“…What You Will Learn Read, sort, and map various data into Python and Pandas Recognise patterns so you can understand and explore data Use statistical models to discover patterns in data Review classical statistical inference using Python, Pandas, and SciPy Detect similarities and differences in data with clustering Clean your data to make it useful Work in Jupyter Notebook to produce publication ready figures to be included in reports In Detail Python, a multi-paradigm programming language, has become the language of choice for data scientists for data analysis, visualization, and machine learning. …”

“…Covering data preparation, statistics, analytics implementation, as well as other crucial topics favored by interviewers, this book: Provides 200+ interview questions often asked by recruiters and hiring managers in global corporations Offers short and to-the-point answers to the depth required, while looking at the problem from all angles Provides a full range of interview questions for jobs ranging from junior analytics to senior data scientists and managers Offers analytics professionals a quick reference on topics in analytics Using a question-and-answer format from start to finish, Interview Questions in Business Analytics: How to Ace Interviews and Get the Job You Want will help you grasp concepts sooner and with deep clarity. …”

“…His expertise in online advertising and digital media includes work as both a data scientist and big data engineer. He has created deep learning models for prediction and has experience in recommendation systems using reinforcement learning and collaborative filtering. …”

“…His expertise in online advertising and digital media includes work as both a data scientist and big data engineer. He has created deep learning models for prediction and has experience in recommendation systems using reinforcement learning and collaborative filtering. …”

“…What you will learn Review the basics of quantum computing Gain a solid understanding of modern quantum algorithms Understand how to formulate optimization problems with QUBO Solve optimization problems with quantum annealing, QAOA, GAS, and VQE Find out how to create quantum machine learning models Explore how quantum support vector machines and quantum neural networks work using Qiskit and PennyLane Discover how to implement hybrid architectures using Qiskit and PennyLane and its PyTorch interface Who this book is for This book is for professionals from a wide variety of backgrounds, including computer scientists and programmers, engineers, physicists, chemists, and mathematicians. …”

“…What you will learn Gain a clear understanding of deepfakes and their creation Understand the risks of deepfakes and how to mitigate them Collect efficient data to create successful deepfakes Get familiar with the deepfakes workflow and its steps Explore the application of deepfakes methods to your own generative needs Improve results by augmenting data and avoiding overtraining Examine the future of deepfakes and other generative AIs Use generative AIs to increase video content resolution Who this book is for This book is for AI developers, data scientists, and anyone looking to learn more about deepfakes or techniques and technologies from Deepfakes to help them generate new image data. …”

“…What you will learn Apply CV across industries, including e-commerce, logistics, and media Build custom image classifiers with Amazon Rekognition Custom Labels Create automated end-to-end CV workflows on AWS Detect product defects on edge devices using Amazon Lookout for Vision Build, deploy, and monitor CV models using Amazon SageMaker Discover best practices for designing and evaluating CV workloads Develop an AI governance strategy across the entire machine learning life cycle Who this book is for If you are a machine learning engineer or data scientist looking to discover best practices and learn how to build comprehensive CV solutions on AWS, this book is for you. …”

“…What you will learn Excel in exploratory data analysis (EDA) for tabular, text, audio, video, and image data Understand how to use Python libraries to apply rules to label raw data Discover data augmentation techniques for adding classification labels Leverage K-means clustering to classify unsupervised data Explore how hybrid supervised learning is applied to add labels for classification Master text data classification with generative AI Detect objects and classify images with OpenCV and YOLO Uncover a range of techniques and resources for data annotation Who this book is for This book is for machine learning engineers, data scientists, and data engineers who want to learn data labeling methods and algorithms for model training. …”

“…What you will learn Build a solid foundation in key MLOps concepts and best practices Explore MLOps workflows, covering model development and training Implement complete MLOps workflows on the Red Hat OpenShift platform Build MLOps pipelines for automating model training and deployments Discover model serving approaches using Seldon and Intel OpenVino Get to grips with operating data science and machine learning workloads in OpenShift Who this book is for This book is for MLOps and DevOps engineers, data architects, and data scientists interested in learning the OpenShift platform. …”

“…What you will learn Develop a solid understanding of Auto-GPT's fundamental principles Hone your skills in creating engaging and effective prompts Effectively harness the potential of Auto-GPT's versatile plugins Tailor and personalize AI applications to meet specific requirements Proficiently manage Docker configurations for advanced setup Ensure the safe and efficient use of continuous mode Integrate your own LLM with Auto-GPT for enhanced performance Who this book is for This book is for developers, data scientists, and AI enthusiasts interested in leveraging the power of Auto-GPT and its plugins to create powerful AI applications. …”

“…What you will learn Implement a data observability approach to enhance the quality of data pipelines Collect and analyze key metrics through coding examples Apply monkey patching in a Python module Manage the costs and risks associated with your data pipeline Understand the main techniques for collecting observability metrics Implement monitoring techniques for analytics pipelines in production Build and maintain a statistics engine continuously Who this book is for This book is for data engineers, data architects, data analysts, and data scientists who have encountered issues with broken data pipelines or dashboards. …”

“…What you will learn Grasp the advantages of MXNet and Gluon libraries Build and train network models from scratch using MXNet Apply transfer learning for more complex, fine-tuned network architectures Address modern Computer Vision and NLP problems using neural network techniques Train state-of-the-art models with GPUs and leverage modern optimization techniques Improve inference run-times and deploy models in production Who this book is for This book is for data scientists, machine learning engineers, and developers who want to work with Apache MXNet for building fast and scalable deep learning solutions. …”

“…What you will learn Understand different use cases and real-world applications of data in the cloud Work with document and indexed NoSQL databases Get to grips with modeling considerations for analytics, AI, and ML Use real-world examples to learn about ETL services Design structured, semi-structured, and unstructured data for your applications and analytics Improve observability, performance, security, scalability, latency SLAs, SLIs, and SLOs Who this book is for This book is for database developers, data engineers, and architects looking to design, model, and build database applications on the cloud with an extended focus on operational consideration and taking their data to AI. Data scientists, as well ML and AI engineers who want to use Google Cloud services in the data to AI journey will also find plenty of useful information in this book. …”

“…Learn how to create interactive and visually aesthetic plots using the Bokeh package in Python About This Book A step by step approach to creating interactive plots with Bokeh Go from nstallation all the way to deploying your very own Bokeh application Work with a real time datasets to practice and create your very own plots and applications Who This Book Is For This book is well suited for data scientists and data analysts who want to perform interactive data visualization on their web browsers using Bokeh. …”

“…What you will learn Learn how to create interactive and responsive data visualizations using Chart.js Learn how to create Canvas-based graphics without Canvas programming Create composite charts and configure animated data updates and transitions Efficiently display quantitative information using bar and line charts, scatterplots, and pie charts Learn how to load, parse, and filter external files in JSON and CSV formats Understand the benefits of using a data visualization framework Who this book is for The ideal target audience of this book includes web developers and designers, data journalists, data scientists and artists who wish to create interactive data visualizations..…”

“…What you will learn Install and run the Jupyter Notebook system on your machine Implement programming languages such as R, Python, Julia, and JavaScript with the Jupyter Notebook Use interactive widgets to manipulate and visualize data in real time Start sharing your Notebook with colleagues Invite your colleagues to work with you on the same Notebook Organize your Notebook using Jupyter namespaces Access big data in Jupyter for dealing with large datasets using Spark Who this book is for Learning Jupyter 5 is for developers, data scientists, machine learning users, and anyone working on data analysis or data science projects across different teams. …”

“…What you will learn Become familiar with the basic features of the TensorFlow library Get to know Linear Regression techniques with TensorFlow Learn SVMs with hands-on recipes Implement neural networks to improve predictive modeling Apply NLP and sentiment analysis to your data Master CNN and RNN through practical recipes Implement the gradient boosted random forest to predict housing prices Take TensorFlow into production Who this book is for If you are a data scientist or a machine learning engineer with some knowledge of linear algebra, statistics, and machine learning, this book is for you. …”

“…Hands-on recipes to work with Tensorflow on desktop, mobile, and cloud environment Who This Book Is For This book is intended for data analysts, data scientists, machine learning practitioners and deep learning enthusiasts who want to perform deep learning tasks on a regular basis and are looking for a handy guide they can refer to. …”

“…Who This Book Is For This book is for programmers, scientists, and engineers who have the knowledge of Python and know the basics of data science. …”

“…Moreover, it is highly recommended for practitioners, scientists, and audio engineers who want to master coding algorithms for high-fidelity audio…”