Forget far-away dreams of the future. Artificial intelligence is here now! 

Every time you use a smart device or some sort of slick technology—be it a smartwatch, smart speaker, security alarm, or even customer service chat box—you’re engaging with artificial intelligence (AI). If you’re curious about how AI is developed—or question whether AI is real—Artificial Intelligence For Dummies holds the answers you’re looking for. Starting with a basic definition of AI and explanations of data use, algorithms, special hardware, and more, this reference simplifies this complex topic for anyone who wants to understand what operates the devices we can’t live without.  

This book will help you: 

• Separate the reality of artificial intelligence from the hype 
• Know what artificial intelligence can accomplish and what its limits are 
• Understand how AI speeds up data gathering and analysis to help you make informed decisions more quickly 
• See how AI is being used in hardware applications like drones, robots, and vehicles 
• Know where AI could be used in space, medicine, and communication fields sooner than you think 

Almost 80 percent of the devices you interact with every day depend on some sort of AI. And although you don’t need to understand AI to operate your smart speaker or interact with a bot, you’ll feel a little smarter—dare we say more intelligent—when you know what’s going on behind the scenes.  So don’t wait. Pick up this popular guide to unlock the secrets of AI today! 

Move up the Kaggle leaderboards and supercharge your data science and machine learning career by analyzing famous competitions and working through exercises.

Purchase of the print or Kindle book includes a free eBook in PDF format.

Key Features

• Challenge yourself to start thinking like a Kaggle Grandmaster
• Fill your portfolio with impressive case studies that will come in handy during interviews
• Packed with exercises and notes pages for you to enhance your skills and record key findings

Book Description

More than 80,000 Kaggle novices currently participate in Kaggle competitions. To help them navigate the often-overwhelming world of Kaggle, two Grandmasters put their heads together to write The Kaggle Book, which made plenty of waves in the community. Now, they've come back with an even more practical approach based on hands-on exercises that can help you start thinking like an experienced data scientist.

In this book, you'll get up close and personal with four extensive case studies based on past Kaggle competitions. You'll learn how bright minds predicted which drivers would likely avoid filing insurance claims in Brazil and see how expert Kagglers used gradient-boosting methods to model Walmart unit sales time-series data. Get into computer vision by discovering different solutions for identifying the type of disease present on cassava leaves. And see how the Kaggle community created predictive algorithms to solve the natural language processing problem of subjective question-answering.

You can use this workbook as a supplement alongside The Kaggle Book or on its own alongside resources available on the Kaggle website and other online communities. Whatever path you choose, this workbook will help make you a formidable Kaggle competitor.

What you will learn

• Take your modeling to the next level by analyzing different case studies
• Boost your data science skillset with a curated selection of exercises
• Combine different methods to create better solutions
• Get a deeper insight into NLP and how it can help you solve unlikely challenges
• Sharpen your knowledge of time-series forecasting
• Challenge yourself to become a better data scientist

Who this book is for

If you're new to Kaggle and want to sink your teeth into practical exercises, start with The Kaggle Book, first. A basic understanding of the Kaggle platform, along with knowledge of machine learning and data science is a prerequisite.

This book is suitable for anyone starting their Kaggle journey or veterans trying to get better at it. Data analysts/scientists who want to do better in Kaggle competitions and secure jobs with tech giants will find this book helpful.

Table of Contents

1. The Most Renowned Tabular Competition – Porto Seguro's Safe Driver Prediction
2. The Makridakis Competitions – M5 on Kaggle for Accuracy and Uncertainty
3. Vision Competition: Cassava Leaf Disease Competition
4. NLP Competition – Google Quest Q&A Labeling

Get a step ahead of your competitors with insights from over 30 Kaggle Masters and Grandmasters. Discover tips, tricks, and best practices for competing effectively on Kaggle and becoming a better data scientist.

Purchase of the print or Kindle book includes a free eBook in the PDF format.

Key Features

• Learn how Kaggle works and how to make the most of competitions from over 30 expert Kagglers
• Sharpen your modeling skills with ensembling, feature engineering, adversarial validation and AutoML
• A concise collection of smart data handling techniques for modeling and parameter tuning

Book Description

Millions of data enthusiasts from around the world compete on Kaggle, the most famous data science competition platform of them all. Participating in Kaggle competitions is a surefire way to improve your data analysis skills, network with an amazing community of data scientists, and gain valuable experience to help grow your career.

The first book of its kind, The Kaggle Book assembles in one place the techniques and skills you'll need for success in competitions, data science projects, and beyond. Two Kaggle Grandmasters walk you through modeling strategies you won't easily find elsewhere, and the knowledge they've accumulated along the way. As well as Kaggle-specific tips, you'll learn more general techniques for approaching tasks based on image, tabular, textual data, and reinforcement learning. You'll design better validation schemes and work more comfortably with different evaluation metrics.

Whether you want to climb the ranks of Kaggle, build some more data science skills, or improve the accuracy of your existing models, this book is for you.

Plus, join our Discord Community to learn along with more than 1,000 members and meet like-minded people!

What you will learn

• Get acquainted with Kaggle as a competition platform
• Make the most of Kaggle Notebooks, Datasets, and Discussion forums
• Create a portfolio of projects and ideas to get further in your career
• Design k-fold and probabilistic validation schemes
• Get to grips with common and never-before-seen evaluation metrics
• Understand binary and multi-class classification and object detection
• Approach NLP and time series tasks more effectively
• Handle simulation and optimization competitions on Kaggle

Who this book is for

This book is suitable for anyone new to Kaggle, veteran users, and anyone in between. Data analysts/scientists who are trying to do better in Kaggle competitions and secure jobs with tech giants will find this book useful.

A basic understanding of machine learning concepts will help you make the most of this book.

Table of Contents

1. Introducing Kaggle and Other Data Science Competitions
2. Organizing Data with Datasets
3. Working and Learning with Kaggle Notebooks
4. Leveraging Discussion Forums
5. Competition Tasks and Metrics
6. Designing Good Validation
7. Modeling for Tabular Competitions
8. Hyperparameter Optimization
9. Ensembling with Blending and Stacking Solutions
10. Modeling for Computer Vision
11. Modeling for NLP
12. Simulation and Optimization Competitions
13. Creating Your Portfolio of Projects and Ideas
14. Finding New Professional Opportun

The go-to guide for learning coding from the ground-up

Adding some coding know-how to your skills can help launch a new career or bolster an old one. Coding All-in-One For Dummies offers an ideal starting place for learning the languages that make technology go. This edition gets you started with a helpful explanation of how coding works and how it’s applied in the real-world before setting you on a path toward writing code for web building, mobile application development, and data analysis. Add coding to your skillset for your existing career, or begin the exciting transition into life as a professional developer—Dummies makes it easy.

• Learn coding basics and how to apply them
• Analyze data and automate routine tasks on the job
• Get the foundation you need to launch a career as a coder
• Add HTML, JavaScript, and Python know-how to your resume

This book serves up insight on the basics of coding, designed to be easy to follow, even if you’ve never written a line of code in your life. You can do this.

Your comprehensive entry-level guide to machine learning

While machine learning expertise doesn’t quite mean you can create your own Turing Test-proof android—as in the movie Ex Machina—it is a form of artificial intelligence and one of the most exciting technological means of identifying opportunities and solving problems fast and on a large scale. Anyone who masters the principles of machine learning is mastering a big part of our tech future and opening up incredible new directions in careers that include fraud detection, optimizing search results, serving real-time ads, credit-scoring, building accurate and sophisticated pricing models—and way, way more.

Unlike most machine learning books, the fully updated 2nd Edition of Machine Learning For Dummies doesn't assume you have years of experience using programming languages such as Python (R source is also included in a downloadable form with comments and explanations), but lets you in on the ground floor, covering the entry-level materials that will get you up and running building models you need to perform practical tasks. It takes a look at the underlying—and fascinating—math principles that power machine learning but also shows that you don't need to be a math whiz to build fun new tools and apply them to your work and study.

• Understand the history of AI and machine learning
• Work with Python 3.8 and TensorFlow 2.x (and R as a download)
• Build and test your own models
• Use the latest datasets, rather than the worn out data found in other books
• Apply machine learning to real problems

Whether you want to learn for college or to enhance your business or career performance, this friendly beginner's guide is your best introduction to machine learning, allowing you to become quickly confident using this amazing and fast-developing technology that's impacting lives for the better all over the world.

Take a deep dive into deep learning 

Deep learning provides the means for discerning patterns in the data that drive online business and social media outlets. Deep Learning for Dummies gives you the information you need to take the mystery out of the topic—and all of the underlying technologies associated with it.    

In no time, you’ll make sense of those increasingly confusing algorithms, and find a simple and safe environment to experiment with deep learning. The book develops a sense of precisely what deep learning can do at a high level and then provides examples of the major deep learning application types.

• Includes sample code
• Provides real-world examples within the approachable text
• Offers hands-on activities to make learning easier
• Shows you how to use Deep Learning more effectively with the right tools

This book is perfect for those who want to better understand the basis of the underlying technologies that we use each and every day.  

The fast and easy way to learn Python programming and statistics

Python is a general-purpose programming language created in the late 1980s—and named after Monty Python—that's used by thousands of people to do things from testing microchips at Intel, to powering Instagram, to building video games with the PyGame library. 

Python For Data Science For Dummies is written for people who are new to data analysis, and discusses the basics of Python data analysis programming and statistics. The book also discusses Google Colab, which makes it possible to write Python code in the cloud.

• Get started with data science and Python
• Visualize information
• Wrangle data
• Learn from data

The book provides the statistical background needed to get started in data science programming, including probability, random distributions, hypothesis testing, confidence intervals, and building regression models for prediction.

Your secret weapon to understanding—and using!—one of the most powerful influences in the world today

From your Facebook News Feed to your most recent insurance premiums—even making toast!—algorithms play a role in virtually everything that happens in modern society and in your personal life. And while they can seem complicated from a distance, the reality is that, with a little help, anyone can understand—and even use—these powerful problem-solving tools!

In Algorithms For Dummies, you'll discover the basics of algorithms, including what they are, how they work, where you can find them (spoiler alert: everywhere!), who invented the most important ones in use today (a Greek philosopher is involved), and how to create them yourself.

You'll also find:

• Dozens of graphs and charts that help you understand the inner workings of algorithms
• Links to an online repository called GitHub for constant access to updated code
• Step-by-step instructions on how to use Google Colaboratory, a zero-setup coding environment that runs right from your browser

Whether you're a curious internet user wondering how Google seems to always know the right answer to your question or a beginning computer science student looking for a head start on your next class, Algorithms For Dummies is the can't-miss resource you've been waiting for.

Learn to solve challenging data science problems by building powerful machine learning models using Python

About This Book

• Understand which algorithms to use in a given context with the help of this exciting recipe-based guide
• This practical tutorial tackles real-world computing problems through a rigorous and effective approach
• Build state-of-the-art models and develop personalized recommendations to perform machine learning at scale

Who This Book Is For

This Learning Path is for Python programmers who are looking to use machine learning algorithms to create real-world applications. It is ideal for Python professionals who want to work with large and complex datasets and Python developers and analysts or data scientists who are looking to add to their existing skills by accessing some of the most powerful recent trends in data science. Experience with Python, Jupyter Notebooks, and command-line execution together with a good level of mathematical knowledge to understand the concepts is expected. Machine learning basic knowledge is also expected.

What You Will Learn

• Use predictive modeling and apply it to real-world problems
• Understand how to perform market segmentation using unsupervised learning
• Apply your new-found skills to solve real problems, through clearly-explained code for every technique and test
• Compete with top data scientists by gaining a practical and theoretical understanding of cutting-edge deep learning algorithms
• Increase predictive accuracy with deep learning and scalable data-handling techniques
• Work with modern state-of-the-art large-scale machine learning techniques
• Learn to use Python code to implement a range of machine learning algorithms and techniques

In Detail

Machine learning is increasingly spreading in the modern data-driven world. It is used extensively across many fields such as search engines, robotics, self-driving cars, and more. Machine learning is transforming the way we understand and interact with the world around us.

In the first module, Python Machine Learning Cookbook, you will learn how to perform various machine learning tasks using a wide variety of machine learning algorithms to solve real-world problems and use Python to implement these algorithms.

The second module, Advanced Machine Learning with Python, is designed to take you on a guided tour of the most relevant and powerful machine learning techniques and you'll acquire a broad set of powerful skills in the area of feature selection and feature engineering.

The third module in this learning path, Large Scale Machine Learning with Python, dives into scalable machine learning and the three forms of scalability. It covers the most effective machine learning techniques on a map reduce framework in Hadoop and Spark in Python.

This Learning Path will teach you Python machine learning for the real world. The machine learning techniques covered in this Learning Path are at the forefront of commercial practice.

This Learning Path combines some of the best that Packt has to offer in one complete, curated package. It includes content from the following Packt products:

• Python Machine Learning Cookbook by Prateek Joshi
• Advanced Machine Learning with Python by John Hearty
• Large Scale Machine Learning with Python by Bastiaan Sjardin, Alberto Boschetti, Luca Massaron

Style and approach

This course is a smooth learning path that will teach you how to get st

Découvrez l'univers des algorithmes présents dans tous les systèmes informatiques d'aujourd'hui
De nos jours tous les programmes informatiques comme par exemple ceux qui utilisent la compression de données ou les moteurs de recherche utilisent des algorithmes. Un algorithme permet de faire un choix dans un problème qui lui est présenté, et plus l'algorithme est puissant, plus le choix est rapide et bon.
Le but de ce livre est d'expliquer comment fonctionnent les algorithmes et comment on peut les tester et les mettre en oeuvre. Vous verrez également comment modéliser un problème de façon à ce qu'il puisse être résolu par un ordinateur. Les algortihmes sont également la pièce maitresses des systèmes de Big Data.
Ce livre s'adresse à toux ceux, étudiants, managers ouanalystes de données qui ont besoin des algorithmes dans la gestion des données qu'ils manipulent.

Comprehensive recipes to give you valuable insights on Transformers, Reinforcement Learning, and more

Key Features

• Deep Learning solutions from Kaggle Masters and Google Developer Experts
• Get to grips with the fundamentals including variables, matrices, and data sources
• Learn advanced techniques to make your algorithms faster and more accurate

Book Description

The independent recipes in Machine Learning Using TensorFlow Cookbook will teach you how to perform complex data computations and gain valuable insights into your data. Dive into recipes on training models, model evaluation, sentiment analysis, regression analysis, artificial neural networks, and deep learning - each using Google’s machine learning library, TensorFlow.

This cookbook covers the fundamentals of the TensorFlow library, including variables, matrices, and various data sources. You’ll discover real-world implementations of Keras and TensorFlow and learn how to use estimators to train linear models and boosted trees, both for classification and regression.

Explore the practical applications of a variety of deep learning architectures, such as recurrent neural networks and Transformers, and see how they can be used to solve computer vision and natural language processing (NLP) problems.

With the help of this book, you will be proficient in using TensorFlow, understand deep learning from the basics, and be able to implement machine learning algorithms in real-world scenarios.

What you will learn

• Take TensorFlow into production
• Implement and fine-tune Transformer models for various NLP tasks
• Apply reinforcement learning algorithms using the TF-Agents framework
• Understand linear regression techniques and use Estimators to train linear models
• Execute neural networks and improve predictions on tabular data
• Master convolutional neural networks and recurrent neural networks through practical recipes

Who this book is for

If you are a data scientist or a machine learning engineer, and you want to skip detailed theoretical explanations in favor of building production-ready machine learning models using TensorFlow, this book is for you.

Basic familiarity with Python, linear algebra, statistics, and machine learning is necessary to make the most out of this book.

Table of Contents

1. Getting Started with TensorFlow 2.x
2. The TensorFlow Way
3. Keras
4. Linear Regression
5. Boosted Trees
6. Neural Networks
7. Predicting with Tabular Data
8. Convolutional Neural Networks
9. Recurrent Neural Networks
10. Transformers
11. Reinforcement Learning with TensorFlow and TF-Agents
12. Taking TensorFlow to Production

Plongez au coeur de l'intelligence arficielle et de la data science
Vous aussi participez à la révolution qui ramène l'intelligence artificielle au coeur de notre société, grace à la data scince et au machine learning.
La data science consiste à traduire des problèmes de toute autre nature, en problèmes de modélisation quantitative, résolus par des algorithmes de traitement.
Ce livre va vous faire découvrir tous les ingrédients qui font du machine learning l'outil indispensable du développement d'applications liées à l'intelligence artificielle.



Au programme de ce livre :


Découvrez toutes les applications du quotidien qui utilisent le machine learning


Apprenez les langages du machine learning : Python et R, afin de vous adresser aux machines qui effectueront des traitements sur les données


Apprenez à coder en R avec R studio


Apprenez à coder en Python en utilisant Anaconda