coder to embark on the Python journey.

Key Points:

This book uses ‘interactive recall practice’ as the key teaching method.

Washington University states that this method augments learning performance by 400 percent.

This book has approximately 1,000 interactive exercises, which are online and free.

Book #6 Python Cookbook

Author: David Beazley

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Review:

This book is one of the best resources to learn to write lean code in Python 3. Learners of this book know how to avoid writing unnecessary long code and falling into traps. It is a fast-paced Python book.

Key Points:

This book is full of practical examples written in Python 3.3, which is suitable for experienced Python programmers who are searching for content that has modern tools.

The recipes in this book cover an extensive range in scope and difficulty, which begins from simple string concatenation to the creation of BNF recursive parsers.

This book is the optimum choice if you want to update Python 2 code or need assistance to create programs in Python 3.

Book #7 Python for Data Analysis

Author: William McKinney

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Review:

McKinney has clear experience and vision for the pandas framework. He has nicely explained the main function and inner workings of NumPy and pandas. This is a very practical book that has a plethora of examples, which can be best leveraged by being on the keyboard while reading this book.

Key Points:

This book is a modern introduction that deals with scientific computing in Python, which enables readers to know data-intensive applications.

It consists of complete instructions to manipulate, process, clean, and crunch datasets in Python.

Python programmers who are novices in scientific computing and analysts new to Python can use this book.

Book #8 Python Crash Course

Author: Eric Matthes

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Review:

The structure of this book is such that the difficulty level increases gradually as you proceed forward. Each chapter in the book has exercises at the end, which enable cementing the content. Real Python has chosen this book as one of the best for those who want to learn Python.

Key Points:

This book introduces Python programming in a thorough, quick-paced manner with the result that you can write programs, solve problems, and make things work in no time.

This book is ideal for those who want to learn fundamental programming concepts, such as loops, classes, dictionaries, and lists.

The exercises will ensure that you can write clean and readable code and make programs interactive.

Book #9 Hands-on Machine Learning with Scikit-Learn and TensorFlow

Author: Aurelien Geron

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Review:

This book teaches readers not only various tools but also the framework that you can apply to a specific issue and the method of thinking about what you want to do in each project. The coding exercises cement your learning and restrict readers from outpacing themselves.

Key Points:

This book uses two production-ready Python frameworks (TensorFlow and Scikit-Learn) to develop an intuitive understanding of the tools and concepts essential to building intelligent systems.

You can learn a gamut of techniques, beginning from simple linear regression and terminating deep neural networks.

Even if you know nothing about machine learning, this book helps you to leverage simple and efficient tools for program implementation that are capable of learning from data.

Book #10 Python Programming

Author: John Zelle

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Review:

This book explains tough concepts at a good speed with pertinent examples. It fulfills two aims: introducing readers to computer science and then introducing them to Python as the first programming language.

Key Points:

The design of this book is such that one can select it as a primary textbook in the first course of computing at the college level.

This book teaches the core skills of computer science traditionally with an emphasis on programming, design, and problem resolution.

The most crucial modification in this edition is that a majority of the uses of eval were removed and a discussion of its dangers added.

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Top 10 Books On C ++ For Beginners And Advanced (List)

Best Books to Learn C++

Here is a list of ten popular C++ books that you may find helpful for learning and improving your skills in C++ programming.

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Let us go through each book’s review and highlights in detail.

Book #1 The C++ Programming Language

Author: Bjarne Stroustrup

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 Review: Key Points:

The author mentions how C++ is a powerful and flexible programming language with rich features and capabilities.

It is essential to understand the underlying concepts and principles of C++ rather than just learning its syntax.

This book mentions that a good programming style and design are necessary for writing effective C++ code.

Book #2 Effective C++: 55 Specific Ways to Improve Your Programs and Designs

Author: Scott Meyers

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Review:

This book is organized around 55 specific guidelines for improving C++ programs and designs and covers a wide range of topics, including object-oriented programming, templates, and performance optimization. The book’s 55 specific items address a particular aspect of C++ programming. Some topics include choosing the correct data type, using inheritance effectively, and avoiding common programming pitfalls.

Key Points:

The book explains that writing efficient and effective C++ code requires a strong understanding of the performance implications of different programming techniques.

It says maintainability is an important consideration when writing C++ code.

According to the author, design patterns can be valuable for organizing and structuring code.

Book #3 Accelerated C++: Practical Programming by Example

Author: Andrew Koenig and Barbara E. Moo

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Review: Key Points:

Practice and hands-on learning are essential for becoming proficient in C++ programming.

It is important to understand the underlying principles of C++ rather than just learning its syntax.

The authors provide some exercises and examples to help readers develop their skills in C++ programming.

Book #4 C++ Primer

Author: Stanley B. Lippman, Josée Lajoie, and Barbara E. Moo

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Review: Key Points:

The book includes the basics of C++ syntax and programming, including data types, variables, operators, loops, and functions.

It also explains the fundamentals of object-oriented programming, including classes, inheritance, and polymorphism.

It uses templates to create generic and reusable codes.

This book introduces the STL, including containers, iterators, and algorithms.

Book #5 C++ Concurrency in Action

Author: Anthony Williams

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Review: Key Points:

It includes techniques for writing lock-free code using atomic operations and other low-level mechanisms.

The book has strategies for managing concurrency in C++, including thread pools and task-based programming.

Book #6 C++ Templates

Author: David Vandevoorde and Nicolai M. Josuttis

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Review: Key Points:

The book has the basics of template syntax and programming.

These include function templates, class templates, and template specialization.

The author has given the best practices for using templates in real-world applications, including tips for debugging and optimizing template code.

Book #7 C++17 – The Complete Guide

Author: Nicolai M. Josuttis

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Review:

“C++17 – The Complete Guide” is a comprehensive guide to the C++ programming language, explicitly targeting the C++17 standard. The book is suitable for experienced C++ programmers to learn about the new features introduced in C++17. Also, for those who are new to C++ and want to know the language from the ground up.

Key Points:

The author provides an in-depth understanding of the C++ programming language, including its syntax, semantics, and features.

The book also has practical examples and code samples to help readers understand how to use C++ effectively and efficiently.

There are tips and best practices for writing clean, maintainable, and efficient C++ codes.

Book #8 C++ for Dummies

Author: Stephen R. Davis

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Review:

The book has a beginner-level introduction to the C++ programming language. The book is suitable for those interested in programming and who want to learn C++ as their first programming language.

Key Points:

It introduces the basics of programming, including concepts such as variables, loops, and functions.

It is also a reference guide to C++ syntax and features for easy reference as readers progress through the book.

Book #9 C++17 in Detail

Author: Bartlomiej Filipek

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Review:

It is a comprehensive guide to the C++17 programming language, an updated version of the C++ language that includes many new features and improvements. The book covers the major components of C++17, including generalized lambda expressions, nested namespaces, improved return type deduction, and context of statements. It also covers the new parallel algorithms in the Standard Template Library (STL) and the new filesystem library for working with files and directories.

Key Points:

The book introduces many new features that improve the expressiveness and performance of the language.

The new filesystem library provides a convenient and portable way to work with files and directories in C++.

The book provides detailed explanations and examples of each new feature, making it a valuable resource for C++ programmers looking to learn and use C++17 effectively.

Book #10 C++ Coding Standards

Author: Herb Sutter and Andrei Alexandrescu

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Review:

This guide will guide you to writing high-quality, maintainable C++ code. The book covers various topics, including naming conventions, design principles, testing, debugging, and performance optimization. It also includes guidance on using the C++ Standard Template Library (STL) and other shared C++ libraries.

Key Points:

It has high-quality C++ code that requires attention to detail and a systematic approach to design and implementation.

The authors emphasize the importance of testing and debugging and provide practical tips and techniques for these tasks.

The book includes extensive coverage of the STL and other standard C++ libraries, making it a valuable resource for anyone looking to use these tools in their code effectively.

Top 10 Business Finance Books (Updated 2023)

Best Books to Learn Business Finance

Business finance books provide strategies for finding funding and making money. These books intend to provide capital for projects, boost earnings, and aid in the long-term financial performance of organizations.

The list of business finance books aids readers at all levels of expertise in their understanding of Business Finance. Beginners can use this as a starting point, and professionals with more expertise can use it as a reference guide. These deal with funding, debt management, profit, return on investment, trade, and valuation.

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Let us discuss each business finance book in detail, outlining their reviews and key points.

Book #1: Valuation: Measuring and Managing the Value of Companies

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Review:

The book explains how to appropriately assess a company while considering variables like taxes, inflation, running and non-operating expenditures, and retirement. It thoroughly explains how to determine a company’s value and get the best returns on investment. It also describes situations and exceptions, like emerging markets and fast-growing businesses.

Key Points:

How to create value, predict and assess performance, and strategic investment & management

A description of the valuation complexities, tactics, and procedures

Information on both fundamental and cutting-edge management practices.

Book #2: The Alchemy of Finance

Author: George Soros

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Review:

The Alchemy of Finance is the written wisdom of renowned investor George Soros. The book offers a framework for financial decision-making and explains Soros’s trading and investment methods. While later chapters predict future trends, earlier chapters examine economics via a historical lens. Although the book’s economic experiment occurred in the 1980s, the lessons learned, and market insights still apply today.

Key Points:

The book discusses economics and reflexivity, the human uncertainty principle, and power relationships.

The multi-billionaire investor offers a new perspective on economic behavior by outlining how the actions of financial actors impact market activity.

It contains unique investment techniques, including the “theory of reflexivity,” which forms their basis.

Book #3: The Personal MBA: Master the Art of Business

Author: Josh Kaufman

Buy this book here.

Review:

This book is one of the best books on business finance for beginners. The main point made by the author is that becoming a business expert can be achieved without going to business school. It discusses a variety of MBA programs and has a sizable section on finance that provides a thorough overview of the best ways to make money. It also explains market analysis.

Before launching a new business, one should conduct research, especially if one intends to invest all of their assets. The book serves as a crash course in company operations and is a guide to making businesses profitable and effective.

Key Points:

It explains the Iron Law of the Market, which discusses why firms work according to the size and caliber of the market it services

The book also teaches complementary subjects such as valuation, sales, and improving systems.

It also states how and why adding value for your clients is necessary through the 12 Standard Forms of Value.

It explains cash flow, income statements, purchasing power, lifetime value, pricing, and funding.

Book #4: Profit First: Transform Your Business from a Cash-Eating Monster to a Money-Making Machine

Author: Mike Michalowicz

Buy this book here.

Review: Key Points:

It lays out the realistic actions business owners can take to improve their bank balances.

These summaries suggest a new strategy that ensures ongoing profitability

It guides how to expand businesses and start making money by creating a routine for handling finances.

Book #5: How Finance Works: The HBR Guide to Thinking Smart About the Numbers

Buy this book here.

Review:

According to Desai, to achieve professionally, a person must make sensible financial decisions and develop the confidence to defend those decisions to others. The book serves as a primer for the field of financial systems. The manual examines how wise leaders make financial decisions and touches on capital allocation. It simplifies complex ideas into brief explanations for beginners’ easy understanding. However, experienced experts can benefit from it.

Key Points:

The book covers essential concepts such as valuation, financial analysis, capital markets, etc.

The book demystifies finance while guiding readers through its intricate but inexhaustibly fascinating realm.

It covers concepts that teach the skills to comprehend finance functions through exciting case studies, interactive exercises, full-color images, etc.

Book #6: The Intelligent Investor

Author: Benjamin Graham

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Review: Key Points:

The book guides through various stock market policies like portfolio, dividend, EPS, security analysis, etc.

It uses case studies to explore the historical background of the stock market.

It lays out formulas for reducing risk and achieving better returns.

Book #7: Accounting for the Numberphobic: A Survival Guide for Small Business Owners

Author: Dawn Fotopulos

Buy this book here.

Review:

Accounting for the Numberphobic is primarily for business owners with no accounting knowledge. This manual teaches business owners to overcome their fear of doing the math and interpreting the data.

Key Points:

It illustrates using financial statements, ledgers, and profit and loss statements.

Increasing your profits is based on your Net Income Statement

Determine when your company is self-sustaining and how to measure and improve cash flow

It also explores the connection between the balance sheet and the value of a business.

Book #8: How to Read a Financial Report: Wringing Vital Signs Out of the Numbers

Author: Tage C. Tracy and John A. Tracy

Buy this book here.

Review

The book focuses on the information that financial accounts and reports provide about a company. It clarifies sometimes confusing areas of financial reporting and discusses how organizations can “financially design” operating performance. Ultimately, it provides examples and exhibits to help readers understand financial statements and their use.

Key Points:

It provides thorough, detailed instructions for assessing financial information and reading and understanding financial reports.

The book illustrates every aspect of financial reports like income, taxes, cashflows, and others.

It contains several examples and explanations of various financial reporting and analytical tools.

Book #9:The Little Book of Common Sense Investing: The Only Way to Guarantee Your Fair Share of Stock Market Returns

Author: John C. Bogle

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Review: Key Points:

This guide explores the nuances of index funds and shows readers how to build a strong and stable portfolio that accumulates wealth.

It shows how to create a broadly diversified, inexpensive portfolio without taking risks for picking particular stocks, managers, or switching sectors.

The book tells of setting realistic expectations for stock returns. It is essential to recognize that stock returns come from three factors: dividend yield, income growth, and changes in market valuation.

Book #10: Warren Buffett and the Interpretation of Financial Statements: The Search for the Company with a Durable Competitive Advantage

Author: Mary Buffett and David Clark

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Review:

The book thoroughly examines the writer’s investment philosophies and tactics. It highlights Buffett’s knowledge of maximum business debt, the impact of R&D on profits, etc. It also explains the profitable use of financial statements to make good investment decisions.

Key Points:

The guide offers a step-by-step breakdown of examining investment statements and deciding on a company’s worth.

It explores Buffett’s tried-and-true dos and don’ts by reading an income statement and balance sheet.

The book additionally discloses how much debt a company can hold before it becomes too risky to touch.

It also includes Buffett’s profitability statements and calculations to recognize the business with a resilient, competitive edge.

Top 10 Python Gui Frameworks For Developers In 2023

Top 10 Python GUI frameworks for developers in 2023 for building interactive GUIs

Due to the diverse range of options in Python GUI frameworks, it might be confusing to many beginners or intermediate developers. But is not difficult to start with them. Python is listed as the most preferred language as per the analysis of StackOverflow and the majority of developers are ready to explore the development options in python.

Python applications are visually appealing, open source, and easy to use. To adapt yourself to this learning curve, for a better user experience and intuition with the created applications, you must know about the top 10 Python GUI frameworks. Let’s take a deep dive into the curated list of GUI frameworks for developers.

PyGUI

With the help of pyGUI, developers can create applications that have unique features of python by inserting less code between the created applications and the underlying facilities of this pythonic GUI Platform. You can also use this GUI project on Windows machines, Unix-based systems, and the latest versions of macOS devices.

Kivy

Python by inserting less code between the created applications and the underlying facilities of this pythonic GUI Platform. You can also use this GUI project on Windows machines, Unix-based systems, and the latest versions of macOS devices. Kivy also supports NUI (natural user interface) meaning, a user can naturally learn about many of the interactions provided by this open-source Python GUI Framework.

PySimpleGUI

Tkinter

The world of Tkinter welcomes many Python developers to equip its Tk GUI Toolkit. The toolkit distributes a library of fundamental GUI Widgets with which one can build a simple and open-source Graphical User Interface. Some of the GUI Widgets acquired with various levels of customizability are

a) Buttons (can be used to accept user input)

b) Checkbuttons (can be used to put together selections such as monochrome images, colours, and border size)

c) File dialogues (upload & download files from/to the app)

d) Labels (to display textual information)

e) Canvas (provides the developers with space to draw and paint plots, graphs and flexibility to the structure of your application)

       5. Wax 

Wax has made GUI development easier and more scalable for Python developers by successfully removing low-level GUI aspects. With this benefit, developers do not need to interact with Windows, Mac OS, or Linux on a low-abstraction level, resulting in ready-to-use visual effects with fewer lines of code. It is also more developer-friendly than the other Python GUI Frameworks wrapped in native graphical widgets.

LibAvg

LibAvg is a high-performance GUI Framework for Python developers. This high-level development platform is written in C Plus + and developed by Alfred Bigler. It can be used for smoothly developing User Interfaces for modern touch-based devices. Regardless, this framework has already been used in several commercial projects, including GlobalData, SimMed, and C-Base Scanner, and it works well on Windows, Linux, and Mac OS X.

PyQt5

PyQt5 is yet another easy-to-use cross-platform GUI framework for Python developers. You can significantly create applications that support Mac OS X, Linux, Windows, Android, and Raspberry Pi in a shorter period. PyQt5 allows users to customise themes and create unified aesthetic graphical designs by arranging multiple GUI widgets such as sliders, list-boxes, buttons, and so on within the window.

PyGame

PyGame, in addition to being a useful GUI framework, has recently evolved into a primer for game programming in Python. The most general reason is developers can create VGs using its sound libraries and visually appealing graphics which are housed within its collection of Python modules. Considering whether or not our future generations will find this framework useful!!

wxPython

PyQt5 is yet another easy-to-use cross-platform GUI framework for Python developers. You can significantly create applications that support Mac OS X, Linux, Windows, Android, and Raspberry Pi in a shorter period. PyQt5 allows users to customise themes and create unified aesthetic graphical designs by arranging multiple GUI widgets such as sliders, List-Boxes, Buttons, and so on within the window.

Pyside2

Ai Glossary: 25+ Terms For Beginners (2023 Edition)

Artificial Intelligence (AI) is developing exponentially fast. It is going to revolutionize most industries by offering a level of automation and precision no human could ever achieve. This means lots of new opportunities and applications we’ve never even thought of.

🎨 For instance, did you know an AI art generator won an art competition full of talented artists?

Even though not everyone needs to worry or care about AI, it’s worthwhile to learn some basic concepts and words associated with this field of computer science.

This is a beginner-friendly glossary of popular AI terms and concepts. Most terms on this list are basic AI jargon you might hear in news, at dinner tables, or at work. Make sure to keep up with the conversations by reading these terms!

Make sure to also read my complete guide to Artificial Intelligence!

Anyway, let’s jump into the list of AI words!

1. AI

Artificial Intelligence or AI mimics the human intelligence process by using computer programs.

The idea of an AI program is that it learns similarly to humans. For example, an AI can drive a car, recognize images, detect images, and much more.

AI has become one of the 21st-century buzzwords. Notice that the term Artificial Intelligence is nothing but maths and probabilities under the hood.

You commonly hear people saying AI doesn’t use code. But this is far from the truth. All AI programs are written by data scientists and software developers using coding languages, such as Python. But the programs are crafted in a way that they are capable of making decisions on their own. So the actions taken by AI software are not hard-coded into the system.

2. API

API stands for an application programming interface via which developers can access data and pre-made code solutions.

In the artificial intelligence space, many companies and startups use APIs via which they can access third-party AI solutions. This can make building impressive AI applications super easy.

The concept of APIs explains why all of a sudden startups and individual developers seem to have some kind of AI superpowers.

This is why APIs are awesome. Instead of spending millions and millions in research, anyone can start an AI business with little to no cost.

For example, most of the impressive AI writing tools use OpenAI’s GPT-3 language under the hood via an API. When doing this, a company is just an intermediary between a customer and the AI provided by a third party.

These days, anyone with little technical skills can build impressive AI software over a weekend using the right type of API.

3. Big Data

Big data refers to large data sets that are too huge to be processed in traditional data processing manner. Big data is a mix of structured, semi-structured, and unstructured data that organizations collect. Typically, businesses use big data to mine information from data to make better business decisions.

One example of using big data is machine learning and artificial intelligence. Artificial intelligence solutions rely on data. To train a powerful AI model, you need to run it through lots of data.

Big data is typically characterized by three V’s:

Volume. Big data consists of large volumes of data in multiple environments.

Variety. Big data systems store a broad variety of data.

Velocity. The velocity describes the rate at which data is generated, collected, and processed.

4. Chatbot

Chatbots or AI chatbots are a popular application of artificial intelligence. Many businesses use AI chatbots to streamline customer support services.

One common example of chatbots is when visiting a web page and a chat box opens up. If you type something, you’ll get an immediate response. This response is not written by a human, but by an AI chatbot.

An AI chatbot uses natural language processing techniques, such as sentiment analysis to extract information about the messages it receives. Then it uses a natural language processing model to produce an output that it predicts will best serve the recipient.

Chatbots are a central application of AI. They are getting better and better quickly as AI develops. These days it’s sometimes impossible to tell whether you are talking to a bot or to a human.

5. Classification

In machine learning, classification means a modeling problem where the machine learning model predicts a class label for some input data.

A great example of classification is spam filtering. Given an input message, the task of the machine learning model is to classify whether the message is spam or not.

Another example of classification is recognizing handwritten images. Given an image of a character, the machine learning model tries to classify the letter it sees.

To build a successful classifier, you need a big dataset with lots of examples of inputs and outputs via which the model can learn to predict the outcomes.

For example, to build a handwritten character recognizer, the model needs to see a vast array of examples of handwritten characters.

6. Composite AI

Composite AI refers to a combination of AI techniques for accomplishing the best results.

Remember, AI is a broad term that governs subfields like machine learning, natural language processing, deep learning, neural networks, and more.

Sometimes the solutions aren’t achievable by using a single technique. This is why sometimes the AI solutions are built using a composite structure where multiple subfields of AI are combined to get the results.

7. Computer Vision

Computer vision is one of the main subfields of artificial intelligence. A computer vision program uses image data to train a computer to “see” the visual world.

To make computer vision work, the program has to analyze and learn from digital images and videos using deep learning models. Based on the data, a computer vision program can classify objects and make decisions.

You might be surprised that the earliest experiments with computer vision AI were made back in the 1950s. During this time, the first neural networks were used to determine the edges of objects in images to categorize them into circles and squares. During the 1970s, a computer interpreted handwritten text with optical character recognition.

These four factors have made computer vision thrive:

Mobile devices with built-in cameras.

Increase in computational power.

Computer-vision-focused hardware.

New computer vision algorithms like CNN networks.

8. DALL-E 2

DALL-E 2 is an impressive text-to-image algorithm that has gained massive attention in the recent past.

To put it short, DALL-E 2 works such that it takes a text input and outputs an image that describes the input. At the time of writing, DALL-E 2 isn’t publicly available. Instead, it has a waiting list with tens of thousands of accepted participants.

On a very high level, DALL-E 2 is just a function that maps text to images. But under the hood, it’s a lot more than that.

Unlike previous attempts in generating images from text, DALL-E 2 takes it to the next level. I recommend checking some of the pictures it has been able to produce. For instance, here is a Tweet from one of the creators of DALL-E 2:

DALL-E 2 uses artificial intelligence to convert text to images. To produce images, DALL-E 2 uses two techniques:

Natural language processing for analyzing the intent of the input text.

Computer vision creation for outputting an image that best matches the intent.

9. Data Mining

Data mining refers to finding patterns in data to predict outcomes. In other words, it’s the process of turning raw data into useful and actionable information.

Data mining involves lots of techniques businesses can use to:

Cut costs

Reduce risks

Increase profit

Enhance customer relationships

And more.

Data mining is also called “knowledge discovery in databases”, which is a lengthier yet more descriptive naming for the action. It’s a process of figuring out hidden connections in data and predicting the future with them.

The data mining process roughly follows these five steps:

Define business objectives. This is the phase of identifying the business problem and asking a lot of questions. During this phase, analysts sometimes have to conduct some research to better understand the business context.

Data preparation. When the problem is defined, data scientists determine the data set that helps answer the questions of the problem. During this phase, the data is cleaned up so that for example duplicates, missing values, and outliers are removed.

Data analysis. Data scientists try to look for relationships in the data, such as correlations, association rules, or other types of patterns. This is the phase that drives toward drawing conclusions and making decisions based on the data.

Result evaluation. After aggregating the data, it’s time to evaluate and interpret the results. During this phase, the results are finalized in an understandable and actionable format. With the results, companies can come up with new strategies and solutions to achieve their objectives.

10. Data Science

Data science is a broad study of vast volumes of data. A data scientist uses modern tools, techniques, and algorithms to:

Find unseen patterns

Discover insightful information

Make better business decisions

Data scientists use machine learning algorithms to create predictive models to extract information from the data.

The data being analyzed can come from different sources and have a variety of different formats. The data can be structured or unstructured.

Sometimes the data is already found in a data storage or database.

Usually, the data also needs to be obtained from somewhere, such as by scraping the web.

Here are some key concepts related to data science:

Machine Learning

Modeling

Statistics

Coding

Databases

Before learning data science, you should be familiar with these concepts and know how to use them.

11. Deep Learning

A computer doesn’t understand what is learning as we humans do. To make a computer learn, it has to mimic the human-intelligence process of learning. This is where deep learning is used.

Deep learning is a machine learning subfield. Deep learning algorithms are used to teach computers how to learn by example.

A famous deep learning application is self-driving cars.

The deep learning algorithms that run under the hood can are taught to recognize objects on the road in real-time. These objects include:

Road signs

Pedestrians

Traffic lights

Traffic lanes

Other vehicles

Also, the voice control services are a result of clever deep-learning algorithms.

To make a deep learning algorithm work, you have to feed it a lot of labeled training data. The algorithm uses the data to train itself to recognize patterns.

For instance, with enough training data, you can teach a deep learning algorithm to recognize objects from images.

12. Ethical AI

Ethical AI refers to AI that refers to ethical guidelines related to fundamental values, like:

Individual rights

Privacy

Non-discrimination

Non-manipulation

The idea of ethical AI is to place importance on ethical aspects in specifying what is legitimate/illegitimate use of AI. The organizations that use ethical AI have stated policies and review processes to make sure the AI follows the ethical guidelines.

Ethical AI does not only consider things permissible by law but also thinks one step further. Another way to put it is if something is legal, it doesn’t mean it’s ethical AI.

For instance, an AI algorithm that manipulates people in self-destructive behavior that is legal doesn’t represent ethical AI.

13. Hybrid AI

Hybrid AI is a combination of human insight and AI, such as machine learning and deep learning.

This type of AI is still in its early development phase and has a bunch of challenges. But experts still believe in it.

Another description of Hybrid AI is that it’s a combination of symbolic AI and non-symbolic AI.

A web search is a perfect example of Hybrid AI. Let’s say a user types “1 EUR to USD” into a search engine:

The search engine identifies a currency-conversion problem in the search phrase. This is the symbolic AI part of the search engine.

The search engine then runs machine learning algorithms to rank and present the search results. This is the non-symbolic part of the search engine.

14. Image Recognition

Image Recognition is a subfield of Computer Vision and AI.

Image Recognition represents methods used to detect and analyze images to automate tasks.

The state-of-the-art image recognition algorithms are capable of identifying people, places, objects, and other similar types of elements within an image or drawing. Besides, the algorithms are able to draw actionable conclusions from the detected objects.

15. Linear Algebra

Linear algebra is the key branch in mathematics when it comes to artificial intelligence and machine learning algorithms.

Linear algebra deals with linear equations, vector spaces, and matrices. Another way to put it is that linear algebra studies linear functions and vectors.

Although you traditionally use linear algebra to model natural phenomena, it’s the key component of making machine learning algorithms work.

16. Machine Learning

Machine Learning is one of the most well-known subfields of AI.

Machine learning is a field of study that focuses on using data and algorithms to simulate human learning. Machine learning programs rely on big data to learn patterns and relationships in them.

Behind the scenes, machine learning is nothing but a lot of basic linear algebra.

The simplest form of machine learning algorithm takes data and fits a curve to it to predict future values.

The process of the machine learning algorithm can be broken into three key parts:

Decision process. The algorithm makes a prediction or classification. Using input data the algorithm produces an estimate of patterns in the data.

Error function. The error function considers the “goodness” of the prediction made by the model. If you have known examples, the error function can compare the prediction to these examples to assess the accuracy.

Model optimization process. Depending on the outcome of the prediction made by the algorithm, you might need to adjust it. This is to reduce the error produced by the algorithm when comparing a prediction and a real example.

17. MidJourney

Image credit: MidJourney

MidJourney is a new AI art generator that turns text into an image. And not just any image, but a realistic, creative, or abstract masterpiece unlike we’ve never seen before!

MidJourney has become viral in the recent past thanks to its amazing AI art creations.

To use MidJourney, give it a text input from your deepest imaginations and it will turn it into an image.

Notice that at the time of writing the tool is still in beta mode and accessible through invitation.

MidJourney represents the new wave of AI text-to-image software. MidJourney has already won a real art contest!

But MidJourney is definitely not the only powerful AI art tool. Other similar solutions, such as DALL-E and Stable Diffusion are also making their way to the top of the game.

The best part about these AI art tools is that they are evolving so rapidly. You can expect them to only get better over time.

Make sure to check the best AI art generators—you’ll be impressed!

18. Model

Model is a commonly used term in machine learning—the key subfield of AI.

A model or machine learning model is a file that recognizes specific types of patterns from data. To create a machine learning model, you have to write an algorithm and train the model with it and big data.

The machine learning model learns from data and you can use the model to predict outcomes of future values.

For instance, you could make a machine learning model that predicts the highest temperature of the day given the temperature in the morning. You could train the algorithm with a linear regression model and a bunch of weather data points from the past.

19. Natural Language Processing

Natural Language Processing (NLP) is one of the key subfields of AI. With NLP, a computer program can process text and spoken language in a similar fashion to us humans.

NLP makes it possible for humans to interact with computers using natural language.

Natural Language Processing combines:

Computational linguistics

Statistical modeling

Machine learning models

Deep learning models

When used in a clever way, a combination of these studies give rise to computer programs that are capable of understanding the full meaning of the text or spoken language. This means the programs can sense the intention and sentiment too!

NLP is useful in translations, responding to voice commands, text summarization, and more.

20. Neural Network

A neural network is a subfield of deep learning, which is one of the key subfields of AI.

A neural network mimics the brain’s pattern recognition process. It’s a simulation of how biological neurons communicate in the human brain.

A neural network is formed by:

An input layer

One (or more) hidden layer

An output layer

The layers consist of nodes (artificial neurons). The previous layer’s nodes are connected to the nodes in the next layer. Besides, each node carries a weight and a threshold.

If the output of a node exceeds the threshold, the node is activated and it sends data to the next layer.

This is similar to how the neural activity in our brain.

When building a deep learning algorithm, a developer runs the training data through a neural network. The algorithm then learns to classify the training data based on neural network activations.

21. OpenAI

OpenAI is an AI research lab and company.

OpenAI is one of the key players in the field. The company has developed a bunch of AI-powered programs and algorithms unlike seen before.

The two best-known examples of OpenAI’s AI algorithms are:

DALL-E. This AI algorithm allows for generating outstanding images based on text input.

GPT-3. This cutting-edge natural language processing model allows for creating human-like text based on a short input. It can write news, poems, short stories, or even books.

The best part of these AI models is that they are available to the general public.

Anyone can access GPT-3 and develop a super powerful text-generation app with a few lines of code. This happens through the GPT-3 API that OpenAI provides.

At the time of writing, DALL-E is not yet in general availability, but it is accessible via a waiting list and an invitation.

22. Optical Character Recognition

Optical character recognition or OCR (text recognition) extracts textual data from images, scanned documents, and PDFs.

The OCR algorithms work by:

Identifying all the letters in an image.

Putting the letters to words.

Putting the words into sentences.

OCR can use AI to create even more impressive methods for extracting text from images. Sometimes the AI-powered OCR is called ICR for Intelligent Character Recognition.

ICR can identify languages from writing or extract information in different styles of handwriting.

A typical OCR software helps save time and cut costs when it comes to turning physical documents into digital format. Instead of doing the work manually, you can take a picture and use OCR to scan its contents.

Make sure to read my article about the best OCR software.

23. Prompt Engineering

Prompt engineering is a relatively new term in the field of AI.

Prompt engineering refers to writing carefully thought text inputs for AI algorithms to generate the desired outcome.

The term prompt engineering is relevant when using:

AI text-to-image generators

AI text generators

For example, an AI text-to-image generator might produce rather unimpressive results if the text input is not descriptive and specific enough.

To use AI to generate impressive images, videos, or text, you need to be careful when giving input.

For instance, you might need to drop a name of an artist, era, painting style, and such. You also might need to insert some technical terminology in the prompt to give the imagery a specific type of look.

Prompt engineering might be the career of the future.

24. Singularity

In the field of AI, singularity refers to the event where the AI becomes self-aware and starts to evolve on its own out of control.

But don’t worry just yet. The modern-day AI isn’t very smart compared to the human brain.

Even though singularity is not a reasonable present concern, it’s definitely something we have to be careful about in the future. The rapid developments in computing and technology might make AI dangerous or destructive.

The developments of AI must focus on making AI our friend, not our enemy.

25. Speech Recognition

Speech recognition is a popular application of artificial intelligence.

The idea of speech recognition or automatic speech recognition (ASR) is for a computer program to pick up on the spoken words and turn them into text.

Many speech recognition services use AI algorithms to process spoken language. They utilize the composition of audio and voice signals to process speech.

There are many speech recognition algorithms, including natural language processing techniques and neural networks.

The ideal AI-based speech recognition system learns as it goes. This makes the tools more accurate over time.

26. Strong AI

Strong AI is a theoretical level of artificial intelligence where the AI is as intelligent as humans and is self-aware. Moreover, a strong AI system would be able to solve problems, learn new skills, and plan the future—like we humans do.

Strong AI is commonly known as artificial general intelligence or AGI for short.

It’s up for debate if we’ll ever reach this level of AI. Some optimistic researchers claim this type of AI is not further than a couple of decades away from us. Others say it will never be achieved. Only time will tell.

27. Turing Test

The turning test is a test that determines whether a machine has human-like intelligence.

If a machine can have a conversation with a human without being detected as a machine, the machine has passed the Turing test—or shown human intelligence.

The Turing test was proposed by Alan Turing back in 1950.

Even with the impressive and rapid developments in the field of AI, no machine has ever passed a Turing test. But it’s getting closer.

The main motivation and theories of AI all evolve around the concept of passing a Turing test. This is why you commonly hear people talking about the Turing test or passing it.

28. Weak AI

Weak AI is an approach to AI research and development in which AI is considered to only be capable of simulating the human intelligence process.

Weak AI systems aren’t actually conscious.

Weak AI is bound by the rules developed for it and cannot autonomously go beyond those rules.

A great example of weak AI is a chatbot. It appears to be conscious and answers intelligently. But it cannot go beyond that.

As a matter of fact, all modern-day AI solutions are examples of weak AI.

Weak AI is also commonly called narrow artificial intelligence.

Conclusion

So there you have it—a whole bunch of AI jargon you might hear every day!

To put it short, AI or artificial intelligence is a rapidly developing subfield of computer science. AI is already used in impressive applications and it can carry out tasks unlike ever before. Only time will tell what the future holds for AI and us living with it.

Thanks for reading!

Read Also

An Introduction On Etl Tools For Beginners

This article was published as a part of the Data Science Blogathon.

Introduction on ETL Tools

The amount of data being used or stored in today’s world is extremely huge. Many companies, organizations, and industries store the data and use it as per the requirement. While handling this huge amount of data, one has to follow certain steps. Whenever we start working with data, specific words/terms come to our minds. For example, data warehouses, databases, attributes, ETL, data filtering, etc. In this article, we are going to have a brief introduction to one such term named ETL.

What is ETL?

ETL stands for extract, transform, load. Let’s see these terms one by one.

Extract:

It means extracting data from its source which can be an application or another database. Extraction can be divided further into two types:

a) Partial extraction

b) Full extraction

Transform:

It means transforming the raw data which has been extracted from sources. Transforming includes filtering the data, cleaning the data, mapping and transforming data, etc. This step may include some simple changes to source data or some multiprocessing which includes multiple data sources.

Load:

It means converting transformed data into the target database. The target databases can be DataMart, Data Warehouses, or databases. These destination sources are used for analytical purposes, planning business strategies, etc.

In short, the ETL tool performing the above three steps ensures that the data is complete, usable and as per the requirement for further processes like analysis, reporting, and machine learning/artificial intelligence.

Where to use ETL? Machine Learning and Artificial Intelligence

Machine Learning and Artificial Intelligence include a lot of data. The cloud is the only feasible solution to store this huge amount of data. Besides, both of these techniques require large datastores for analytical model building and training. Cloud-based ETL tools are useful here to both migrate large amounts of data to the cloud and transform them to be analytics-ready.

Data Warehousing

Many of the enterprisers use ETL tools to collect data from various sources, then transform it into a consistent format and load it into a data warehouse. Then business intelligence teams can analyze the data stored in data warehouses for business purposes. Data warehouses play an important role in various business intelligence functions. Also, they act as a key component in creating dashboards/reports.

Data Migration

Data Migration is the process of transferring data from one system to another while changing the storage, database, or application. ETL plays an important role here. ETL tools help in integrating the contextual data which can be further used by business analysts/marketers for personalized marketing, improving the user experience, or in understanding customer behavior.

Why use ETL?

There are plenty of reasons why ETL is being used. ETL provides a method of moving data from various sources into a data warehouse. It helps companies to analyze their business data and further helps in making critical business decisions or planning marketing strategies. Sample data comparison can be performed between the source and target systems with the help of ETL. ETL offers deep historical context as well, which can be used for various business purposes. Besides, ETL helps to migrate the data into a data warehouse.

ETL Challenges Loss of Data/Irrelevant data

There is a possibility that some of the data is lost or data gets corrupted because some steps are not performed correctly while transforming or loading the data. Some irrelevant data can also be there due to such mistakes.

Disparate Data Sources

Sometimes the data sources may not be aligned or mapped properly. In such cases, dealing with these data sources becomes a big challenge.

Problems with data quality and integrity

Sometimes while normalizing or transforming the data, there can be performance issues. This may lead to loss of data quality or data integrity. Hence, it becomes another big challenge while using ETL.

ETL Tools

ETL Tools can be of different types. Some software companies develop and sell commercial ETL software products. They can be included in Enterprise Software ETL Tools. Examples of such tools are as follows:

1. SAP Data Services

2. Oracle Data Integrator

3. IBM ETL Tool

4. SAS Data Manager

Another type of ETL tool is open-source ETL tools. For example, Hadoop. Hadoop is a general-purpose distributed computing platform. It can be used to store, manipulate and analyze data. These products are free to use.

The Third type of ETL Tool is Custom ETL Tools. These are simple programming languages that are being used by many companies to write their own ETL tools. These programming languages include Python, Java, SQL, Spark, and Hadoop. These types of ETL tools provide the greatest flexibility. Although, they require a lot of effort.

Apart from these tools, Amazon AWS, Google Cloud Platform, and Microsoft Azure provide their own ETL capabilities as cloud services.

Conclusion

ETL model is being used by many companies for more than 30 years. Many companies read data from various sources, transform this extracted data using different techniques and then load it into the destination sources/systems. Though, some challenges to be faced while using/testing ETL tools, the ETL Tools are in use for many years. Companies use ETL to safely move their data from one system to another.

The media shown in this article is not owned by Analytics Vidhya and is used at the Author’s discretion.

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