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Author: Manohar Swamynathan
Publisher: Apress
ISBN: 148424947X
Category : Computers
Languages : en
Pages : 469
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Book Description
Explore fundamental to advanced Python 3 topics in six steps, all designed to make you a worthy practitioner. This updated version’s approach is based on the “six degrees of separation” theory, which states that everyone and everything is a maximum of six steps away and presents each topic in two parts: theoretical concepts and practical implementation using suitable Python 3 packages. You’ll start with the fundamentals of Python 3 programming language, machine learning history, evolution, and the system development frameworks. Key data mining/analysis concepts, such as exploratory analysis, feature dimension reduction, regressions, time series forecasting and their efficient implementation in Scikit-learn are covered as well. You’ll also learn commonly used model diagnostic and tuning techniques. These include optimal probability cutoff point for class creation, variance, bias, bagging, boosting, ensemble voting, grid search, random search, Bayesian optimization, and the noise reduction technique for IoT data. Finally, you’ll review advanced text mining techniques, recommender systems, neural networks, deep learning, reinforcement learning techniques and their implementation. All the code presented in the book will be available in the form of iPython notebooks to enable you to try out these examples and extend them to your advantage. What You'll Learn Understand machine learning development and frameworksAssess model diagnosis and tuning in machine learningExamine text mining, natuarl language processing (NLP), and recommender systemsReview reinforcement learning and CNN Who This Book Is For Python developers, data engineers, and machine learning engineers looking to expand their knowledge or career into machine learning area.
Author: Daneyal Anis
Publisher:
ISBN:
Category :
Languages : en
Pages : 68
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Book Description
*Start your Data Science career using Python today!* Are you ready to start your new exciting career? Ready to crush your machine learning career goals? Are you overwhelmed with complexity of the books on this subject?Then let this breezy and fun little book on Python and machine learning models make you a data scientist in 7 days! First part of this book introduces Python basics including: 1) Data Structures like Pandas 2) Foundational libraries like Numpy, Seaborn and Scikit-Learn Second part of this book shows you how to build predictive machine learning models step by step using techniques such as: 1) Regression analysis 2) Decision tree analysis 3) Training and testing data models 4) And much more! After reading this book you will be able to: 1) Code in Python with confidence 2) Build new machine learning models from scratch 3) Know how to clean and prepare your data for analytics 4) Speak confidently about statistical analysis techniques Data Science was ranked the fast-growing field by LinkedIn and Data Scientist is one of the most highly sought after and lucrative careers in the world! If you are on the fence about making the leap to a new and lucrative career, this is the book for you! What sets this book apart from other books on the topic of Python and Machine learning: 1) Step by step code examples and explanation 2) Complex concepts explained visually 3) Real world applicability of the machine learning models introduced 4) Bonus free code samples that you can try yourself without any prior experience in Python! What do I need to get started? You will have a step by step action plan in place once you finish this book and finally feel that you, can master data science and machine learning and start lucrative and rewarding career! Ready to dive in to the exciting world of Python and Machine Learning? Then scroll up to the top and hit that BUY BUTTON!
Author: Rudolph Russell
Publisher: Createspace Independent Publishing Platform
ISBN: 9781719528405
Category :
Languages : en
Pages : 106
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Book Description
MACHINE LEARNING - PYTHON Buy the Paperback version of this book, and get the Kindle eBook version included for FREE! Do You Want to Become An Expert Of Machine Learning?? Start Getting this Book and Follow My Step by Step Explanations! Click Add To Cart Now! This book is for anyone who would like to learn how to develop machine-learning systems. We will cover the most important concepts about machine learning algorithms, in both a theoretical and a practical way, and we'll implement many machine-learning algorithms using the Scikit-learn library in the Python programming language. In the first chapter, you'll learn the most important concepts of machine learning, and, in the next chapter, you'll work mainly with the classification. In the last chapter you'll learn how to train your model. I assume that you've knowledge of the basics of programming This book contains illustrations and step-by-step explanations with bullet points and exercises for easy and enjoyable learning. Benefits of reading this book that you're not going to find anywhere else: Introduction to Machine Learning Classification How to train a Model Different Models Combinations Don't miss out on this new step by step guide to Machine Learning. All you need to do is scroll up and click on the BUY NOW button to learn all about it!
Author: Daneyal Anis
Publisher:
ISBN:
Category :
Languages : en
Pages : 200
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Book Description
*Start your Data Science career using Python today!*Are you ready to start your new exciting career? Ready to master artificial intelligence and deep learning concepts?Are you overwhelmed with complexity of the books on this subject?Then let this breezy and fun little book on Python, Machine Learning and Deep Learning models make you a Data Scientist in 7 days!This book continues from where the first book in the series, Ultimate Step by Step Guide to Machine Learning Using Python, left of. In the first book you were introduced to Python concepts such as: -Data Structures like Pandas -Foundational libraries like Numpy, Seaborn and Scikit-Learn-Regression analysis-Classification-Clustering-Association Learning-Dimension ReductionThis book builds on those concepts to expand on Machine Learning algorithms like: -Linear and Logistical regression-Decision tree-Support vector machines (SVM)After that, this book takes you on a journey into Deep Learning and Neural Networks with important concepts and libraries like: -Convolutional and Recurrent Neural Networks-TensorFlow-Keras-PyTorch-Keras-Apache MXNet-Microsoft Cognitive Toolkit (CNTK)The final part of the book covers all foundational concepts that are required for Amazon Web Services (AWS) Certified Machine Learning Specialization by explaining how to deploy your models at scale on Cloud technologies. While AWS is used in the book for illustrative purposes, Microsoft Azure and Google Cloud are also introduced as alternative cloud technologies. After reading this book you will be able to: -Code in Python with confidence-Build new machine learning and deep learning models from scratch-Know how to clean and prepare your data for analytics-Speak confidently about statistical analysis techniquesData Science was ranked the fast-growing field by LinkedIn and Data Scientist is one of the most highly sought after and lucrative careers in the world!If you are on the fence about making the leap to a new and lucrative career, this is the book for you!What sets this book apart from other books on the topic of Python and Machine learning: -Step by step code examples and explanation-Complex concepts explained visually-Real world applicability of the machine learning and deep learning models introducedWhat do I need to get started?You will have a step by step action plan in place once you finish this book and finally feel that you, can master data science and artificial intelligence and start a lucrative and rewarding career! Ready to dive in to the exciting world of Python and Deep Learning?Then scroll up to the top and hit that BUY BUTTON!
Author: Manohar Swamynathan
Publisher: Apress
ISBN: 148424947X
Category : Computers
Languages : en
Pages : 469
Get Book Here
Book Description
Explore fundamental to advanced Python 3 topics in six steps, all designed to make you a worthy practitioner. This updated version’s approach is based on the “six degrees of separation” theory, which states that everyone and everything is a maximum of six steps away and presents each topic in two parts: theoretical concepts and practical implementation using suitable Python 3 packages. You’ll start with the fundamentals of Python 3 programming language, machine learning history, evolution, and the system development frameworks. Key data mining/analysis concepts, such as exploratory analysis, feature dimension reduction, regressions, time series forecasting and their efficient implementation in Scikit-learn are covered as well. You’ll also learn commonly used model diagnostic and tuning techniques. These include optimal probability cutoff point for class creation, variance, bias, bagging, boosting, ensemble voting, grid search, random search, Bayesian optimization, and the noise reduction technique for IoT data. Finally, you’ll review advanced text mining techniques, recommender systems, neural networks, deep learning, reinforcement learning techniques and their implementation. All the code presented in the book will be available in the form of iPython notebooks to enable you to try out these examples and extend them to your advantage. What You'll Learn Understand machine learning development and frameworksAssess model diagnosis and tuning in machine learningExamine text mining, natuarl language processing (NLP), and recommender systemsReview reinforcement learning and CNN Who This Book Is For Python developers, data engineers, and machine learning engineers looking to expand their knowledge or career into machine learning area.
Author: Ankur Ankan
Publisher: Packt Publishing Ltd
ISBN: 1784395218
Category : Computers
Languages : en
Pages : 284
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Book Description
Master probabilistic graphical models by learning through real-world problems and illustrative code examples in Python About This Book Gain in-depth knowledge of Probabilistic Graphical Models Model time-series problems using Dynamic Bayesian Networks A practical guide to help you apply PGMs to real-world problems Who This Book Is For If you are a researcher or a machine learning enthusiast, or are working in the data science field and have a basic idea of Bayesian Learning or Probabilistic Graphical Models, this book will help you to understand the details of Graphical Models and use it in your data science problems. This book will also help you select the appropriate model as well as the appropriate algorithm for your problem. What You Will Learn Get to know the basics of Probability theory and Graph Theory Work with Markov Networks Implement Bayesian Networks Exact Inference Techniques in Graphical Models such as the Variable Elimination Algorithm Understand approximate Inference Techniques in Graphical Models such as Message Passing Algorithms Sample algorithms in Graphical Models Grasp details of Naive Bayes with real-world examples Deploy PGMs using various libraries in Python Gain working details of Hidden Markov Models with real-world examples In Detail Probabilistic Graphical Models is a technique in machine learning that uses the concepts of graph theory to compactly represent and optimally predict values in our data problems. In real world problems, it's often difficult to select the appropriate graphical model as well as the appropriate inference algorithm, which can make a huge difference in computation time and accuracy. Thus, it is crucial to know the working details of these algorithms. This book starts with the basics of probability theory and graph theory, then goes on to discuss various models and inference algorithms. All the different types of models are discussed along with code examples to create and modify them, and also to run different inference algorithms on them. There is a complete chapter devoted to the most widely used networks Naive Bayes Model and Hidden Markov Models (HMMs). These models have been thoroughly discussed using real-world examples. Style and approach An easy-to-follow guide to help you understand Probabilistic Graphical Models using simple examples and numerous code examples, with an emphasis on more widely used models.
Author: Hoss Belyadi
Publisher: Gulf Professional Publishing
ISBN: 0128219300
Category : Science
Languages : en
Pages : 478
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Book Description
Machine Learning Guide for Oil and Gas Using Python: A Step-by-Step Breakdown with Data, Algorithms, Codes, and Applications delivers a critical training and resource tool to help engineers understand machine learning theory and practice, specifically referencing use cases in oil and gas. The reference moves from explaining how Python works to step-by-step examples of utilization in various oil and gas scenarios, such as well testing, shale reservoirs and production optimization. Petroleum engineers are quickly applying machine learning techniques to their data challenges, but there is a lack of references beyond the math or heavy theory of machine learning. Machine Learning Guide for Oil and Gas Using Python details the open-source tool Python by explaining how it works at an introductory level then bridging into how to apply the algorithms into different oil and gas scenarios. While similar resources are often too mathematical, this book balances theory with applications, including use cases that help solve different oil and gas data challenges. - Helps readers understand how open-source Python can be utilized in practical oil and gas challenges - Covers the most commonly used algorithms for both supervised and unsupervised learning - Presents a balanced approach of both theory and practicality while progressing from introductory to advanced analytical techniques
Author: Flynn Fisher
Publisher: Flynn Fisher
ISBN:
Category : Computers
Languages : en
Pages : 68
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Book Description
The Complete Python Masterclass Made Easy, Even if You’ve Never Coded in Your Life! If you go on Google right at this second and open any statistics with the most in-demand programming languages for the past 5 years until today you will consistently see in the top 3 a language called ‘Python’. More often than not, it is the number one programming language to learn year after year. But why would so many people look for Python experts? Two big reasons: - It’s an extremely powerful high-level programming language - The coding syntax is very simplified, making it fail-proof to learn and execute Combining those two things makes Python constantly being improved and updated. While learning the basics is something that will get you started, you will have the ability to grow your skills above and beyond because there’s always new updates and improvements being made. In ‘Learn Python Programming for Beginners’, Flynn Fisher starts from scratch. He will teach you the fundamentals of coding with Python and help you lay down the building blocks of your future programming abilities. This book is made in a way that every chapter is building upon each other. By the end, you will learn: - The Fundamentals of Python Programming laid down in a matter of days with a no nonsense approach of learning - Creating Operations by Combining the Fundamentals together and building upon each other step-bystep - Machine Learning with Python explained in plain English that will get you to skyrocket your education and your programming skills - Apply Your Knowledge with the practical exercises inside the book, which cover everything from the basics to data analysis and machine learning Programming can be hard if you don’t have a precise step-by-step guide. Luckily, inside this book, you will find all the building blocks needed to start your Python programming journey. See you inside as you start your Python coding journey!
Author: Aman Kharwal
Publisher:
ISBN: 935648483X
Category : Computers
Languages : en
Pages : 253
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Book Description
Key Features: Clear Explanations of Machine Learning Algorithms: The book offers clear and concise explanations of machine learning algorithms, ensuring that readers of all levels can grasp the concepts effortlessly. Hands-On Approach: Packed with practical examples using Python and code snippets, you'll gain a hands-on understanding of how each algorithm works and learn to implement them in real projects. Comprehensive Coverage: From linear regression and support vector machines to decision trees and neural networks, the book covers a wide array of algorithms, giving you a solid foundation to explore diverse problem domains. Performance Evaluation Methods: Learn how to evaluate the effectiveness of your models, identify areas for improvement, and optimize their performance using industry-standard evaluation techniques. Data Preprocessing Techniques: Discover the critical elements of data preprocessing that lay the groundwork for building robust and accurate machine learning models. Time Series Forecasting: Explore advanced algorithms specifically designed for time series data, a critical component of numerous real-world applications. Appendix for Easy Reference: Access all parameters of commonly used machine learning algorithms in a handy appendix, facilitating efficient model tuning.
Author: Anderson Coen
Publisher:
ISBN:
Category :
Languages : en
Pages : 154
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Book Description
Python Machine Learning Would you want to learn how to utilize Python to produce machine learning models, but you think it would be too complicated for you? Or maybe you like to automate simple stuff with your PC, but you do not know how to do it. As a novice, you might think programming is complicated. Understanding artificial intelligence coding could take several months. Not to mention that the chance of giving up before perfecting it could be high. Therefore, you could think of employing a professional developer to shorten the time if you have time to develop. That might look like a great solution, but it is surely very costly. You still have pay for the developer if he doesn't do the proper job you want. You know the best solution for this? The perfect solution is to follow a complete programming manual with hands-on projects as well as practical exercises. This book is structured as a course with six chapters. Inside the book, you will be able to go through a first section in which basic and fundamental notions of deep learning are mention, to get to the next chapters made to help you learn advanced coding insights needed to build training data sets for the development of successful machine learning models. In detail, you will learn: The Fundamentals of Machine Learning Machine-Learning Systems An Overview of Python for Machine Learning Understanding Python Libraries for Machine Learning Introducing Neural Networks and Deep Learning Practical Data Management What makes this book different? The majority of books available on the market take a brief look into machine learning, presenting some of the subjects but never going deep. This book is not one of those. Even if you are totally new to programming in 2020 or you're simply looking to widen your abilities as a programmer, this book is perfect for you! Well, stress no more! Buy this book and also learn all... and DOWNLOAD IT NOW!
Author: Ryan Turner
Publisher: Publishing Factory
ISBN:
Category : Computers
Languages : en
Pages : 119
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Book Description
Are you a novice programmer who wants to learn Python Machine Learning? Are you worried about how to translate what you already know into Python? This book will help you overcome those problems. As machines get ever more complex and perform more and more tasks to free up our time, so it is that new ideas are developed to help us continually improve their speed and abilities. One of these is Python and in Python Machine Learning: The Ultimate Beginner's Guide to Learn Python Machine Learning Step by Step using Scikit-Learn and Tensorflow, you will discover information and advice on: • What machine learning is • The history of machine learning • Approaches to machine learning • Support vector machines • Machine learning and neural networks • The Internet of Things (IoT) • The future of machine learning • And more… This book has been written specifically for beginners and the simple, step by step instructions and plain language make it an ideal place to start for anyone who has a passing interest in this fascinating subject. Python really is an amazing system and can provide you with endless possibilities when you start learning about it. Get a copy of Python Machine Learning today and see where the future lies!
