Tag: <span>learn science</span>

01 Jul

Speed Tufting Is Both An Art and a Science – Book Review
Posted in Education by anshbookstore on July 1, 2023 @ 8:52 am

Many people are quilters, and it takes a long time to learn all the different kinds of stitches, and how to put together complex patterns. People make all kinds of things out of quilting material. Many people are quite accomplished artists in this medium. Still, making a really nice rug for the floor or for a wall can be much more difficult until you learn how to do it correctly. They’re all kinds of things you need to learn if you choose to take up this new sport. Let’s talk about that for a second shall we?

The reason I say this is because I own a very good book that talks about tufting, and the other day when I was going through my bookshelves determining which books I should donate to friends or to the local library I came across this book. I decided to take it with me to the local coffee shop and read through it. I’m very glad I did. It’s an extremely interesting book, and I’d like to recommend it to you as well. The name of the book is;

“The Art of Speed Tufting” by Joseph Montell, published by RC – Rug Crafters, Santa Ana, California, 1973, 63 pages.

In this book you will learn about all of the tools needed for speed tufting. You will learn how to use a tongue and steel shuttle, and a needle and wooden handle tool. You will learn why tufting weavers prefer spring brass tongues, and how to use the adjusting screw to get the tufting tool to walk. It is my belief that if you put in a good 20 hours of practice, you can learn to be a speed tufting artist. If you doubt that, perhaps you might also read the book “The First 20 Hours,” which suggests that you can learn a new skill quite easily if you put your mind to it and use the right methodology to learn.

Okay, back to the tufting book; in this book you will learn how to thread the tufting tool and why there is a bend in the tufting tongue and how to gauge and adjust the loop length as well as the distance between stitches. You will learn about the stretching pattern, preparing the yarn and how to use a yarn reeler. I had no idea about how to latex the back once you are completed, or why the hemming of the pattern and the hemming of a round rug were different.

This book takes you through creating custom patterns from start to finish. Lastly, the book tells you how to wash your creation without ruining it. Please consider all this and think on it – and perhaps, buy this book if you are interested.



Source by Lance Winslow

07 Jun

Understanding Artificial Intelligence, Machine Learning and Deep Learning
Posted in Education by anshbookstore on June 7, 2023 @ 8:40 am

Artificial Intelligence (AI) and its subsets Machine Learning (ML) and Deep Learning (DL) are playing a major role in Data Science. Data Science is a comprehensive process that involves pre-processing, analysis, visualization and prediction. Lets deep dive into AI and its subsets.

Artificial Intelligence (AI) is a branch of computer science concerned with building smart machines capable of performing tasks that typically require human intelligence. AI is mainly divided into three categories as below

  • Artificial Narrow Intelligence (ANI)
  • Artificial General Intelligence (AGI)
  • Artificial Super Intelligence (ASI).

Narrow AI sometimes referred as ‘Weak AI’, performs a single task in a particular way at its best. For example, an automated coffee machine robs which performs a well-defined sequence of actions to make coffee. Whereas AGI, which is also referred as ‘Strong AI’ performs a wide range of tasks that involve thinking and reasoning like a human. Some example is Google Assist, Alexa, Chatbots which uses Natural Language Processing (NPL). Artificial Super Intelligence (ASI) is the advanced version which out performs human capabilities. It can perform creative activities like art, decision making and emotional relationships.

Now let’s look at Machine Learning (ML). It is a subset of AI that involves modeling of algorithms which helps to make predictions based on the recognition of complex data patterns and sets. Machine learning focuses on enabling algorithms to learn from the data provided, gather insights and make predictions on previously unanalyzed data using the information gathered. Different methods of machine learning are

  • supervised learning (Weak AI – Task driven)
  • non-supervised learning (Strong AI – Data Driven)
  • semi-supervised learning (Strong AI -cost effective)
  • reinforced machine learning. (Strong AI – learn from mistakes)

Supervised machine learning uses historical data to understand behavior and formulate future forecasts. Here the system consists of a designated dataset. It is labeled with parameters for the input and the output. And as the new data comes the ML algorithm analysis the new data and gives the exact output on the basis of the fixed parameters. Supervised learning can perform classification or regression tasks. Examples of classification tasks are image classification, face recognition, email spam classification, identify fraud detection, etc. and for regression tasks are weather forecasting, population growth prediction, etc.

Unsupervised machine learning does not use any classified or labelled parameters. It focuses on discovering hidden structures from unlabeled data to help systems infer a function properly. They use techniques such as clustering or dimensionality reduction. Clustering involves grouping data points with similar metric. It is data driven and some examples for clustering are movie recommendation for user in Netflix, customer segmentation, buying habits, etc. Some of dimensionality reduction examples are feature elicitation, big data visualization.

Semi-supervised machine learning works by using both labelled and unlabeled data to improve learning accuracy. Semi-supervised learning can be a cost-effective solution when labelling data turns out to be expensive.

Reinforcement learning is fairly different when compared to supervised and unsupervised learning. It can be defined as a process of trial and error finally delivering results. t is achieved by the principle of iterative improvement cycle (to learn by past mistakes). Reinforcement learning has also been used to teach agents autonomous driving within simulated environments. Q-learning is an example of reinforcement learning algorithms.

Moving ahead to Deep Learning (DL), it is a subset of machine learning where you build algorithms that follow a layered architecture. DL uses multiple layers to progressively extract higher level features from the raw input. For example, in image processing, lower layers may identify edges, while higher layers may identify the concepts relevant to a human such as digits or letters or faces. DL is generally referred to a deep artificial neural network and these are the algorithm sets which are extremely accurate for the problems like sound recognition, image recognition, natural language processing, etc.

To summarize Data Science covers AI, which includes machine learning. However, machine learning itself covers another sub-technology, which is deep learning. Thanks to AI as it is capable of solving harder and harder problems (like detecting cancer better than oncologists) better than humans can.



Source by Cinoy Ravindran

08 May

How to Become an Expert in Data Science
Posted in Education by anshbookstore on May 8, 2023 @ 8:13 am

There are many skills required to become an expert in data science.

But what is most important is mastery of the technical concepts. These include various factors like programming, modeling, statistics, machine learning, and databases.

Programming

Programming is the primary concept you need to know before heading into data science and its various opportunities. To complete any project or carry out some activities related to it, there is a need for a basic level of programming languages. The common programming languages are Python and R since they can be learned easily. It is required for analyzing the data. The tools used for this are RapidMiner, R Studio, SAS, etc.

Modeling

The mathematical models help with carrying out calculations quickly. This, in turn, helps you to make swifter predictions based on the raw data available in front of you. It involves identifying which algorithm would be more befitting for which problem. It also teaches how to train those models. It is a process to systematically put the data retrieved into a specific model for ease in use. It also helps certain organizations or institutions group the data systematically so that they can derive meaningful insights from them. There are three main stages of data science modeling: conceptual, which is regarded as the primary step in modeling, and logical and physical, which are related to disintegrating the data and arranging it into tables, charts, and clusters for easy access. The entity-relationship model is the most basic model of data modeling. Some of the other data modeling concepts involve object-role modeling, Bachman diagrams, and Zachman frameworks.

Statistics

Statistics is one of the four fundamental subjects needed for data science. At the core of data science lies this branch of statistics. It helps the data scientists to obtain meaningful results.

Machine Learning

Machine learning is considered to be the backbone of data science. You need to have a good grip over machine learning to become a successful data scientist. The tools used for this are Azure ML Studio, Spark MLib, Mahout, etc. You should also be aware of the limitations of machine learning. Machine learning is an iterative process.

Databases

A good data scientist should have the proper knowledge of how to manage large databases. They also need to know how databases work and how to carry on the process of database extraction. It is the stored data that is structured in a computer’s memory so that it could be accessed later on in different ways per the need. There are mainly two types of databases. The first one is the relational database, in which the raw data are stored in a structured form in tables and are linked to each other when needed. The second type is non-relational databases, also known as NoSQL databases. These use the fundamental technique of linking data through categories and not relations, unlike relational databases. The key-value pairs are one of the most popular forms of non-relational or NoSQL databases.



Source by Shalini M

09 Mar

Instant Covert Hypnosis – How to Master the Art and Science of the Handshake Induction
Posted in Education by anshbookstore on March 9, 2023 @ 6:52 am

Are you interested in learning the art and science of instant covert hypnosis? Just imagine what you could do with this skill, you could triple your income, seduce the women of your dreams and generally make your life far more easier and exciting.

Today I am going to teach you an advanced instant covert hypnosis technique – The handshake induction. This is a technique that you can use to instantly put someone into a hypnotic trance and anyone with even a passing interest in hypnosis would have heard of this dazzling display of hypnotic artistry.  

To begin reach out and begin shaking your subject’s hand as you normally would. After you have made three typical up and down motions, begin to let go of the subject’s hand, but only for a millisecond, then quickly re-grasp fully. Then release your grasp then quickly re-grasp applying slightly more pressure with one finger than the rest. Release a third time then re- grasp one last time applying slight pressure with a different finger.

Now at the same time that you are doing these motions, gaze directly in your subject’s eyes and then as you are performing the final grasp, move your gaze slightly above their head and behind them, as if you were looking through them. What this process does is induce an altered state through confusion.

Most handshakes are normal and most people have shaken hands hundreds of times. But this handshake is different, it unusual behavior is different it confuses your subject making them go into their head to try figure out what to do. Basically this instant covert hypnosis technique knocks them off their mental balance and put them into an altered state. Did you enjoy learning this technique because I have many more to teach you and you can learn them by following the links below.



Source by David St. Claire

07 Feb

Science Fair Projects to Make Everybody Happy
Posted in Education by anshbookstore on February 7, 2023 @ 6:32 am

Science fair projects – Kids think they should be fun. Teachers think they should be educational. Parents just want them to be fast and easy. Since students, teachers and parents are all involved in the process of getting ready for the science fair, most of the time, science projects have to be all of the above!

As a result, finding the perfect science fair project can be difficult. Here are five steps to finding a project that will make everybody happy.

1. Know what kind of science project is required. There are five kinds of projects, and many a student has had their project idea rejected because of a science technicality. Make sure you know if the science teacher requires an experimental (investigatory) project, a demonstration of a science principle, a report on a subject in science, a collection of items, or a scientific model. Most science fairs require an experiment, which has an hypothesis, tests the hypothesis following the scientific method, and arrives at a conclusion.

2. Find out what interests the student. What does your child do in her spare time? Does he ride horses, is she a soccer player? Is music a passion, or do you have a budding engineer on your hands? If a student is already interested in a subject, learning more about it will come naturally.

3. Determine the budget for time – and money. If your science fair is next week, you need to search for a fast and easy science project that can be done without ordering supplies from Outer Botswana. If you can’t afford special chemicals or science equipment, then you’ll need to focus on projects that can use materials easily found in your home.

4. Use all available resources for the science project search. Head to the library and look at the books on science projects. You can also use the internet. Go to your search engine and type “science project on vitamin C” or “science experiment on insulation”. Note, however, that many books and websites have demonstration projects instead of experiments. So, again, be careful that you find the right type of project.

5. Make a list of possible projects, and work together to choose the best one!



Source by Kayla Fay

08 Jan

Why Statistics and Python to Become Data Scientist?
Posted in Education by anshbookstore on January 8, 2023 @ 6:20 am

If you are into statistics and python, you can take the right courses to become a data scientist. Data covers numerous machines, such as automobiles, robots and smartphones, just to name a few. The amount of data produced by these units requires the use of specialist tools and procedures for decision-making and analysis. Let’s find out why it’s important to learn statistics and python to be a data scientist. Read on to find out more.

In schools, colleges and universities, python is gaining a lot of popularity as an important programming language. The reason is that this language is agile with a lot of libraries and other supporting material like game development and network automation. The good thing is that the Python eco-system has resulted in a lot of libraries in order to allow data analysis. Therefore, it’s part of data science courses.

The lifecycle of data science: first of all, data science has a lifecycle, which is used to perform analysis all over the world. The purpose of the lifecycle is to offer means to develop hypotheses and then test them.

Python helps run fundamental statistical analysis on a given set of data. And these analyses may include measurements of hypothesis testing, probability distribution and central tendency.

Python also helps find out more about input/output variables and operations through a different sample program. Besides, the program shows how you can name different variables and data types. The good thing about this language is that it has no case statements.

Although it’s not used in data science, the object-based design and analysis is also presented. The purpose of this design and analysis is to organize the programs around the given modules.

As far as the libraries are concerned, the courses may include TensorFlow, keras, scikit-learn, Scipy and Numpy, to name a few. These libraries create the base of data science with the help of Python.

If you need to find out more information, you can check out Data Science Central, which is a great platform. On this site, you can choose from a lot of eBooks to find out more about the topic. They also have a forum section to help you take part in the discussions. This can further enhance your knowledge. Aside from this, a lot of YouTube channels are dedicated for the same purpose. You can check them out.

The good thing is that many of the libraries feature online sandboxes. They allow you to try out the library features. You can follow the tutorials to get started with coding. All you need to do is check out different Python modules to find out more. With the passage of time, you will be able to learn more.

So, this is why Python carries so much importance in the field of data science. If you want to become a data scientist, we suggest that you take the right courses to improve your skills in the field of this programming language called Python. Hopefully, you will find this article helpful.



Source by Shalini M

09 Dec

Microsoft DP-100 Exam Guide to Success
Posted in Education by anshbookstore on December 9, 2022 @ 6:02 am

Data science career is rapidly booming in the IT industry. Data scientists are more in demand due to the increasing demand. It is the best time to start learning data science. The reasons mentioned make DP-100 certifications important and mandatory to learn for data science aspirants.

Microsoft Azure Data Scientist Certification overview:

The DP-100 is Azure Data Scientist Associate Certification by Microsoft. This certification aims to use machine learning and data science knowledge to run and carry out workloads of machine learning on Azure. It utilizes the Azure Machine Learning Service. It helps create and plan a working environment suitable for data science workloads on Azure, instructing the predictive machine learning models and executing the experiments.

Benefits of Microsoft DP-100 Certification:

  • Data Scientists are in high demand currently. A certification of this mentioned on the resume can help to bag a high-profile job.
  • A Microsoft certification can immensely enhance the earning and the career opportunities.
  • Various reviews have acknowledged that a Microsoft certification has helped them secure a good job in a reputed company with high payment raise.
  • A candidate with a DA-100 certificate has a higher chance of getting selected among many individuals.

DP-100 Exam Prerequisites:

  • Fundamental understanding and knowledge of Microsoft Azure
  • Should be expert with python language and must be able to work with python data.
  • Should be aware of various libraries like Pandas, Numpy, and Matplotlib.
  • Should understand topics of data science.
  • Should be able to prepare the data.
  • Should be able to train machine learning models using libraries such as Tensorflow, Scikit-Learn, and PyTorch.

Information about Microsoft DP-100 Exam:

  • It is certified by Microsoft.
  • Azure Data Scientist Associate is the certification name.
  • DP-100 is the exam code.
  • The exam is for 180 minutes.
  • The Number of Questions varies between 40 – 60.
  • The Passing Score needed is 700.
  • The exam costs about USD 165.00.

How to Prepare for the DP-100 Exam:

Microsoft Azure Data Scientist Associate certification can be earned by following the best practices. Some steps are mentioned below to help in the preparation of the exam.

  1. After deciding to take the DP-100 exam, go through the official page of the Microsoft DP-100 Certification. Information about “Designing and Implementing a Data Science Solution on Azure” is stated there.
  2. Next, start learning about the syllabus topics. Scroll down the exam objectives and understand them carefully. DP-100 enthusiasts can find the most important domains that should be studied.
  3. Practicing is an important aspect of clearing out the exams. Enroll yourself in a training course before appearing for the actual exams. Free online courses or paid online courses can help to gain insights into the exam pattern of DP-100. Third-party professionals can take the training.
  4. Practice tests are of great help in determining where you need more practice and sufficient knowledge to crack the exam. To avoid mistakes and to overcome them, practice tests are a must.
  5. The official page provides various links to resources and study materials that might help prepare for the DP-100 exam. Take help of them to understand the topics in depth.



Source by Shalini M

09 Nov

Why Classroom Training For Data Science And ML?
Posted in Education by anshbookstore on November 9, 2022 @ 5:16 am

Nowadays, an increasing number of companies are looking for data-driven technologies like automation and artificial intelligence. Therefore, they are in need of qualified and skilled data scientists to meet their needs. In fact, statistics tell us that the year 2020 will see a 20% higher demand for machine learning and data science professionals. In this article, we are going to take a look at the importance of classroom training for ML and data science.

What Is Data Science?

First, it’s important to keep in mind that the field of DS is both a science and art. It involves the analysis and extraction of important data from different sources as far as the planning and measurement of success is concerned. The majority of business depend on this these days.

Why should you take Data Science Training?

It’s important to remember that this field is going through a lot of development. Also, an increasing number of employers realize the value of professionals in this field. As a matter of fact, reports from Indeed tell us that the job posts for these pros has gone up in number by up to 75% over the past three years.

The demand for these professionals is quite high, which is why the competition is stiff. Since this can be a profitable career path, more and more students are opting for these training. In other words, If you really want to pursue a career in the field of machine learning and data science, you should get proper training.

For certification, your first step is to sign up for a data science course. The course will help you find out everything that you need for success in this field. In other words, you will learn both basics as well as advanced skills.

Although you can take free online courses, nothing can beat the classroom training in an accredited institute. The institute will award you with a certification once you have completed the course.

If you are in search of a course that can help you keep updated with the most recent trends in the field, you can ask around or search online.

Although it’s better to take classroom courses, you can also opt for online classrooms. This offers a great convenience for those who are looking to learn new skills from the comfort of their homes. This allows you to a great flexibility that online classrooms can’t offer. Plus, you can learn at your own pace and choose your desired schedule to meet your needs.

If you want to get started, now is the time to apply for a course. Keep in mind that data science and machine learning courses are best for you if you want to secure your future.

The Bottom Line

In short, if you want to take data science and ML training, we suggest that you take a start now. Getting started early is important if you want to stay ahead of your peers. Hopefully, this will help you take the right decision.



Source by Shalini M

10 Oct

What Are the Programming Languages Required for Data Science?
Posted in Education by anshbookstore on October 10, 2022 @ 3:24 am

Since the advancement of Data Science is capturing more popularity. Job opportunities in this field are more. Therefore, in order to gain knowledge and become a professional worker, you need to have a brief idea about at least one of these languages that is required in Data Science.

PYTHON

Python is a general purpose, multiparadigm and one of the most popular languages. It is simple, easy- to-learn and widely used by the data scientists. Python has a huge number of libraries which is its biggest strength and can help us perform multiple tasks like image processing, web development, data mining, database, graphical user interface etc. Since technologies such as Artificial Intelligence and Machine Learning have advanced to a great height, the demand for Python experts has risen. Since Python combines improvement with the ability to interface with algorithms of high performance written in C or Fortran, it has become the most popularly used language among data scientists. The process of Data Science revolves around ETL (extraction-transformation-loading) process which makes Python well suited.

R

For statistical computing purposes, R in data science is considered as the best programming language. It is a programming language and software environment for graphics and statistical computing. It is domain specific and has excellent high-quality range. R consists of open source packages for statistical and quantitative application. This includes advanced plotting, non-linear regression, neural networks, phylogenetics and many more. For analyzing data, Data Scientists and Data Miners use R widely.

SQL

SQL, also known as Structured Query Language is also one of the most popular languages in the field of Data Science. It is a domain-specific programming language and is designed to manage relational database. It is systematic at manipulating and updating relational databases and is used for a wide range of applications. SQL is also used for retrieving and storing data for years. Declarative syntax of SQL makes it a readable language. SQL’s efficiency is a proof that data scientists consider it a useful language.

JULIA

Julia is a high level, JIT (“just-in-time”) compiled language. It offers dynamic typing, scripting capabilities and simplicity of a language like Python. Because of faster execution, it has become a fine choice to deal with complex projects that contains high volumes of data sets. Readability is the key advantage of this language and Julia is also a general-purpose programming language.

SCALA

Scala is multiparadigm, open source, general-purpose programming language. Scala programs are complied to Java Bytecode which runs on JVM. This permits interoperability with Java language making it a substantial language which is appropriate for Data Science. Scala + Spark is the best solution when computing to operate with Big Data.

JAVA

Java is also a general purpose, extremely popular object-oriented programming language. Java programs are compiled to byte code which is platform independent and runs on any system that has JVM. Instructions in Java are executed by a Java run-time system called Java Virtual Machine (JVM). This language is used to create web applications, backend systems and also desktop and mobile applications. Java is said to be a good choice for Data Science. Java’s safety and performance is said to be really advantageous for Data Science since companies prefer to integrate the production code into the codebase that exist, directly.



Source by Shalini M

10 Sep

Learning Energizes Your Brain To Learn More
Posted in Education by anshbookstore on September 10, 2022 @ 1:32 am

Have you ever wanted to learn about something but didn’t know how? You’re not alone. For every question, there is usually an answer; it’s merely a matter of discovering the most appropriate avenue of access that will lead you to an explanation. Sometimes it’s a short road, other times it can seem like the never-ending highway to bewilderment.

Deciphering conscious thought is a more complex process than you might imagine. For the brain to input new quantities of information, an entire series of biological connections have to occur. Those connections are transmitted via electrical impulses called neurons. Explaining how conscious thoughts arise from electric signals is something numerous scientists are still trying to learn.

Not as simple as it seems, considering the brain is considered “the most complex object in the known universe,” according to Christof Koch, Chief Scientific Officer of the Allen Institute for Brain Science. Koch is one of many researchers diligently working on uncovering the mystery of how the brain connects its 100 billion neurons to perform the myriad of daily conscious activities we all experience.

Neurological Landscape Of The Brain Constantly Expanding

Science is now trying to explain questions about the brain that analytical thinking has not been able to answer. Koch compares studying the brain to examining the rainforest. With the amount of biological diversity found throughout a tropical jungle, new generations of scientific investigators continually discover new and uncharted territories. And again, the universe expands, presenting new questions and providing new observations.

It is much the same with our brain. As exploratory tools evolve, so too, does our capacity to analyze and understand the complexities within our brain. Neurologists have uncovered possibilities previously unknown, such as humans possessing 1,000 different types of nerve cells, just as there are 1,000 different species of trees in the rainforest.

Understanding how things work, reflecting on why they are, theorizing about possible explanations for unclear experiences, then experimenting to either prove or disprove a theory is referred to as the learning cycle: Experiencing > Reflecting > Theorizing > Experimenting. This scientific interpretation of the learning process may seem overly simplistic, but nonetheless represents the cognitive steps that occur when we learn.

What Learning Style Are You?

Keep in mind these actions happen must faster in the deep unexplored recesses of the brain than in the relative surface-level awareness of the conscious mind. Learning time can vary based on experiential differences; reflections may emerge quicker if the brain recognizes a previous related experience; theorizing can become more efficient if a reflection mirrors a previous action, and experimentation could be minimized given the cycle is familiar.

In other words, we learn as a result of previous learning.

D.A. Kolb, Ph.D. in social psychology from Harvard University, condenses the learning process into what has become known as the Four Learning Styles: Divergers are people who analyze experiences and think deeply about them; Convergers conceptualize experiences then give them the practicality test; Accomodators like to ‘do’ rather than ‘think’, and Assimilators prefer to think rather than act… they prefer collecting information over excessive experimentation.

Attempting to decipher the mysteries of the brain without reflecting on our past experiences to do so, would be short-changing the very learning process we are seeking to unravel.



Source by Gary G Sweet