Описание: This book presents a theoretical critical appraisal of the Mechanistic Theory of Human Cognition (MTHC), which is one of the most popular major theories in the contemporary field of cognitive science.
There are many topics within the scope of the secondary school mathematics curriculum that are clearly of a motivational sort, and because of lack of time they are usually not included in the teaching process. This book provides the teacher 125 individual units - ranging from grades 7 through 12 - that can be used to enhance the mathematics curriculum. Each unit presents a preassessment, instructional objectives, and a detailed description of the topic as well as teaching suggestions. Each unit has a post-assessment. This is the sort of instructional intervention that can make students love mathematics!
Автор: Dennis Sale Название: Creative Teaching ISBN: 9811012512 ISBN-13(EAN): 9789811012518 Издательство: Springer Рейтинг: Цена: 13059.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: It firstly summarises the extensive research field on human psychological functioning relating to learning and how this can be fully utilised in the design and facilitation of quality learning experiences.
Автор: Knox Jason Название: Machine Learning for Beginners ISBN: 1801200815 ISBN-13(EAN): 9781801200813 Издательство: Неизвестно Рейтинг: Цена: 4131.00 р. Наличие на складе: Нет в наличии.
Описание: Thinking about beginning a career in the field of Data Science? Do you want to understand more in depth everything that concerns Machine Learning? Or maybe you're a total newbie eager to start learning this topic from zero or so.
Machine Learning is one of the most exciting developments to come out of computer science since its founding. It's dramatically changing society all around us and the new occupation of Data Science which has arisen as a result of the development of Machine Learning has opened up a new career path that guarantees employment that is exciting, at the cutting edge, and guaranteed to be challenging.
Maybe you're aware of all the hype but you are quite sure what Machine Learning is. If that's the case you've come to the right place. This book is designed to be a beginner's introduction to the exciting world of Machine Learning and Data Science. In this book we are going to pull the curtain back and reveal the secrets and tools used in these exciting fields.
We'll begin by recounting a history of machines and how they are an extension of the human mind and also an extension of human labor. Then we will introduce you to the concept of Machine Learning and explore how it relates to Artificial Intelligence into Deep Learning. You will learn all the different ways that Machine Learning can be applied in the real world in practical circumstances. After this, we will reveal the different types of learning and training that is used in order to get computers to learn how to deal with the real world and become autonomous agents. We will teach you all about Supervised and Unsupervised Learning. You're also going to learn the concepts behind all the major algorithms that are used in Data Science and Machine Learning.
Inside you'll discover:
What Linear Regression is, and the concept of least squares;
Types of learning used to train machines to think and act autonomously;
Avoid getting lost in Decision Trees and Random Forests;
Understand Logistic Regression;
Learn how tools like Clustering are used;
Find out some of the recent applications of Machine Learning to the real world;
See how Machine Learning is being used in Social Media, Analysis, by Government and by companies like Amazon, Netflix and Google;
And much more...
So, don't waste anymore time and let's start your journey
Are you tired of taking risks, hoping things will pay off big but you are always worried about the risks? Have you been hearing about some of the buzzwords in the world of business like data science, data analysis, and machine learning, but worry they will be too hard for you to catch onto and learn more about?
Are you looking for ways to know more about your industry, what products to release, and how to gain a competitive edge overall, without all of the risks?
If this sounds like something you have dealt with, then machine learning for Python is the best option for you This guidebook is going to dive into all of the parts of this that you need to know right now Inside, we will explore what machine learning is all about, how to add it into Python, and so many of the algorithms and steps you need to really make all of this a reality for your needs.
Inside this guidebook, be prepared to take some of the basics of Python and machine learning, and turn yourself into an expert, someone who knows with certainty that all of your decisions are the right ones, and who has data and information to back them all up. Some of the different topics we will discuss in this guidebook to help make this a reality, and to ensure we can learn and make good predictions, includes:
-The basics of machine learning and artificial intelligence.
-How to work with Python and machine learning to get started with all the options that work with this topic.
-How to work with some of the different Python machine learning algorithms out there for you to choose from.
-How to work with a model of machine learning and go through the process of having your computer learn on its own.
-More examples of how to work with Python and machine learning together.
-The importance of working with neural networks and what all of this can mean to your code.
-A look at deep learning and data science that can take your machine learning to the next level.
-The steps you need to know to get started with data Preprocessing.
-A look at where machine learning and more will be able to help lead us to the future.
Working with machine learning for Python is an important topic a lot of businesses are diving into now more than ever. They see the value of working with data science, and what this process can do for them in terms of their success and their sound business decisions. When you are ready to learn how to use machine learning for Python for some of your business and data science needs, make sure to take a look at this guidebook to get started.
Are you tired of taking risks, hoping things will pay off big but you are always worried about the risks? Have you been hearing about some of the buzzwords in the world of business like data science, data analysis, and machine learning, but worry they will be too hard for you to catch onto and learn more about?
Are you looking for ways to know more about your industry, what products to release, and how to gain a competitive edge overall, without all of the risks?
If this sounds like something you have dealt with, then machine learning for Python is the best option for you This guidebook is going to dive into all of the parts of this that you need to know right now Inside, we will explore what machine learning is all about, how to add it into Python, and so many of the algorithms and steps you need to really make all of this a reality for your needs.
Inside this guidebook, be prepared to take some of the basics of Python and machine learning, and turn yourself into an expert, someone who knows with certainty that all of your decisions are the right ones, and who has data and information to back them all up. Some of the different topics we will discuss in this guidebook to help make this a reality, and to ensure we can learn and make good predictions, includes:
-The basics of machine learning and artificial intelligence.
-How to work with Python and machine learning to get started with all the options that work with this topic.
-How to work with some of the different Python machine learning algorithms out there for you to choose from.
-How to work with a model of machine learning and go through the process of having your computer learn on its own.
-More examples of how to work with Python and machine learning together.
-The importance of working with neural networks and what all of this can mean to your code.
-A look at deep learning and data science that can take your machine learning to the next level.
-The steps you need to know to get started with data Preprocessing.
-A look at where machine learning and more will be able to help lead us to the future.
Working with machine learning for Python is an important topic a lot of businesses are diving into now more than ever. They see the value of working with data science, and what this process can do for them in terms of their success and their sound business decisions. When you are ready to learn how to use machine learning for Python for some of your business and data science needs, make sure to take a look at this guidebook to get started
Описание: If you are tired of reading without understanding and want to learn the value of big data and artificial intelligence simply and quickly, then keep reading...
Today 91% of Python programmers are not quite prepared. This is what the IT companies say, according to a recent survey. Do you know why? The reason is that they have no notions of artificial intelligence, so future programmers will no longer find work without having knowledge of this particular field that is constantly evolving. Artificial intelligence has made great strides over the years, and by 2024 it is estimated that 77% of programmers will have to be experts in this field to implement it in the various programming languages. So if you are such a programmer or aspirant and ignore the importance that Python data analysis and artificial intelligence have in our future, then you will be cut off from the business world. The solution? You need to learn these things and, above all, do it in a clear, simple, and practical way. The goal of Python for Data Science is to give you an advanced level training on Python, artificial intelligence, and deep machine learning as quickly as possible.What are some points you will learn in this book?- Artificial Intelligence: How Does it Work? How is it Used?- The Key Elements of Machine Learning- Machine Learning vs Deep Learning- Data Science vs Business Intelligence- The Data Science Lifecycle- The Value of Big Data Explained to a Child- 4 Tips for Data Cleaning and Organizing Your Data- Python Data Analysis 360 - 6 Different Machine Learning Algorithms- How to Handle Data VisualizationsPython for Data Science is perfect for those who already look to the future and want to ensure a job for the next 20 years by beating the competition, even if you know nothing about computer codes and you have never turned on a computer in your life. Would You Like to Know More? Buy now to find out about Python for Data Science.
Описание: This book reports on a study on physics problem solving in real classrooms situations.Problem solving plays a pivotal role in the physics curriculum at all levels. However, physics students’ performance in problem solving all too often remains limited to basic routine problems, with evidence of poor performance in solving problems that go beyond equation retrieval and substitution. Adopting an action research methodology, the study bridges the `research-practical divide? by explicitly teaching physics problem-solving strategies through collaborative group problem-solving sessions embedded within the curriculum. Data were collected using external assessments and video recordings of individual and collaborative group problem-solving sessions by 16-18 year-olds. The analysis revealed a positive shift in the students’ problem-solving patterns, both at group and individual level. Students demonstrated a deliberate, well-planned deployment of the taught strategies. The marked positive shifts in collaborative competences, cognitive competences, metacognitive processing and increased self-efficacy are positively correlated with attainment in problem solving in physics. However, this shift proved to be due to different mechanisms triggered in the different students.
Описание: Addresses the complex realm of machine learning and its applications for solving various real-world problems in a variety of disciplines, such as manufacturing, business, information retrieval, and security. This premier reference source is essential for professors, researchers, and students in artificial intelligence as well as computer science and engineering.
Автор: Carpenter Thomas P., Fennema Elizabeth, Romberg Thomas a. Название: Rational Numbers: An Integration of Research ISBN: 1138984531 ISBN-13(EAN): 9781138984530 Издательство: Taylor&Francis Рейтинг: Цена: 7501.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание:
Until recently there had been relatively little integration of programs of research on teaching, learning, curriculum, and assessment. However, in the last few years it has become increasingly apparent that a more unified program of research is needed to acquire an understanding of teaching and learning in schools that will inform curriculum development and assessment. The chapters in this volume represent a first step toward an integration of research paradigms in one clearly specified mathematical domain.
Integrating a number of different research perspectives is a complex task, and ways must be found to reduce the complexity without sacrificing the integration. The research discussed in this volume is tied together because it deals with a common content strand. During the last ten years specific content domains have served as focal points for research on the development of mathematical concepts in children. The areas of addition and subtraction, algebra, rational numbers, and geometry are notable examples. Whether a similar organizational structure will prevail for programs of research that integrate the study of teaching, learning, curriculum, and assessment is an open question. The perspectives presented in this volume illustrate the potential for adopting this perspective.
This unique compendium presents an introduction to problem solving, information theory, statistical machine learning, stochastic methods and quantum computation. It indicates how to apply quantum computation to problem solving, machine learning and quantum-like models to decision making -- the core disciplines of artificial intelligence.
Most of the chapters were rewritten and extensive new materials were updated. New topics include quantum machine learning, quantum-like Bayesian networks and mind in Everett many-worlds
Do you Want to learn more about Python Programming, Machine Learning and Artificial Intelligence ?.... then read on.
Python is a powerful programming language that can be used for the development of various types of applications. It is an Object-Oriented Programming language and it is interpreted rather than being compiled.
Python is considered to be among the most beloved programming languages in any circle of programmers. Software engineers, hackers, and Data Scientists alike are in love with the versatility that Python has to offer. Besides, the Object-Oriented feature of Python coupled with its flexibility is also some of the major attractions for this language. Programmers are now developing a wide range of mobile as well as web applications that we enjoy on an everyday basis.
Python Programming Crash Course doesn't make any assumptions about your background or knowledge of Python or computer programming. You need no prior knowledge to benefit from this book. You will be guided step by step using a logical and systematic approach. As new concepts, commands, or jargon are encountered they are explained in plain language, making it easy for anyone to understand.
In this Book you will learning:
Introduction ito iPython
Variables
Operators
Loops
Functions
Object-Oriented iProgramming-OOP
Modules
File ihandling
Would you like to know more?
Download the Book, Python Programming Crash Course .Scroll to the top of the page and click the "Buy now" button to get your copy now.
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