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.

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.

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.

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

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.

How to foster social and emotional learning, even when teaching remotely. The onset of the COVID-19 pandemic posed multiple dilemmas for educators; the most immediate one, when schools closed their physical doors, was how to switch nimbly from classroom instruction to emergency remote teaching. But educators also face a related, ongoing challenge: how to meet the social and emotional needs of their learners when separated by distance, whether in the middle of a traumatic event or on an unremarkable day of schooling. In this essential volume of the SEL Solutions Series, online learning expert Stephanie Louise Moore shows how teachers can seamlessly integrate effective SEL practices into their online instruction, beginning with the all-important creation of a social learning community. Strategies and resources are provided throughout to help with every step, including: understanding the individual needs of diverse distanced learners; developing students’ navigational and focusing skills in the digital learning environment; increasing the level of interaction in online lessons; building in flexibility and choice; and assessing learning in a remote context.

"This book provides fundamental research on the architecture of learning technology systems, discussing such issues as the common structures in LTS and solutions for specific forms such as knowledge-based, distributed, or adaptive applications of e-learning. Researchers, and scholars in the fields of learning content software development, computing and educational technologies, and e-learning will find it an invaluable resource"--Provided by publisher.

Looking for the best e-learning solution providers to keep an eye on in 2022? Look no further than "The 10 Most Dexterous E-Learning Solution Providers to Watch in 2022"! This comprehensive guide explores the top companies in the e-learning industry, highlighting their unique strengths and innovations that make them stand out from the rest. From interactive course content to cutting-edge virtual reality technology, these providers offer the latest and greatest in e-learning solutions. Whether you're a student, teacher, or business professional looking to enhance your skills, "The 10 Most Dexterous E-Learning Solution Providers to Watch in 2022" is the perfect resource to help you stay ahead of the curve. So what are you waiting for? Download your copy today and start exploring the future of e-learning!

This book presents a collection of results from the interdisciplinary research project “ELLI” published by researchers at RWTH Aachen University, the TU Dortmund and Ruhr-Universität Bochum between 2011 and 2016. All contributions showcase essential research results, concepts and innovative teaching methods to improve engineering education. Further, they focus on a variety of areas, including virtual and remote teaching and learning environments, student mobility, support throughout the student lifecycle, and the cultivation of interdisciplinary skills.