Best data science machine learning
If you looking for data science machine learning then you are right place. We are searching for the best data science machine learning on the market and analyze these products to provide you the best choice.
1. Data Science from Scratch: First Principles with Python
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Oreilly Associates IncDescription
Data science libraries, frameworks, modules, and toolkits are great for doing data science, but theyre also a good way to dive into the discipline without actually understanding data science. In this book, youll learn how many of the most fundamental data science tools and algorithms work by implementing them from scratch.
If you have an aptitude for mathematics and some programming skills, author Joel Grus will help you get comfortable with the math and statistics at the core of data science, and with hacking skills you need to get started as a data scientist. Todays messy glut of data holds answers to questions no ones even thought to ask. This book provides you with the know-how to dig those answers out.
- Get a crash course in Python
- Learn the basics of linear algebra, statistics, and probabilityand understand how and when they're used in data science
- Collect, explore, clean, munge, and manipulate data
- Dive into the fundamentals of machine learning
- Implement models such as k-nearest Neighbors, Naive Bayes, linear and logistic regression, decision trees, neural networks, and clustering
- Explore recommender systems, natural language processing, network analysis, MapReduce, and databases
2. Hands-On Machine Learning with Scikit-Learn and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems
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O Reilly MediaDescription
Graphics in this book are printed in black and white.
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. This practical book shows you how.
By using concrete examples, minimal theory, and two production-ready Python frameworksscikit-learn and TensorFlowauthor Aurlien Gron helps you gain an intuitive understanding of the concepts and tools for building intelligent systems. Youll 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 youve learned, all you need is programming experience to get started.
- Explore the machine learning landscape, particularly neural nets
- Use scikit-learn to track an example machine-learning project end-to-end
- Explore several training models, including support vector machines, decision trees, random forests, and ensemble methods
- Use the TensorFlow library to build and train neural nets
- Dive into neural net architectures, including convolutional nets, recurrent nets, and deep reinforcement learning
- Learn techniques for training and scaling deep neural nets
- Apply practical code examples without acquiring excessive machine learning theory or algorithm details
3. Practical Statistics for Data Scientists: 50 Essential Concepts
Description
Statistical methods are a key part of of data science, yet very few data scientists have any formal statistics training. Courses and books on basic statistics rarely cover the topic from a data science perspective. This practical guide explains how to apply various statistical methods to data science, tells you how to avoid their misuse, and gives you advice on what's important and what's not.
Many data science resources incorporate statistical methods but lack a deeper statistical perspective. If youre familiar with the R programming language, and have some exposure to statistics, this quick reference bridges the gap in an accessible, readable format.
With this book, youll learn:
- Why exploratory data analysis is a key preliminary step in data science
- How random sampling can reduce bias and yield a higher quality dataset, even with big data
- How the principles of experimental design yield definitive answers to questions
- How to use regression to estimate outcomes and detect anomalies
- Key classification techniques for predicting which categories a record belongs to
- Statistical machine learning methods that learn from data
- Unsupervised learning methods for extracting meaning from unlabeled data
4. Malware Data Science: Attack Detection and Attribution
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Malware Data Science explains how to identify, analyze, and classify large-scale malware using machine learning and data visualization.Security has become a "big data" problem. The growth rate of malware has accelerated to tens of millions of new files per year while our networks generate an ever-larger flood of security-relevant data each day. In order to defend against these advanced attacks, you'll need to know how to think like a data scientist.
In Malware Data Science, security data scientist Joshua Saxe introduces machine learning, statistics, social network analysis, and data visualization, and shows you how to apply these methods to malware detection and analysis.
You'll learn how to:
- Analyze malware using static analysis
- Observe malware behavior using dynamic analysis
- Identify adversary groups through shared code analysis
- Catch 0-day vulnerabilities by building your own machine learning detector
- Measure malware detector accuracy
- Identify malware campaigns, trends, and relationships through data visualization
Whether you're a malware analyst looking to add skills to your existing arsenal, or a data scientist interested in attack detection and threat intelligence, Malware Data Science will help you stay ahead of the curve.
5. R for Data Science: Import, Tidy, Transform, Visualize, and Model Data
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O Reilly MediaDescription
Learn how to use R to turn raw data into insight, knowledge, and understanding. This book introduces you to R, RStudio, and the tidyverse, a collection of R packages designed to work together to make data science fast, fluent, and fun. Suitable for readers with no previous programming experience, R for Data Science is designed to get you doing data science as quickly as possible.
Authors Hadley Wickham and Garrett Grolemund guide you through the steps of importing, wrangling, exploring, and modeling your data and communicating the results. Youll get a complete, big-picture understanding of the data science cycle, along with basic tools you need to manage the details. Each section of the book is paired with exercises to help you practice what youve learned along the way.
Youll learn how to:
- Wrangletransform your datasets into a form convenient for analysis
- Programlearn powerful R tools for solving data problems with greater clarity and ease
- Exploreexamine your data, generate hypotheses, and quickly test them
- Modelprovide a low-dimensional summary that captures true "signals" in your dataset
- Communicatelearn R Markdown for integrating prose, code, and results
6. Python Machine Learning: Machine Learning and Deep Learning with Python, scikit-learn, and TensorFlow, 2nd Edition
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Key Features
- Second edition of the bestselling book on Machine Learning
- A practical approach to key frameworks in data science, machine learning, and deep learning
- Use the most powerful Python libraries to implement machine learning and deep learning
- Get to know the best practices to improve and optimize your machine learning systems and algorithms
Book Description
Machine learning is eating the software world, and now deep learning is extending machine learning. Understand and work at the cutting edge of machine learning, neural networks, and deep learning with this second edition of Sebastian Raschka's bestselling book, Python Machine Learning. Thoroughly updated using the latest Python open source libraries, this book offers the practical knowledge and techniques you need to create and contribute to machine learning, deep learning, and modern data analysis.
Fully extended and modernized, Python Machine Learning Second Edition now includes the popular TensorFlow deep learning library. The scikit-learn code has also been fully updated to include recent improvements and additions to this versatile machine learning library.
Sebastian Raschka and Vahid Mirjalili's unique insight and expertise introduce you to machine learning and deep learning algorithms from scratch, and show you how to apply them to practical industry challenges using realistic and interesting examples. By the end of the book, you'll be ready to meet the new data analysis opportunities in today's world.
If you've read the first edition of this book, you'll be delighted to find a new balance of classical ideas and modern insights into machine learning. Every chapter has been critically updated, and there are new chapters on key technologies. You'll be able to learn and work with TensorFlow more deeply than ever before, and get essential coverage of the Keras neural network library, along with the most recent updates to scikit-learn.
What you will learn
- Understand the key frameworks in data science, machine learning, and deep learning
- Harness the power of the latest Python open source libraries in machine learning
- Explore machine learning techniques using challenging real-world data
- Master deep neural network implementation using the TensorFlow library
- Learn the mechanics of classification algorithms to implement the best tool for the job
- Predict continuous target outcomes using regression analysis
- Uncover hidden patterns and structures in data with clustering
- Delve deeper into textual and social media data using sentiment analysis
Table of Contents
- Giving Computers the Ability to Learn from Data
- Training Simple Machine Learning Algorithms for Classification
- A Tour of Machine Learning Classifiers Using Scikit-Learn
- Building Good Training Sets - Data Preprocessing
- Compressing Data via Dimensionality Reduction
- Learning Best Practices for Model Evaluation and Hyperparameter Tuning
- Combining Different Models for Ensemble Learning
- Applying Machine Learning to Sentiment Analysis
- Embedding a Machine Learning Model into a Web Application
- Predicting Continuous Target Variables with Regression Analysis
- Working with Unlabeled Data - Clustering Analysis
- Implementing a Multilayer Artificial Neural Network from Scratch
- Parallelizing Neural Network Training with TensorFlow
- Going Deeper - The Mechanics of TensorFlow
- Classifying Images with Deep Convolutional Neural Networks
- Modeling Sequential Data using Recurrent Neural Networks
7. Introduction to Machine Learning with Python: A Guide for Data Scientists
Description
Machine learning has become an integral part of many commercial applications and research projects, but this field is not exclusive to large companies with extensive research teams. If you use Python, even as a beginner, this book will teach you practical ways to build your own machine learning solutions. With all the data available today, machine learning applications are limited only by your imagination.
Youll learn the steps necessary to create a successful machine-learning application with Python and the scikit-learn library. Authors Andreas Mller and Sarah Guido focus on the practical aspects of using machine learning algorithms, rather than the math behind them. Familiarity with the NumPy and matplotlib libraries will help you get even more from this book.
With this book, youll learn:
- Fundamental concepts and applications of machine learning
- Advantages and shortcomings of widely used machine learning algorithms
- How to represent data processed by machine learning, including which data aspects to focus on
- Advanced methods for model evaluation and parameter tuning
- The concept of pipelines for chaining models and encapsulating your workflow
- Methods for working with text data, including text-specific processing techniques
- Suggestions for improving your machine learning and data science skills
8. Math for Machine Learning: Open Doors to Data Science and Artificial Intelligence
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