Tensorflow Developer Certificate Exam Practice Tests 2024 Made Easy

Tensorflow Developer Certificate Exam Practice Tests 2024 Made Easy Book in PDF, ePub and Kindle version is available to download in english. Read online anytime anywhere directly from your device. Click on the download button below to get a free pdf file of Tensorflow Developer Certificate Exam Practice Tests 2024 Made Easy book. This book definitely worth reading, it is an incredibly well-written.

TensorFlow Developer Certificate Exam Practice Tests 2024 Made Easy

MR Troy
TensorFlow Developer Certificate Exam Practice Tests 2024 Made Easy Book PDF

Author : MR Troy
Publisher : MR Troy
Page : 0 pages
File Size : 46,8 Mb
Release : 2024-02-03
Category : Computers
ISBN : 9798224603350

Get Book

TensorFlow Developer Certificate Exam Practice Tests 2024 Made Easy by MR Troy Pdf

What you'll learn Participants will be thoroughly prepared for the exam with tailored practice tests that closely mimic the format and content of the actual certification exam. Upon passing the exam, students will be able to add a digital badge to their LinkedIn profiles and join the TensorFlow Certificate Network. Learners will warm up hands-on experience in TensorFlow by completing practice tests and exercises in real-world scenarios. Students will regain a deep understanding of TensorFlow fundamentals, including Linear Regression, Image Classification, NLP, and Time Series predictions. Description Welcome to "TensorFlow Developer Certificate Exam Practice Tests 2024 made easy," your efficient path to mastering TensorFlow and preparing for certification. This book will equip you with the knowledge and practical skills needed for the TensorFlow Developer Certificate Exam in a convenient format. What Makes This Book Effective? Streamlined Learning: Ideal for those with busy schedules, our focused content is structured to make the most of your time (in less than 2 hours). Hands-On Practice: Dive into practice tests across key TensorFlow areas like Linear Regression, Image Classification, NLP, and Time Series, crafted to enhance your understanding and proficiency. Insider Knowledge: Gain insights with expert tips that will help you confidently approach the exam. Flexible Learning Environment: Choose your preferred learning tool-Google Colab, Jupyter Notebooks, or PyCharm-to work through the content. Why Choose This Book? Prepare with Confidence: Our carefully designed practice tests aim to give you a solid grounding in the exam's format and content areas. Join a Community: Consider joining the TensorFlow Certificate Network to connect with other professionals upon completion. Showcase Your Skills: Learn how to add a digital badge to your LinkedIn and GitHub profiles to highlight your TensorFlow capabilities. Enroll in "TensorFlow Developer Certificate Exam Practice Tests 2024 Made Easy" and start building your practical TensorFlow skills today! Who this book is for: Aspiring or current AI and machine learning professionals aiming to gain TensorFlow certification. Individuals with basic programming knowledge and/or a foundational understanding of machine learning concepts. Developers and students looking for a comprehensive yet concise preparation for the TensorFlow Developer Certificate Exam. Anyone interested in enhancing their TensorFlow skills and adding a recognized credential to their resume or online profiles. Requirements Basic understanding of any programming language (Python preferred). It's beneficial if learners have foundational knowledge of machine learning principles.

Google Cloud Professional Cloud Developer Exam Practice Questions & Dumps

Quantic Books
Google Cloud Professional Cloud Developer Exam Practice Questions Dumps Book PDF

Author : Quantic Books
Publisher : Unknown
Page : 40 pages
File Size : 51,5 Mb
Release : 2020-09-30
Category : Electronic
ISBN : 9798692234834

Get Book

Google Cloud Professional Cloud Developer Exam Practice Questions & Dumps by Quantic Books Pdf

A Professional Cloud Developer builds scalable and highly available applications using Google-recommended practices and tools. This individual has experience with cloud-native applications, developer tools, managed services, and next-generation databases. A Professional Cloud Developer also has proficiency with at least one general-purpose programming language and is skilled at producing meaningful metrics and logs to debug and trace code.Preparing for google cloud professional cloud developer certification to become a Professional Cloud Developer Certified by Google Cloud? Here we have brought best Exam Questions for you so that you can prepare well for this Exam.Unlike other online simulation practice tests, you get a Paperback version that is easy to read & remember these questions. You can simply rely on these questions for successfully certifying this exam.

Vlocity Platform Application Developer Certification Practice Exam

MMD Trainings
Vlocity Platform Application Developer Certification Practice Exam Book PDF

Author : MMD Trainings
Publisher : Independently Published
Page : 84 pages
File Size : 44,7 Mb
Release : 2021-02-19
Category : Electronic
ISBN : 9798711344261

Get Book

Vlocity Platform Application Developer Certification Practice Exam by MMD Trainings Pdf

Enhance your certification score. Even though you're working at a top company, you'll want to keep your certification score up. It's important for your job search, especially if you want to go for a higher level position. When you're looking for new jobs, people are going to be screening your resume a bit with certifications. Understand the test structure and what to expect; then walk through each topic area, quiz yourself with practice questions and answers, and ensure you're ready to take the certification.Scenario based exam questions for Vlocity Platform Application Developer Prep Practice ExamOnline practice exam to be completed in Specified Time Duration.Practice tests are created by Subject Matter Experts. Your results are immediately available, while you stay focused on your exam results. Practice tests provides the answer to a test/questions you haven't already learned.Exam Questions similar to actual Certification Exam.Life time Access to practice tests to try as many times until you master the subject. You have access to practice test answers 24 hours a day, 365 days a year. If you're not satisfied, you can easily return to the practice test to make corrections.The practice test have been designed carefully by maintaining the exam structure, syllabus, topic weights, cut score and time duration same as actual certification exam. The aim of the practice exam is to allow the candidate to identify the risks related to a exam topics and be able to recognise them when analysing a real practice scenario.Applicants will need to make sure that they are able to complete the practice examination in its entirety and pass all the multiple choice tests in general nature.Exam Name: Vlocity Platform Application Developer Prep Practice ExamExam Duration 90minsExam Format Multiple Choice and Multi-Response QuestionsNumber of Questions 50 QuestionsEligibility/Pre-Requisite NILExam Language EnglishPass Score 70

Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow

Aurélien Géron
Hands On Machine Learning with Scikit Learn Keras and TensorFlow Book PDF

Author : Aurélien Géron
Publisher : "O'Reilly Media, Inc."
Page : 851 pages
File Size : 51,8 Mb
Release : 2019-09-05
Category : Computers
ISBN : 9781492032595

Get Book

Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow by Aurélien Géron Pdf

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 frameworks—Scikit-Learn and TensorFlow—author Aurélien Géron helps 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. 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

AI and Machine Learning for Coders

Laurence Moroney
AI and Machine Learning for Coders Book PDF

Author : Laurence Moroney
Publisher : O'Reilly Media
Page : 393 pages
File Size : 55,5 Mb
Release : 2020-10-01
Category : Computers
ISBN : 9781492078166

Get Book

AI and Machine Learning for Coders by Laurence Moroney Pdf

If you're looking to make a career move from programmer to AI specialist, this is the ideal place to start. Based on Laurence Moroney's extremely successful AI courses, this introductory book provides a hands-on, code-first approach to help you build confidence while you learn key topics. You'll understand how to implement the most common scenarios in machine learning, such as computer vision, natural language processing (NLP), and sequence modeling for web, mobile, cloud, and embedded runtimes. Most books on machine learning begin with a daunting amount of advanced math. This guide is built on practical lessons that let you work directly with the code. You'll learn: How to build models with TensorFlow using skills that employers desire The basics of machine learning by working with code samples How to implement computer vision, including feature detection in images How to use NLP to tokenize and sequence words and sentences Methods for embedding models in Android and iOS How to serve models over the web and in the cloud with TensorFlow Serving

Fluent Python

Luciano Ramalho
Fluent Python Book PDF

Author : Luciano Ramalho
Publisher : "O'Reilly Media, Inc."
Page : 1069 pages
File Size : 40,6 Mb
Release : 2015-07-30
Category : Computers
ISBN : 9781491946251

Get Book

Fluent Python by Luciano Ramalho Pdf

Python’s simplicity lets you become productive quickly, but this often means you aren’t using everything it has to offer. With this hands-on guide, you’ll learn how to write effective, idiomatic Python code by leveraging its best—and possibly most neglected—features. Author Luciano Ramalho takes you through Python’s core language features and libraries, and shows you how to make your code shorter, faster, and more readable at the same time. Many experienced programmers try to bend Python to fit patterns they learned from other languages, and never discover Python features outside of their experience. With this book, those Python programmers will thoroughly learn how to become proficient in Python 3. This book covers: Python data model: understand how special methods are the key to the consistent behavior of objects Data structures: take full advantage of built-in types, and understand the text vs bytes duality in the Unicode age Functions as objects: view Python functions as first-class objects, and understand how this affects popular design patterns Object-oriented idioms: build classes by learning about references, mutability, interfaces, operator overloading, and multiple inheritance Control flow: leverage context managers, generators, coroutines, and concurrency with the concurrent.futures and asyncio packages Metaprogramming: understand how properties, attribute descriptors, class decorators, and metaclasses work

Deep Learning for Coders with fastai and PyTorch

Jeremy Howard,Sylvain Gugger
Deep Learning for Coders with fastai and PyTorch Book PDF

Author : Jeremy Howard,Sylvain Gugger
Publisher : O'Reilly Media
Page : 624 pages
File Size : 46,9 Mb
Release : 2020-06-29
Category : Computers
ISBN : 9781492045496

Get Book

Deep Learning for Coders with fastai and PyTorch by Jeremy Howard,Sylvain Gugger Pdf

Deep learning is often viewed as the exclusive domain of math PhDs and big tech companies. But as this hands-on guide demonstrates, programmers comfortable with Python can achieve impressive results in deep learning with little math background, small amounts of data, and minimal code. How? With fastai, the first library to provide a consistent interface to the most frequently used deep learning applications. Authors Jeremy Howard and Sylvain Gugger, the creators of fastai, show you how to train a model on a wide range of tasks using fastai and PyTorch. You’ll also dive progressively further into deep learning theory to gain a complete understanding of the algorithms behind the scenes. Train models in computer vision, natural language processing, tabular data, and collaborative filtering Learn the latest deep learning techniques that matter most in practice Improve accuracy, speed, and reliability by understanding how deep learning models work Discover how to turn your models into web applications Implement deep learning algorithms from scratch Consider the ethical implications of your work Gain insight from the foreword by PyTorch cofounder, Soumith Chintala

Introducing MLOps

Mark Treveil,Nicolas Omont,Clément Stenac,Kenji Lefevre,Du Phan,Joachim Zentici,Adrien Lavoillotte,Makoto Miyazaki,Lynn Heidmann
Introducing MLOps Book PDF

Author : Mark Treveil,Nicolas Omont,Clément Stenac,Kenji Lefevre,Du Phan,Joachim Zentici,Adrien Lavoillotte,Makoto Miyazaki,Lynn Heidmann
Publisher : "O'Reilly Media, Inc."
Page : 171 pages
File Size : 41,9 Mb
Release : 2020-11-30
Category : Computers
ISBN : 9781098116422

Get Book

Introducing MLOps by Mark Treveil,Nicolas Omont,Clément Stenac,Kenji Lefevre,Du Phan,Joachim Zentici,Adrien Lavoillotte,Makoto Miyazaki,Lynn Heidmann Pdf

More than half of the analytics and machine learning (ML) models created by organizations today never make it into production. Some of the challenges and barriers to operationalization are technical, but others are organizational. Either way, the bottom line is that models not in production can't provide business impact. This book introduces the key concepts of MLOps to help data scientists and application engineers not only operationalize ML models to drive real business change but also maintain and improve those models over time. Through lessons based on numerous MLOps applications around the world, nine experts in machine learning provide insights into the five steps of the model life cycle--Build, Preproduction, Deployment, Monitoring, and Governance--uncovering how robust MLOps processes can be infused throughout. This book helps you: Fulfill data science value by reducing friction throughout ML pipelines and workflows Refine ML models through retraining, periodic tuning, and complete remodeling to ensure long-term accuracy Design the MLOps life cycle to minimize organizational risks with models that are unbiased, fair, and explainable Operationalize ML models for pipeline deployment and for external business systems that are more complex and less standardized

Deep Learning with TensorFlow

Giancarlo Zaccone,Md. Rezaul Karim,Ahmed Menshawy
Deep Learning with TensorFlow Book PDF

Author : Giancarlo Zaccone,Md. Rezaul Karim,Ahmed Menshawy
Publisher : Packt Publishing Ltd
Page : 316 pages
File Size : 52,8 Mb
Release : 2017-04-24
Category : Computers
ISBN : 9781786460127

Get Book

Deep Learning with TensorFlow by Giancarlo Zaccone,Md. Rezaul Karim,Ahmed Menshawy Pdf

Delve into neural networks, implement deep learning algorithms, and explore layers of data abstraction with the help of this comprehensive TensorFlow guide About This Book Learn how to implement advanced techniques in deep learning with Google's brainchild, TensorFlow Explore deep neural networks and layers of data abstraction with the help of this comprehensive guide Real-world contextualization through some deep learning problems concerning research and application Who This Book Is For The book is intended for a general audience of people interested in machine learning and machine intelligence. A rudimentary level of programming in one language is assumed, as is a basic familiarity with computer science techniques and technologies, including a basic awareness of computer hardware and algorithms. Some competence in mathematics is needed to the level of elementary linear algebra and calculus. What You Will Learn Learn about machine learning landscapes along with the historical development and progress of deep learning Learn about deep machine intelligence and GPU computing with the latest TensorFlow 1.x Access public datasets and utilize them using TensorFlow to load, process, and transform data Use TensorFlow on real-world datasets, including images, text, and more Learn how to evaluate the performance of your deep learning models Using deep learning for scalable object detection and mobile computing Train machines quickly to learn from data by exploring reinforcement learning techniques Explore active areas of deep learning research and applications In Detail Deep learning is the step that comes after machine learning, and has more advanced implementations. Machine learning is not just for academics anymore, but is becoming a mainstream practice through wide adoption, and deep learning has taken the front seat. As a data scientist, if you want to explore data abstraction layers, this book will be your guide. This book shows how this can be exploited in the real world with complex raw data using TensorFlow 1.x. Throughout the book, you'll learn how to implement deep learning algorithms for machine learning systems and integrate them into your product offerings, including search, image recognition, and language processing. Additionally, you'll learn how to analyze and improve the performance of deep learning models. This can be done by comparing algorithms against benchmarks, along with machine intelligence, to learn from the information and determine ideal behaviors within a specific context. After finishing the book, you will be familiar with machine learning techniques, in particular the use of TensorFlow for deep learning, and will be ready to apply your knowledge to research or commercial projects. Style and approach This step-by-step guide will explore common, and not so common, deep neural networks and show how these can be exploited in the real world with complex raw data. With the help of practical examples, you will learn how to implement different types of neural nets to build smart applications related to text, speech, and image data processing.

Deep Learning For Dummies

John Paul Mueller,Luca Massaron
Deep Learning For Dummies Book PDF

Author : John Paul Mueller,Luca Massaron
Publisher : John Wiley & Sons
Page : 370 pages
File Size : 50,6 Mb
Release : 2019-05-14
Category : Computers
ISBN : 9781119543046

Get Book

Deep Learning For Dummies by John Paul Mueller,Luca Massaron Pdf

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.

Machine Learning with TensorFlow, Second Edition

Mattmann A. Chris
Machine Learning with TensorFlow Second Edition Book PDF

Author : Mattmann A. Chris
Publisher : Manning Publications
Page : 454 pages
File Size : 50,8 Mb
Release : 2021-02-02
Category : Computers
ISBN : 9781617297717

Get Book

Machine Learning with TensorFlow, Second Edition by Mattmann A. Chris Pdf

Updated with new code, new projects, and new chapters, Machine Learning with TensorFlow, Second Edition gives readers a solid foundation in machine-learning concepts and the TensorFlow library. Summary Updated with new code, new projects, and new chapters, Machine Learning with TensorFlow, Second Edition gives readers a solid foundation in machine-learning concepts and the TensorFlow library. Written by NASA JPL Deputy CTO and Principal Data Scientist Chris Mattmann, all examples are accompanied by downloadable Jupyter Notebooks for a hands-on experience coding TensorFlow with Python. New and revised content expands coverage of core machine learning algorithms, and advancements in neural networks such as VGG-Face facial identification classifiers and deep speech classifiers. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the technology Supercharge your data analysis with machine learning! ML algorithms automatically improve as they process data, so results get better over time. You don’t have to be a mathematician to use ML: Tools like Google’s TensorFlow library help with complex calculations so you can focus on getting the answers you need. About the book Machine Learning with TensorFlow, Second Edition is a fully revised guide to building machine learning models using Python and TensorFlow. You’ll apply core ML concepts to real-world challenges, such as sentiment analysis, text classification, and image recognition. Hands-on examples illustrate neural network techniques for deep speech processing, facial identification, and auto-encoding with CIFAR-10. What's inside Machine Learning with TensorFlow Choosing the best ML approaches Visualizing algorithms with TensorBoard Sharing results with collaborators Running models in Docker About the reader Requires intermediate Python skills and knowledge of general algebraic concepts like vectors and matrices. Examples use the super-stable 1.15.x branch of TensorFlow and TensorFlow 2.x. About the author Chris Mattmann is the Division Manager of the Artificial Intelligence, Analytics, and Innovation Organization at NASA Jet Propulsion Lab. The first edition of this book was written by Nishant Shukla with Kenneth Fricklas. Table of Contents PART 1 - YOUR MACHINE-LEARNING RIG 1 A machine-learning odyssey 2 TensorFlow essentials PART 2 - CORE LEARNING ALGORITHMS 3 Linear regression and beyond 4 Using regression for call-center volume prediction 5 A gentle introduction to classification 6 Sentiment classification: Large movie-review dataset 7 Automatically clustering data 8 Inferring user activity from Android accelerometer data 9 Hidden Markov models 10 Part-of-speech tagging and word-sense disambiguation PART 3 - THE NEURAL NETWORK PARADIGM 11 A peek into autoencoders 12 Applying autoencoders: The CIFAR-10 image dataset 13 Reinforcement learning 14 Convolutional neural networks 15 Building a real-world CNN: VGG-Face ad VGG-Face Lite 16 Recurrent neural networks 17 LSTMs and automatic speech recognition 18 Sequence-to-sequence models for chatbots 19 Utility landscape

Official Google Cloud Certified Associate Cloud Engineer Study Guide

Dan Sullivan
Official Google Cloud Certified Associate Cloud Engineer Study Guide Book PDF

Author : Dan Sullivan
Publisher : John Wiley & Sons
Page : 560 pages
File Size : 44,8 Mb
Release : 2019-04-01
Category : Computers
ISBN : 9781119564188

Get Book

Official Google Cloud Certified Associate Cloud Engineer Study Guide by Dan Sullivan Pdf

The Only Official Google Cloud Study Guide The Official Google Cloud Certified Associate Cloud Engineer Study Guide, provides everything you need to prepare for this important exam and master the skills necessary to land that coveted Google Cloud Engineering certification. Beginning with a pre-book assessment quiz to evaluate what you know before you begin, each chapter features exam objectives and review questions, plus the online learning environment includes additional complete practice tests. Written by Dan Sullivan, a popular and experienced online course author for machine learning, big data, and Cloud topics, Official Google Cloud Certified Associate Cloud Engineer Study Guide is your ace in the hole for deploying and managing Google Cloud Services. • Select the right Google service from the various choices based on the application to be built • Compute with Cloud VMs and managing VMs • Plan and deploying storage • Network and configure access and security Google Cloud Platform is a leading public cloud that provides its users to many of the same software, hardware, and networking infrastructure used to power Google services. Businesses, organizations, and individuals can launch servers in minutes, store petabytes of data, and implement global virtual clouds with the Google Cloud Platform. Certified Associate Cloud Engineers have demonstrated the knowledge and skills needed to deploy and operate infrastructure, services, and networks in the Google Cloud. This exam guide is designed to help you understand the Google Cloud Platform in depth so that you can meet the needs of those operating resources in the Google Cloud.

Mathematics for Machine Learning

Marc Peter Deisenroth,A. Aldo Faisal,Cheng Soon Ong
Mathematics for Machine Learning Book PDF

Author : Marc Peter Deisenroth,A. Aldo Faisal,Cheng Soon Ong
Publisher : Cambridge University Press
Page : 391 pages
File Size : 48,9 Mb
Release : 2020-04-23
Category : Computers
ISBN : 9781108470049

Get Book

Mathematics for Machine Learning by Marc Peter Deisenroth,A. Aldo Faisal,Cheng Soon Ong Pdf

Distills key concepts from linear algebra, geometry, matrices, calculus, optimization, probability and statistics that are used in machine learning.

Machine Learning for Algorithmic Trading

Stefan Jansen
Machine Learning for Algorithmic Trading Book PDF

Author : Stefan Jansen
Publisher : Packt Publishing Ltd
Page : 822 pages
File Size : 48,7 Mb
Release : 2020-07-31
Category : Business & Economics
ISBN : 9781839216787

Get Book

Machine Learning for Algorithmic Trading by Stefan Jansen Pdf

Leverage machine learning to design and back-test automated trading strategies for real-world markets using pandas, TA-Lib, scikit-learn, LightGBM, SpaCy, Gensim, TensorFlow 2, Zipline, backtrader, Alphalens, and pyfolio. Purchase of the print or Kindle book includes a free eBook in the PDF format. Key FeaturesDesign, train, and evaluate machine learning algorithms that underpin automated trading strategiesCreate a research and strategy development process to apply predictive modeling to trading decisionsLeverage NLP and deep learning to extract tradeable signals from market and alternative dataBook Description The explosive growth of digital data has boosted the demand for expertise in trading strategies that use machine learning (ML). This revised and expanded second edition enables you to build and evaluate sophisticated supervised, unsupervised, and reinforcement learning models. This book introduces end-to-end machine learning for the trading workflow, from the idea and feature engineering to model optimization, strategy design, and backtesting. It illustrates this by using examples ranging from linear models and tree-based ensembles to deep-learning techniques from cutting edge research. This edition shows how to work with market, fundamental, and alternative data, such as tick data, minute and daily bars, SEC filings, earnings call transcripts, financial news, or satellite images to generate tradeable signals. It illustrates how to engineer financial features or alpha factors that enable an ML model to predict returns from price data for US and international stocks and ETFs. It also shows how to assess the signal content of new features using Alphalens and SHAP values and includes a new appendix with over one hundred alpha factor examples. By the end, you will be proficient in translating ML model predictions into a trading strategy that operates at daily or intraday horizons, and in evaluating its performance. What you will learnLeverage market, fundamental, and alternative text and image dataResearch and evaluate alpha factors using statistics, Alphalens, and SHAP valuesImplement machine learning techniques to solve investment and trading problemsBacktest and evaluate trading strategies based on machine learning using Zipline and BacktraderOptimize portfolio risk and performance analysis using pandas, NumPy, and pyfolioCreate a pairs trading strategy based on cointegration for US equities and ETFsTrain a gradient boosting model to predict intraday returns using AlgoSeek's high-quality trades and quotes dataWho this book is for If you are a data analyst, data scientist, Python developer, investment analyst, or portfolio manager interested in getting hands-on machine learning knowledge for trading, this book is for you. This book is for you if you want to learn how to extract value from a diverse set of data sources using machine learning to design your own systematic trading strategies. Some understanding of Python and machine learning techniques is required.

Artificial Intelligence with Python

Prateek Joshi
Artificial Intelligence with Python Book PDF

Author : Prateek Joshi
Publisher : Packt Publishing Ltd
Page : 437 pages
File Size : 42,5 Mb
Release : 2017-01-27
Category : Computers
ISBN : 9781786469670

Get Book

Artificial Intelligence with Python by Prateek Joshi Pdf

Build real-world Artificial Intelligence applications with Python to intelligently interact with the world around you About This Book Step into the amazing world of intelligent apps using this comprehensive guide Enter the world of Artificial Intelligence, explore it, and create your own applications Work through simple yet insightful examples that will get you up and running with Artificial Intelligence in no time Who This Book Is For This book is for Python developers who want to build real-world Artificial Intelligence applications. This book is friendly to Python beginners, but being familiar with Python would be useful to play around with the code. It will also be useful for experienced Python programmers who are looking to use Artificial Intelligence techniques in their existing technology stacks. What You Will Learn Realize different classification and regression techniques Understand the concept of clustering and how to use it to automatically segment data See how to build an intelligent recommender system Understand logic programming and how to use it Build automatic speech recognition systems Understand the basics of heuristic search and genetic programming Develop games using Artificial Intelligence Learn how reinforcement learning works Discover how to build intelligent applications centered on images, text, and time series data See how to use deep learning algorithms and build applications based on it In Detail Artificial Intelligence is becoming increasingly relevant in the modern world where everything is driven by technology and data. It is used extensively across many fields such as search engines, image recognition, robotics, finance, and so on. We will explore various real-world scenarios in this book and you'll learn about various algorithms that can be used to build Artificial Intelligence applications. During the course of this book, you will find out how to make informed decisions about what algorithms to use in a given context. Starting from the basics of Artificial Intelligence, you will learn how to develop various building blocks using different data mining techniques. You will see how to implement different algorithms to get the best possible results, and will understand how to apply them to real-world scenarios. If you want to add an intelligence layer to any application that's based on images, text, stock market, or some other form of data, this exciting book on Artificial Intelligence will definitely be your guide! Style and approach This highly practical book will show you how to implement Artificial Intelligence. The book provides multiple examples enabling you to create smart applications to meet the needs of your organization. In every chapter, we explain an algorithm, implement it, and then build a smart application.