Do you want to learn how machine learning and neural networks work quickly and simply? Do you want to know how to build a machine learning model, and you have no programming skills? Do you want to get started with learning data science?
This book is going to guide you to the basics and the principles behind machine learning. Machine learning is an active research domain and includes several different approaches. This book is going to help you understand the various methods of machine learning and neural networks. It will guide you through the steps you need to build a machine learning model.
Machine learning implies programming. This book will teach you Python programming. This book does not require any pre-programming skills. It will help to get you started in Python programming, as well as how to use Python libraries to analyze data and apply machine learning.
Overall, this book is a go-to guide for getting started in machine learning modeling using Python programming. Once you get through the book, you will be able to develop your machine learning models using Python.
Through this book, you will learn:
- Principles of machine learning
- Types of machine learning: supervised, unsupervised, semi-supervised, and reinforcement learning
- Advantages of each type of machine learning
- Principle and types of neural networks
- Steps to develop and fit artificial neural network model
- Getting started and installing Python
- Tools and platforms for Python programming
- How to use pandas, NumPy and matplotlib Python libraries
- How to develop a simple linear and logistic machine learning model
- How to build and train a multi-layer artificial neural network two ways: from scratch and using the Python libraries
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.
Do you want to learn how machine learning and neural networks work quickly and simply? Do you want to know how to build a machine learning model, and you have no programming skills? Do you want to get started with learning data science?
This book is going to guide you to the basics and the principles behind machine learning. Machine learning is an active research domain and includes several different approaches. This book is going to help you understand the various methods of machine learning and neural networks. It will guide you through the steps you need to build a machine learning model.
Machine learning implies programming. This book will teach you Python programming. This book does not require any pre-programming skills. It will help to get you started in Python programming, as well as how to use Python libraries to analyze data and apply machine learning.
Overall, this book is a go-to guide for getting started in machine learning modeling using Python programming. Once you get through the book, you will be able to develop your machine learning models using Python.
Through this book, you will learn:
- Principles of machine learning
- Types of machine learning: supervised, unsupervised, semi-supervised, and reinforcement learning
- Advantages of each type of machine learning
- Principle and types of neural networks
- Steps to develop and fit artificial neural network model
- Getting started and installing Python
- Tools and platforms for Python programming
- How to use pandas, NumPy and matplotlib Python libraries
- How to develop a simple linear and logistic machine learning model
- How to build and train a multi-layer artificial neural network two ways: from scratch and using the Python libraries
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.
Описание: Equipped with the latest updates, this third edition of Python Machine Learning By Example provides a comprehensive course for ML enthusiasts to strengthen their command of ML concepts, techniques, and algorithms.
Through a series of recent breakthroughs, deep learning has boosted the entire field of machine learning. Now, even programmers who know close to nothing about this technology can use simple, efficient tools to implement programs capable of learning from data.
The updated edition of this practical book uses concrete examples, minimal theory, and three production-ready Python frameworks--scikit-learn, Keras, and TensorFlow--to help you gain an intuitive understanding of the concepts and tools for building intelligent systems. You'll learn a range of techniques, starting with simple linear regression and progressing to deep neural networks. With exercises in each chapter to help you apply what you've learned, all you need is programming experience to get started.
Описание: Starting from the basics of neural networks, this book covers over 50 applications of computer vision and helps you to gain a solid understanding of the theory of various architectures before implementing them. Each use case is accompanied by a notebook in GitHub with ready-to-execute code and self-assessment questions.
Integrate scikit-learn with various tools such as NumPy, pandas, imbalanced-learn, and scikit-surprise and use it to solve real-world machine learning problems
Key Features
Delve into machine learning with this comprehensive guide to scikit-learn and scientific Python
Master the art of data-driven problem-solving with hands-on examples
Foster your theoretical and practical knowledge of supervised and unsupervised machine learning algorithms
Book Description
Machine learning is applied everywhere, from business to research and academia, while scikit-learn is a versatile library that is popular among machine learning practitioners. This book serves as a practical guide for anyone looking to provide hands-on machine learning solutions with scikit-learn and Python toolkits.
The book begins with an explanation of machine learning concepts and fundamentals, and strikes a balance between theoretical concepts and their applications. Each chapter covers a different set of algorithms, and shows you how to use them to solve real-life problems. You'll also learn about various key supervised and unsupervised machine learning algorithms using practical examples. Whether it is an instance-based learning algorithm, Bayesian estimation, a deep neural network, a tree-based ensemble, or a recommendation system, you'll gain a thorough understanding of its theory and learn when to apply it. As you advance, you'll learn how to deal with unlabeled data and when to use different clustering and anomaly detection algorithms.
By the end of this machine learning book, you'll have learned how to take a data-driven approach to provide end-to-end machine learning solutions. You'll also have discovered how to formulate the problem at hand, prepare required data, and evaluate and deploy models in production.
What you will learn
Understand when to use supervised, unsupervised, or reinforcement learning algorithms
Find out how to collect and prepare your data for machine learning tasks
Tackle imbalanced data and optimize your algorithm for a bias or variance tradeoff
Apply supervised and unsupervised algorithms to overcome various machine learning challenges
Employ best practices for tuning your algorithm's hyper parameters
Discover how to use neural networks for classification and regression
Build, evaluate, and deploy your machine learning solutions to production
Who this book is for
This book is for data scientists, machine learning practitioners, and anyone who wants to learn how machine learning algorithms work and to build different machine learning models using the Python ecosystem. The book will help you take your knowledge of machine learning to the next level by grasping its ins and outs and tailoring it to your needs. Working knowledge of Python and a basic understanding of underlying mathematical and statistical concepts is required.
Описание: This volume reports on excavations in advance of the development of a site in Norton-on-Derwent, North Yorkshire close to the line of the main Roman road running from the crossing point of the River Derwent near Malton Roman fort to York. This site provided much additional information on aspects of the poorly understood `small town` of Delgovicia.
Описание: Imagine a world where you can make a computer program learn for itself? 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?
DO YOU WANT TO LEARN THE BASICS OF PYTHON PROGRAMMING QUICKLY?
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.
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 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.
Описание: This book provides the intuition behind the state of the art Deep Learning architectures such as ResNet, DenseNet, Inception, and encoder-decoder without diving deep into the math of it. It shows how you can implement and use various architectures to solve problems in the area of image classification, language translation and NLP using PyTorch.
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