Learning Solutions 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 Learning Solutions book. This book definitely worth reading, it is an incredibly well-written.
Machine Learning with Python Cookbook by Chris Albon Pdf
This practical guide provides nearly 200 self-contained recipes to help you solve machine learning challenges you may encounter in your daily work. If you’re comfortable with Python and its libraries, including pandas and scikit-learn, you’ll be able to address specific problems such as loading data, handling text or numerical data, model selection, and dimensionality reduction and many other topics. Each recipe includes code that you can copy and paste into a toy dataset to ensure that it actually works. From there, you can insert, combine, or adapt the code to help construct your application. Recipes also include a discussion that explains the solution and provides meaningful context. This cookbook takes you beyond theory and concepts by providing the nuts and bolts you need to construct working machine learning applications. You’ll find recipes for: Vectors, matrices, and arrays Handling numerical and categorical data, text, images, and dates and times Dimensionality reduction using feature extraction or feature selection Model evaluation and selection Linear and logical regression, trees and forests, and k-nearest neighbors Support vector machines (SVM), naïve Bayes, clustering, and neural networks Saving and loading trained models
Pattern Recognition and Machine Learning by Christopher M. Bishop Pdf
This is the first textbook on pattern recognition to present the Bayesian viewpoint. The book presents approximate inference algorithms that permit fast approximate answers in situations where exact answers are not feasible. It uses graphical models to describe probability distributions when no other books apply graphical models to machine learning. No previous knowledge of pattern recognition or machine learning concepts is assumed. Familiarity with multivariate calculus and basic linear algebra is required, and some experience in the use of probabilities would be helpful though not essential as the book includes a self-contained introduction to basic probability theory.
The Learning Solution is a practical guide for parents who want to improve their child’s academic situation in school. It is written by a practicing school psychologist to give parents effective strategies for making the most helpful and realistic choices for children experiencing difficulty with their schoolwork. The Learning Solution will provide parents with the skills needed to negotiate the education maze and teach them how to advocate for their child. Parents will also learn how build an effective cooperative relationship with their child at home. The Learning Solution has been updated to include a chapter on mental health issues currently impacting on children’s learning experiences in school. In addition, current information is provided on the medications used in the treatment of Attention Deficit Hyperactivity Disorder.
Practical, hands-on solutions in Python to overcome any problem in Machine Learning Key Features Master the advanced concepts, methodologies, and use cases of machine learning Build ML applications for analytics, NLP and computer vision domains Solve the most common problems in building machine learning models Book Description Machine learning (ML) helps you find hidden insights from your data without the need for explicit programming. This book is your key to solving any kind of ML problem you might come across in your job. You’ll encounter a set of simple to complex problems while building ML models, and you'll not only resolve these problems, but you’ll also learn how to build projects based on each problem, with a practical approach and easy-to-follow examples. The book includes a wide range of applications: from analytics and NLP, to computer vision domains. Some of the applications you will be working on include stock price prediction, a recommendation engine, building a chat-bot, a facial expression recognition system, and many more. The problem examples we cover include identifying the right algorithm for your dataset and use cases, creating and labeling datasets, getting enough clean data to carry out processing, identifying outliers, overftting datasets, hyperparameter tuning, and more. Here, you'll also learn to make more timely and accurate predictions. In addition, you'll deal with more advanced use cases, such as building a gaming bot, building an extractive summarization tool for medical documents, and you'll also tackle the problems faced while building an ML model. By the end of this book, you'll be able to fine-tune your models as per your needs to deliver maximum productivity. What you will learn Select the right algorithm to derive the best solution in ML domains Perform predictive analysis effciently using ML algorithms Predict stock prices using the stock index value Perform customer analytics for an e-commerce platform Build recommendation engines for various domains Build NLP applications for the health domain Build language generation applications using different NLP techniques Build computer vision applications such as facial emotion recognition Who this book is for This book is for the intermediate users such as machine learning engineers, data engineers, data scientists, and more, who want to solve simple to complex machine learning problems in their day-to-day work and build powerful and efficient machine learning models. A basic understanding of the machine learning concepts and some experience with Python programming is all you need to get started with this book.
Designing and Developing Digital and Blended Learning Solutions by Mark Loon Pdf
Designing and Developing Digital and Blended Learning Solutions is essential reading for anyone studying the Level 5 CIPD L&D module of the same name as well as all learning and development professionals looking to understand the growing role of technology in L&D. Covering both current and emerging learning technologies, this book will help readers assess which technologies are right for their needs. It also covers how to design blended learning solutions, how to develop digital learning content as well as how to evaluate the effectiveness of this digital content. Most importantly, Designing and Developing Digital and Blended Learning Solutions provides guidance on how to implement digital and blended learning solutions including identifying the appropriate platforms and the roles, tools and processes needed to support effective implementation. Full of practical examples and advice, this is an invaluable guide for students and practitioners alike.
The Machine Learning Solutions Architect Handbook by David Ping Pdf
Build highly secure and scalable machine learning platforms to support the fast-paced adoption of machine learning solutions Key Features Explore different ML tools and frameworks to solve large-scale machine learning challenges in the cloud Build an efficient data science environment for data exploration, model building, and model training Learn how to implement bias detection, privacy, and explainability in ML model development Book DescriptionWhen equipped with a highly scalable machine learning (ML) platform, organizations can quickly scale the delivery of ML products for faster business value realization. There is a huge demand for skilled ML solutions architects in different industries, and this handbook will help you master the design patterns, architectural considerations, and the latest technology insights you’ll need to become one. You’ll start by understanding ML fundamentals and how ML can be applied to solve real-world business problems. Once you've explored a few leading problem-solving ML algorithms, this book will help you tackle data management and get the most out of ML libraries such as TensorFlow and PyTorch. Using open source technology such as Kubernetes/Kubeflow to build a data science environment and ML pipelines will be covered next, before moving on to building an enterprise ML architecture using Amazon Web Services (AWS). You’ll also learn about security and governance considerations, advanced ML engineering techniques, and how to apply bias detection, explainability, and privacy in ML model development. By the end of this book, you’ll be able to design and build an ML platform to support common use cases and architecture patterns like a true professional. What you will learn Apply ML methodologies to solve business problems Design a practical enterprise ML platform architecture Implement MLOps for ML workflow automation Build an end-to-end data management architecture using AWS Train large-scale ML models and optimize model inference latency Create a business application using an AI service and a custom ML model Use AWS services to detect data and model bias and explain models Who this book is for This book is for data scientists, data engineers, cloud architects, and machine learning enthusiasts who want to become machine learning solutions architects. You’ll need basic knowledge of the Python programming language, AWS, linear algebra, probability, and networking concepts before you get started with this handbook.
Best of The eLearning Guild's Learning Solutions by Bill Brandon Pdf
Best of The eLearning Guild's Learning Solutions delivers expert content that e-Learning professionals worldwide have found to be indispensable. The book includes guidelines, checklists and instructions that will help you create solutions that result in real learning.
Optimizing AI and Machine Learning Solutions by Mirza Rahim Baig Pdf
Build high-impact ML/AI solutions by optimizing each step KEY FEATURES ● Build and fine-tune models for maximum performance. ● Practical tips to make your own state-of-the-art AI/ML models. ● ML/AI problem solving tips with multiple case studies to tackle real-world challenges. DESCRIPTION This book approaches data science solution building using a principled framework and case studies with extensive hands-on guidance. It will teach the readers optimization at each step, whether it is problem formulation or hyperparameter tuning for deep learning models. This book keeps the reader pragmatic and guides them toward practical solutions by discussing the essential ML concepts, including problem formulation, data preparation, and evaluation techniques. Further, the reader will be able to learn how to apply model optimization with advanced algorithms, hyperparameter tuning, and strategies against overfitting. They will also benefit from deep learning by optimizing models for image processing, natural language processing, and specialized applications. The reader can put theory into practice with hands-on case studies and code examples, reinforcing their understanding. With this book, the reader will be able to create high-impact, high-value ML/AI solutions by optimizing each step of the solution building process, which is the ultimate goal of every data science professional. WHAT YOU WILL LEARN ● End-to-end solutions to ML/AI problems. ● Data augmentation and transfer learning. ● Optimizing AI/ML solutions at each step of development. ● Multiple hands-on real case studies. ● Choose between various ML/AI models. WHO THIS BOOK IS FOR This book empowers data scientists, developers, and AI enthusiasts at all levels to unlock the full potential of their ML solutions. This guide equips you to become a confident AI optimization expert. TABLE OF CONTENTS 1. Optimizing a Machine Learning /Artificial Intelligence Solution 2. ML Problem Formulation: Setting the Right Objective 3. Data Collection and Pre-processing 4. Model Evaluation and Debugging 5. Imbalanced Machine Learning 6. Hyper-parameter Tuning 7. Parameter Optimization Algorithms 8. Optimizing Deep Learning Models 9. Optimizing Image Models 10. Optimizing Natural Language Processing Models 11. Transfer Learning
E-Learning Solutions on a Shoestring by Jane Bozarth Pdf
Is e-learning at your organization chronically underfunded? Discover how you can create workplace solutions with minimal budget in e-Learning Solutions on a Shoestring. Author Jane Bozarth, recognized as e-Learning Centre's October 2005 Pick of the Month, provides the nuts-and-bolts information you need to incorporate e-learning solutions at minimal cost. She offers myriad strategies for building from-scratch programs, recycling, reusing, and repurposing resources; negotiating reasonable expenses for "store bought" e-learning products; and incorporating real-world ideas for assembling tools, techniques, and strategies into workplace solutions.
Van Haren Learning Solutions by Van Haren Learning Solutions Pdf
Van Haren Publishing is listed as accredited Archimate® training course provider on the Archimate® Accreditation Register for The Archimate® Standard. Via the website vhls.global we enable you in three easy steps to also deliver Accredited Archimate® Standard training courses based on this Courseware. This material is extension courseware, this is additional supportive material aims to help provide training on the subject of the Archimate Standard, Version 3.1. We know the accreditation process well and support you with this. You don’t have to worry about the accreditation, you can become an affiliate of us and we will guide you in three easy steps through the process, including enrolment of qualified trainers. Our materials are of the highest quality includes extensive exam preparations and many other extra’s! Already, many well-respected training organizations make use of these Van Haren Publishing high quality materials and services for the delivery of The TOGAF Standard training. We have been enabling our Affiliates to grow their revenue and numbers of students. The easiness of enrollment and the many extra’s we provide creates a great competitive advantage for our partners. Contact info(at)vanharen.net if you have any questions. Van Haren Publishing is the official publisher of The Open Group, we provide as one of the only organizations all official publications. The Courseware consist out of ArchiMate® 3 Foundation and Practitioner (Level 1 & 2) and the additional ArchiMate 3.1 Publications like: - ArchiMate 3.1 Specification - ArchiMate 3.1 a pocket Guide The Archimate® Standard has been developed through the collaborative efforts of the whole community. Using The Archimate® Standard results in an Enterprise Architecture that is consistent, reflects the needs of stakeholders, employs best practice, and gives due consideration both to current requirements and the perceived future needs of the business. These courseware materials form the perfect basis for full course accreditation including the Van Haren's QMS, Courseware materials, authorized trainer(s) enrollment and exam voucher(s). Disclaimer: These courseware materials separately are not accredited and do not provide any right to use the trademark. Also these materials may not be commercially used without a Commercial License from The Open Group. When registered for a TOGAF® or Archimate® course, the exam/certification voucher is included in the training fee. Order directly or contact info(at)vanharen.net if you have any questions.
Applied Machine Learning Solutions with Python by Siddhanta Bhatta Pdf
A problem-focused guide for tackling industrial machine learning issues with methods and frameworks chosen by experts. KEY FEATURES ● Popular techniques for problem formulation, data collection, and data cleaning in machine learning. ● Comprehensive and useful machine learning tools such as MLFlow, Streamlit, and many more. ● Covers numerous machine learning libraries, including Tensorflow, FastAI, Scikit-Learn, Pandas, and Numpy. DESCRIPTION This book discusses how to apply machine learning to real-world problems by utilizing real-world data. In this book, you will investigate data sources, become acquainted with data pipelines, and practice how machine learning works through numerous examples and case studies. The book begins with high-level concepts and implementation (with code!) and progresses towards the real-world of ML systems. It briefly discusses various concepts of Statistics and Linear Algebra. You will learn how to formulate a problem, collect data, build a model, and tune it. You will learn about use cases for data analytics, computer vision, and natural language processing. You will also explore nonlinear architecture, thus enabling you to build models with multiple inputs and outputs. You will get trained on creating a machine learning profile, various machine learning libraries, Statistics, and FAST API. Throughout the book, you will use Python to experiment with machine learning libraries such as Tensorflow, Scikit-learn, Spacy, and FastAI. The book will help train our models on both Kaggle and our datasets. WHAT YOU WILL LEARN ● Construct a machine learning problem, evaluate the feasibility, and gather and clean data. ● Learn to explore data first, select, and train machine learning models. ● Fine-tune the chosen model, deploy, and monitor it in production. ● Discover popular models for data analytics, computer vision, and Natural Language Processing. ● Create a machine learning profile and contribute to the community. WHO THIS BOOK IS FOR This book caters to beginners in machine learning, software engineers, and students who want to gain a good understanding of machine learning concepts and create production-ready ML systems. This book assumes you have a beginner-level understanding of Python. TABLE OF CONTENTS 1. Introduction to Machine Learning 2. Problem Formulation in Machine Learning 3. Data Acquisition and Cleaning 4. Exploratory Data Analysis 5. Model Building and Tuning 6. Taking Our Model into Production 7. Data Analytics Use Case 8. Building a Custom Image Classifier from Scratch 9. Building a News Summarization App Using Transformers 10. Multiple Inputs and Multiple Output Models 11. Contributing to the Community 12. Creating Your Project 13. Crash Course in Numpy, Matplotlib, and Pandas 14. Crash Course in Linear Algebra and Statistics 15. Crash Course in FastAPI
Web-Based Learning Solutions for Communities of Practice: Developing Virtual Environments for Social and Pedagogical Advancement by Karacapilidis, Nikos Pdf
" "This book provides readers with an up-to-date research manual in developing innovative and effective learning systems using web-based technologies"--Provided by publisher.
Machine Learning Design Patterns by Valliappa Lakshmanan,Sara Robinson,Michael Munn Pdf
The design patterns in this book capture best practices and solutions to recurring problems in machine learning. The authors, three Google engineers, catalog proven methods to help data scientists tackle common problems throughout the ML process. These design patterns codify the experience of hundreds of experts into straightforward, approachable advice. In this book, you will find detailed explanations of 30 patterns for data and problem representation, operationalization, repeatability, reproducibility, flexibility, explainability, and fairness. Each pattern includes a description of the problem, a variety of potential solutions, and recommendations for choosing the best technique for your situation. You'll learn how to: Identify and mitigate common challenges when training, evaluating, and deploying ML models Represent data for different ML model types, including embeddings, feature crosses, and more Choose the right model type for specific problems Build a robust training loop that uses checkpoints, distribution strategy, and hyperparameter tuning Deploy scalable ML systems that you can retrain and update to reflect new data Interpret model predictions for stakeholders and ensure models are treating users fairly
