Python Machine Learning A Beginner S Guide To Scikit Learn A Hands On Approach
Download Python Machine Learning A Beginner S Guide To Scikit Learn A Hands On Approach PDF/ePub or read online books in Mobi eBooks. Click Download or Read Online button to get Python Machine Learning A Beginner S Guide To Scikit Learn A Hands On Approach book now. This website allows unlimited access to, at the time of writing, more than 1.5 million titles, including hundreds of thousands of titles in various foreign languages.
Python Machine Learning
Are you ready to dive into the world of Python machine learning? Look no further! "Python Machine Learning: A Beginner's Guide to Scikit-Learn" is the perfect guide for you. Written by experienced data scientist, Rajender Kumar, this book takes you on a journey through the basics of machine learning and the powerful Scikit-learn library. Key Features: Detailed introduction to the fundamentals of machine learning and the Scikit-Learn library. Comprehensive coverage of essential concepts such as data preprocessing, model selection, evaluation, and optimization. Hands-on experience with real-world datasets and practical projects that will help you develop the skills you need to succeed in machine learning. Easy-to-follow explanations and step-by-step examples that make it easy for beginners to get started and advanced users to take their skills to the next level. See how machine learning is being used to solve problems in industries such as healthcare, finance and more. This book is perfect for beginners who are new to machine learning and want to learn Scikit-Learn from scratch. It is also ideal for intermediate and advanced users who want to expand their knowledge and build more complex models. Outcome: Unlock the earning potential of up to $300k in job after reading the book. Boosting your resume. Opening doors to new opportunities. What other people says: Don't just take our word for it - see what other readers have said: "I was able to understand machine learning concepts and implement them easily with the help of this book." "Rajender Kumar's writing style made the complex concepts easy to understand." "I highly recommend this book to anyone looking to learn machine learning with Python." Don't miss out on this opportunity to master the art of Python machine learning with "Python Machine Learning: A Beginner's Guide to Scikit-Learn". Get your copy today and start building your own intelligent systems! WHO THIS BOOK IS FOR? "Python Machine Learning: A Beginner's Guide to Scikit-Learn" is intended for a wide range of readers, including: Individuals who are new to the field of machine learning and want to gain a solid understanding of the basics and how to apply them using the popular scikit-learn library in Python. Data scientists, statisticians, and analysts who are familiar with machine learning concepts but want to learn how to implement them using Python and scikit-learn. Developers and engineers who want to add machine learning to their skill set and build intelligent applications using Python. Students and researchers who are studying machine learning and want to learn how to apply it using a widely used and accessible library like scikit-learn. Table of Contents Introduction to Machine Learning Python: A Beginner's Overview Data Preparation Supervised Learning Unsupervised Learning Deep Learning Model Selection and Evaluation The Power of Combining: Ensemble Learning Methods Real-World Applications of Machine Learning Future Directions in Python Machine Learning Additional Resources Tools and Frameworks Datasets Career Resources Glossary
Python Machine Learning from Scratch
Author: Daniel Nedal
language: en
Publisher: Createspace Independent Publishing Platform
Release Date: 2017-07-24
***BUY NOW (Will soon return to 20.59) ******Free eBook for customers who purchase the print book from Amazon*** Are you thinking of learning more about Machine Learning using Python? This book would seek to explain common terms and algorithms in an intuitive way. The author used a progressive approach whereby we start out slowly and improve on the complexity of our solutions. From AI Sciences Publisher Our books may be the best one for beginners; it's a step-by-step guide for any person who wants to start learning Artificial Intelligence and Data Science from scratch. It will help you in preparing a solid foundation and learn any other high-level courses. To get the most out of the concepts that would be covered, readers are advised to adopt a hands on approach which would lead to better mental representations. Step By Step Guide and Visual Illustrations and Examples This book and the accompanying examples, you would be well suited to tackle problems which pique your interests using machine learning. Instead of tough math formulas, this book contains several graphs and images which detail all important Machine Learning concepts and their applications. Target Users The book designed for a variety of target audiences. The most suitable users would include: Anyone who is intrigued by how algorithms arrive at predictions but has no previous knowledge of the field. Software developers and engineers with a strong programming background but seeking to break into the field of machine learning. Seasoned professionals in the field of artificial intelligence and machine learning who desire a bird's eye view of current techniques and approaches. What's Inside This Book? Supervised Learning Algorithms Unsupervised Learning Algorithms Semi-supervised Learning Algorithms Reinforcement Learning Algorithms Overfitting and underfitting correctness The Bias-Variance Trade-off Feature Extraction and Selection A Regression Example: Predicting Boston Housing Prices Import Libraries: How to forecast and Predict Popular Classification Algorithms Introduction to K Nearest Neighbors Introduction to Support Vector Machine Example of Clustering Running K-means with Scikit-Learn Introduction to Deep Learning using TensorFlow Deep Learning Compared to Other Machine Learning Approaches Applications of Deep Learning How to run the Neural Network using TensorFlow Cases of Study with Real Data Sources & References Frequently Asked Questions Q: Is this book for me and do I need programming experience? A: If you want to smash Machine Learning from scratch, this book is for you. If you already wrote a few lines of code and recognize basic programming statements, you'll be OK. Q: Does this book include everything I need to become a Machine Learning expert? A: Unfortunately, no. This book is designed for readers taking their first steps in Machine Learning and further learning will be required beyond this book to master all aspects of Machine Learning. Q: Can I have a refund if this book is not fitted for me? A: Yes, Amazon refund you if you aren't satisfied, for more information about the amazon refund service please go to the amazon help platform. We will also be happy to help you if you send us an email at [email protected]. If you need to see the quality of our job, AI Sciences Company offering you a free eBook in Machine Learning with Python written by the data scientist Alain Kaufmann at http: //aisciences.net/free-books/
Python Machine Learning
★☆Have you come across the terms machine learning and neural networks in most articles you have recently read? Do you also want to learn how to build a machine learning model that will answer your questions within a blink of your eyes?☆★ If you responded yes to any of the above questions, you have come to the right place. Machine learning is an incredibly dense topic. It's hard to imagine condensing it into an easily readable and digestible format. However, this book aims to do exactly that. Machine learning and artificial intelligence have been used in different machines and applications to improve the user's experience. One can also use machine learning to make data analysis and predicting the output for some data sets easy. All you need to do is choose the right algorithm, train the model and test the model before you apply it on any real-world tool. It is that simple isn't it? ★★Apart from this, you will also learn more about★★ ♦ The Different Types Of Learning Algorithm That You Can Expect To Encounter ♦ The Numerous Applications Of Machine Learning And Deep Learning ♦ The Best Practices For Picking Up Neural Networks ♦ What Are The Best Languages And Libraries To Work With ♦ The Various Problems That You Can Solve With Machine Learning Algorithms ♦ And much more... Well, you can do it faster if you use Python. This language has made it easy for any user, even an amateur, to build a strong machine learning model since it has numerous directories and libraries that make it easy for one to build a model. Do you want to know how to build a machine learning model and a neural network? So, what are you waiting for? Grab a copy of this book now!