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Author: Ai Publishing Publisher: ISBN: 9781734790153 Category : Languages : en Pages : 302
Book Description
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.
Author: Ai Publishing Publisher: ISBN: 9781734790153 Category : Languages : en Pages : 302
Book Description
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.
Author: Finn Sanders Publisher: Roland Bind ISBN: Category : Computers Languages : en Pages : 150
Book Description
Imagine a world where you can make a computer program learn for itself? What if it could recognize who is in a picture or the exact websites that you want to look for when you type it into the program? What if you were able to create any kind of program that you wanted, even as a beginner programmer, without all of the convoluted codes and other information that makes your head spin? This is actually all possible. The programs that were mentioned before are all a part of machine learning. This is a breakthrough in the world of information technology, which allows the computer to learn how to behave, rather than asking the programmer to think of every single instance that may show up with their user ahead of time. it is taking over the world, and you may be using it now, without even realizing it. If you have used a search engine, worked with photo recognition, or done speech recognition devices on your phone, then you have worked with machine learning. And if you combine it with the Python programming language, it is faster, more powerful, and easier (even for beginners) to create your own programs today. Python is considered the ultimate coding language for beginners, but once you start to use it, you will never be able to tell. Many of the best programs out there use this language behind them, and if you are a beginner who is ready to learn, this is a great place to start. If you have a program in mind, or you just want to be able to get some programming knowledge and learn more about the power that comes behind it, then this is the guidebook for you. ★★Some of the topics that we will discuss include★★ ♦ The Fundamentals of Machine Learning, Deep learning, And Neural Networks ♦ How To Set Up Your Environment And Make Sure That Python, TensorFlow And Scikit-Learn Work Well For You ♦ How To Master Neural Network Implementation Using Different Libraries ♦ How Random Forest Algorithms Are Able To Help Out With Machine Learning ♦ How To Uncover Hidden Patterns And Structures With Clustering ♦ How Recurrent Neural Networks Work And When To Use ♦ The Importance Of Linear Classifiers And Why They Need To Be Used In Machine Learning ♦ And Much More! This guidebook is going to provide you with the information you need to get started with Python Machine Learning. If you have an idea for a great program, but you don't have the technical knowledge to make it happen, then this guidebook will help you get started. Machine learning has the capabilities, and Python has the ease, to help you, even as a beginner, create any product that you would like. If you want to learn more about how to make the best programs with Python Machine learning, buy the book today!
Author: Django Smith Publisher: Independently Published ISBN: 9781073019335 Category : Languages : en Pages : 144
Book Description
Start Programming Python What if you could make your own program, one that is able to learn by trial and error, or based on the information that you show it? What if you could get a program that could adapt and change based on the input of the user? And what if you were able to make all of this happen with the Python coding language, helping even beginner's work with more complicated codes? This is all possible with Python machine learning. This guidebook is going to take some time to look at Python machine learning and all of the neat things that you are able to do with it. Machine learning is a growing field, one that a lot of programmers want to spend their time on. But even though this sounds like a complicated part of technology to work with, you will find that with the help of the Python coding language, anyone can start writing their own codes in machine learning. This guidebook is going to take a look at all of the different topics that you need to know in order to get started with Python machine learning. Some of the topics that we will explore inside include: The basics of machine learning The difference between supervised and unsupervised machine learning. Setting up your new environment in the Python language. Data preprocessing with the help of machine learning. How to use Python coding to help with linear regression. Decision trees and random forests. How to work with support vector regression problems. Can machine learning really help with Naïve Bayes problems? Accelerated data analysis using the Python code. And so much more! If you have been interested in learning more about machine learning, and you want to be able to learn a few of the codes that can make it happen for you, make sure to check out this guidebook to help you get started! If all of this sounds like your ideal book, then hop on over and hit now that buy button! Well, stress no more! Buy this book and also learn all... and DOWNLOAD IT NOW! ★★Buy the Paperback Version of this Book and get the Kindle Book version for FREE ★★
Author: Tariq Rashid Publisher: ISBN: 9781492064046 Category : Languages : de Pages : 232
Book Description
Neuronale Netze sind Schlüsselelemente des Deep Learning und der Künstlichen Intelligenz, die heute zu Erstaunlichem in der Lage sind. Dennoch verstehen nur wenige, wie Neuronale Netze tatsächlich funktionieren. Dieses Buch nimmt Sie mit auf eine unterhaltsame Reise, die mit ganz einfachen Ideen beginnt und Ihnen Schritt für Schritt zeigt, wie Neuronale Netze arbeiten. Dafür brauchen Sie keine tieferen Mathematik-Kenntnisse, denn alle mathematischen Konzepte werden behutsam und mit vielen Illustrationen erläutert. Dann geht es in die Praxis: Sie programmieren Ihr eigenes Neuronales Netz mit Python und bringen ihm bei, handgeschriebene Zahlen zu erkennen, bis es eine Performance wie ein professionell entwickeltes Netz erreicht. Zum Schluss lassen Sie das Netz noch auf einem Raspberry Pi Zero laufen. - Tariq Rashid hat eine besondere Fähigkeit, schwierige Konzepte verständlich zu erklären, dadurch werden Neuronale Netze für jeden Interessierten zugänglich und praktisch nachvollziehbar.
Author: William Gray Publisher: Independently Published ISBN: 9781096755364 Category : Languages : en Pages : 276
Book Description
What exactly is machine learning and why is it so valuable in the online business ? Are you thinking of learning Python machine learning ?This book teach well you the practical ways to do it ! ★★★ Buy the Paperback version and get the Kindle Book versions for FREE ★★★ Machine Learning is a branch of AI that applied algorithms to learn from data and create predictions - this is important in predicting the world around us. Python is a popular and open-source programming language. In addition, it is one of the most applied languages in artificial intelligence and other scientific fields. Today, it is a top skill in high demand in the job market. Machine learning has become an integral part of many commercial applications and research projects. Using Python, even as a beginner, this book will teach you practical ways to build your own machine learning solutions. Inside Introduction to Machine Learning with Python, you'll learn: Fundamental concepts and applications of machine learning Understand the various categories of machine learning algorithms. Some of the branches of Artificial Intelligence The basics of Python Concepts of Machine Learning using Python Python Machine Learning Applications Machine Learning Case Studies with Python The way that Python evolved throughout time And many more Throughout the recent years, artificial intelligence and machine learning have made some enormous, significant strides in terms of universal, global applicability. You'll discover the steps required to develop a successful machine-learning application using Python. Introduction to Machine Learning with Python is a step-by-step guide for any person who wants to start learning Artificial Intelligence - It will help you in preparing a solid foundation and learn any other high-level courses. Stay ahead and make a choice that will last... If You like to know more, scroll to the top and select " BUY NOW " buttom ★★★ Buy the Paperback version and get the Kindle Book versions for FREE ★★★
Author: Harsh Bhasin Publisher: BPB Publications ISBN: 9389845424 Category : Computers Languages : en Pages : 262
Book Description
Get familiar with various Supervised, Unsupervised and Reinforcement learning algorithms KEY FEATURES ● Understand the types of Machine learning. ● Get familiar with different Feature extraction methods. ● Get an overview of how Neural Network Algorithms work. ● Learn how to implement Decision Trees and Random Forests. ● The book not only explains the Classification algorithms but also discusses the deviations/ mathematical modeling. DESCRIPTION This book covers important concepts and topics in Machine Learning. It begins with Data Cleansing and presents an overview of Feature Selection. It then talks about training and testing, cross-validation, and Feature Selection. The book covers algorithms and implementations of the most common Feature Selection Techniques. The book then focuses on Linear Regression and Gradient Descent. Some of the important Classification techniques such as K-nearest neighbors, logistic regression, Naïve Bayesian, and Linear Discriminant Analysis are covered in the book. It then gives an overview of Neural Networks and explains the biological background, the limitations of the perceptron, and the backpropagation model. The Support Vector Machines and Kernel methods are also included in the book. It then shows how to implement Decision Trees and Random Forests. Towards the end, the book gives a brief overview of Unsupervised Learning. Various Feature Extraction techniques, such as Fourier Transform, STFT, and Local Binary patterns, are covered. The book also discusses Principle Component Analysis and its implementation. WHAT WILL YOU LEARN ● Learn how to prepare Data for Machine Learning. ● Learn how to implement learning algorithms from scratch. ● Use scikit-learn to implement algorithms. ● Use various Feature Selection and Feature Extraction methods. ● Learn how to develop a Face recognition system. WHO THIS BOOK IS FOR The book is designed for Undergraduate and Postgraduate Computer Science students and for the professionals who intend to switch to the fascinating world of Machine Learning. This book requires basic know-how of programming fundamentals, Python, in particular. TABLE OF CONTENTS 1. An introduction to Machine Learning 2. The beginning: Pre-Processing and Feature Selection 3. Regression 4. Classification 5. Neural Networks- I 6. Neural Networks-II 7. Support Vector machines 8. Decision Trees 9. Clustering 10. Feature Extraction Appendix A1. Cheat Sheets A2. Face Detection A3.Biblography
Author: Andrew Johnson Publisher: Amplitudo Limited ISBN: 9781801140157 Category : Languages : en Pages : 362
Book Description
Would you like to start programming with Python from scratch? This is the easiest way you can find it! What are you waiting for? Keep reading! The PROGRAMMING LANGUAGES ACADEMY has created a targeted learning path within reach of anyone who wants to start programming without appropriate skills. In this book, you will find a real step by step path that will take you from 0 to 100 in a few days!!! Once you start reading, you will appreciate a simple, straightforward, and essential guide. The chapters are short and will deliver new information slowly to avoid being overwhelmed by too many notions altogether. Illustrations, examples, and step-by-step guides in each chapter allow you not to make mistakes but, above all, not to confuse. You no longer have to waste time and money trying to learn Python from expensive online courses or from incredibly long textbooks that leave you just more confused and frustrated. Python Workbook: Learn How to Quickly and Effectively Program with Exercises, Projects, and Solutions Do you want to learn one of today's most in-demand programming languages and start an exciting career in data science, web development, or another field of your choice? Learn Python! Python is easy to read because the code looks a lot like regular English, but don't let this simplicity deceive you: it's one of the most influential and versatile programming languages out there! It powers many of your favorite websites and services, including Instagram, Spotify, and even Google! This book takes you on a practical journey through the fantastic features of Python. Unlike books that focus on theoretical concepts only, this book will show you how Python is used - and encourage you to get creative! Here's what you'll find in this book: Practical programming exercises that will help you apply programming concepts to real-life situations Debugging activities that will teach you to notice errors in Python code quickly Fun projects that will test your knowledge and motivate you to practice even more Valuable tips for mastering Python quickly An answer key to check if you were right Learning the basics of any programming language may seem a bit boring at first, but once you've written your first program that does something - even if it's just printing text on the screen - your excitement and motivation will become unstoppable. You'll yearn for more and more programming challenges that will hone your skills! If you've tried learning Python before but got discouraged by too much theory... this book is guaranteed to rekindle your interest in Python programming! Are you ready to start writing Python apps that work? If you're prepared to learn the basics of python programming 7 DAYS FROM TODAY, get a copy of this book today!
Author: Steven Williams Publisher: ISBN: Category : Languages : en Pages : 242
Book Description
If you want to learn how to design and master different Machine Learning algorithms quickly and easily, then keep reading. We live in a world of data deluge where gigabytes of data are generated daily. It is possible that this data might not be very useful for our daily applications. Major setbacks in the use of such data may be due to the presence of loopholes in data links previously generated or the data might be too vast for the limited human mind. Machine learning in this book presents some of the solutions to the problems above. Being an introductory guide, expect to learn the various basics involved in Machine Learning and Python. This book provides an insight into the new world of big data, then behooves you to learn more about Machine Learning. With a detailed and concise overview of the fundamentals, along with the challenges and limitations currently being tackled by the pros, inside this comprehensive guide you will Learn the Fundamentals of Machine Learning which Are Being Developed and Advanced with Python What is Machine Learning and how it is applied in real-world situations Algorithms, in a Language that Requires No Prior Background in Python Discover best practices for evaluating and tuning models Discover the Details of the Supervised, Unsupervised, and Reinforcement Algorithms, which Serve as the Skeleton of Hundreds of Machine Learning Algorithms Being Developed Every Day Become Familiar with Data Science Technology, an Umbrella Term Used for the Cutting-Edge Technologies of Today Understand the Entire Process of Creating Neural Network Models on TensorFlow, Using Open Source Data Sets and real Python Code Uncover the Secrets of the Most Critical Aspect of Developing a Machine Learning Model - Data Pre-Processing and Training/Testing Subsets Artificial Neural Networks And Much More! So what are you waiting for? Even if some concepts of Machine Learning algorithms can appear complex to most computer programming beginners, this book takes the time to explain them in a simple and concise way. Would You Like To Know More? Scroll up and click on the BUY NOW button to get your copy now!
Author: AI OU Publisher: ISBN: 9781801814805 Category : Languages : en Pages : 301
Book Description
This course lays the foundations for both a theoretical and practical understanding of machine learning and artificial intelligence, utilizing Python as a beginner-friendly introduction and invitation to further study Key Features A crash course in Python programming Interactive, guided practice through a series of machine learning exercises Instant access to PDFs, Python codes, and exercises from the publisher's website at no extra cost Book Description Machine Learning (ML) and Artificial Intelligence (AI) are here to stay. Yes, that's right. Based on a significant amount of data and evidence, it's obvious that ML and AI are here to stay. Consider any industry today. The practical applications of ML are really driving business results. Whether it's healthcare, e-commerce, government, transportation, social media sites, financial services, manufacturing, oil and gas, marketing, and sales. You 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. 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. By the end of this course, you will have a firm grasp on the theoretical foundations of machine learning and artificial intelligence as well as having explored and practiced various real-world applications through Python. The code bundle for this course is available at https://www.aispublishing.net/nlp-crash-course1603576259757 What you will learn Get up to speed with Python programming Explore Python NumPy and Pandas libraries for data analysis Practice data visualization via Matplotlib, Seaborn, and Pandas libraries Solve regression problems in ML using Sklearn library Solve classification problems in ML using Sklearn library Study data clustering with ML using Sklearn library Cover deep learning with Python TensorFlow 2.0 Perform dimensi...
Author: Zach Codings Publisher: ISBN: 9781696563116 Category : Languages : en Pages : 148
Book Description
What is machine learning and why would a programmer want to learn how to use it? Is artificial intelligence the same as working with machine learning? Are you interested in becoming a machine learning expert but don't know where to start from? Keep reading... The future of our world is evolving towards an era where interaction with machines form the foundation of most tasks we perform. In light of this, it is important to gain actionable knowledge in machine learning technologies and skills. These skills will be useful in the near future as you maneuver through different career paths. Today data is driving many business processes, and without data, it is impossible to imagine where many of the top businesses would be. Imagine how you used to struggle with search results online back in the day, and how easy it is to look for something online today and get the right results. All this is possible through machine learning models. What you need is a foundational approach to learning the basics of machine learning. You can use this knowledge to build your expertise in machine learning over time. While this is an introductory level book, it introduces you to vast concepts in machine learning that will be important to your career. By the end of the book, you will have learned so much about machine learning and the respective python libraries that you will use when building models all the time. An important aspect of machine learning that we must stress even at this juncture is data analysis. Data is key to the success of machine learning and deep learning models. When implemented properly, the kind of data you have will make a big difference in whether your model succeeds or not. Since we are discussing machine learning and the future of computing as we know it, we will also dedicate some time to discussing the current trends in the world, and how they affect our ability to perform some tasks. In this case, we will look at the Internet of Things (IoT) and how we can use different approaches to integrate machine learning and IoT models. Throughout these pages, you will learn: The Fundamentals of Python for Machine Learning Data Analysis in Python Comparing Deep Learning and Machine Learning Machine Learning with Scikit-Learn Deep Learning with TensorFlow Deep Learning with PyTorch and Keras The Role of Machine Learning in the Internet of Things (IoT) Looking to the Future with Machine Learning And much more... Even if you don't have any background in machine learning and Python programming, this book will give you the tools to develop machine learning models. Arm yourself with all this knowledge! Scroll up and click the BUY NOW BUTTON!
Author: Ai Publishing
Author: Ai Publishing
Author: Finn Sanders
Author: Django Smith
Author: Tariq Rashid
Author: William Gray
Author: Harsh Bhasin
Author: Andrew Johnson
Author: Steven Williams
Author: AI OU
Author: Zach Codings