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The evolution of online writing services

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The evolution of online writing services

Are you struggling with your essays and homework? Make use a College homework help website today and say goodbye to your struggles. Most students are now not shying away from college homework help websites as they are proving to be a useful tool to every college student worldwide. Gone are the days such websites only used to offer assignment writing services. Nowadays the services are helping students develop their reading and writing techniques. The services are going beyond to even teach students how to write their own essays papers and assignments.  So for example if you are a sociology student you can learn about sociology research topics that you can write about in most blog segments. This will help you write a good sociology research paper. Once you start writing the paper, you can learn about different citation styles from the same services. Essay formats such as APA are regularly updated and you can learn more about such updates. Once you are done with your essay or paper and need to write an essay cover page the same websites will teach you how to write one. Editing and proofreading services are also offered by the same sites meaning you can get whatever service you need in house.

This underlines the evolution of assignment writing services. They are now merely not sources of homework help. You can use them to learn on how to be a better writer and researcher. You can use them to learn how use different writing styles and how to avoid plagiarism. Gone are the days such websites were shunned by all and sundry. They have truly revolutionized the world of academia. Most College and post College education students nowadays find themselves using such services for the sole purpose of learning. Since most learning is online nowadays, these services definitely come in handy.

A bulk of students who are using these services nowadays are doctoral students writing their dissertations. Dissertation writing services  have become hugely popular amongst doctoral students. This is because dissertations can’t be done in a day or even week. Writing a dissertation requires months.  You need to prepare both mentally and physically as it requires a lot of research. However with the evolution of custom writing services, writing a dissertation has become easier. This is not because someone can write the dissertation for you but because you will be empowered with all the knowledge required to write an award winning dissertation. This is thanks to most of these custom essay writing services. You see most of the blog segments of these services are filled with information on how to go about almost any writing task.

The question that most students ask is whether such services are legal. These services are legal, however, you must carefully read the terms and conditions. Most of the papers written aren’t meant to be submitted as the student’s original work. They are meant to be used as a guide. If a student decides to submit the paper as his/her original work then the service provide is not liable. As a student, you main objective is to study and this includes doing assignments and essays. There’s no harm in asking for help with some of those essays but when it gets to a point where you are no longer writing your own assignments then there’s a problem. This will end with you failing your exams. You need to be smart about how you go about using such services. “Do my homework for money services” shouldn’t be misused to your disadvantage as a student, so you should ensure that you are on top of things.

Custom essay writing services have greatly evolved over the past couple of years. This underscores the importance of change in our everyday life. The end result is they have become more academia friendly meaning instead of being looked at as a threat to the learning process; they should be seen as complimentary. Students shouldn’t be ashamed or afraid of using such services. As long as they are utilized effectively they will always be advantageous to the students and to the learning process as a whole.

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EDUCATION

How to Prepare for Class 8 Maths Exams from NCERT Maths Book?

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How to Prepare for Class 8 Maths Exams from NCERT Maths Book?

Mathematics is not an easy subject if you are not familiar with the concepts completely. Therefore, for a student, it is immensely important to do maths every day. Regular studies are the first step to scoring well in the exam. However, you can always take additional help from the NCERT maths books.

NCERT Books can be a great help as they offer exercises to solve, easy explanations, ncert maths book class 8 solutions pdf free download, and more. Referring to these books after your child has completed the said chapter in the textbook can help gain a deeper insight. Now, before you start preparing for the exam, it is extremely important to understand the number of chapters present in the syllabus and do them one by one.

Syllabus of Class 8 Maths Exams

Topic:  Rational Numbers

Introduction

Properties of Rational Numbers

Representing Rational Numbers on the Number Line

Rational Number between Two Rational Numbers

Topic: Linear Equations in One Variable

Introduction to linear equations

Solving Equations

Some Applications

Solving Equations where there are Variable on either side

Some More Applications

Reducing Equations to Simpler Form

Equations Reducible to the Linear Form

Topic: Understanding Quadrilaterals

Introduction

Polygons

A few Measures of the Exterior Angles of a Polygon

Kinds of Quadrilaterals

Some Special Parallelograms

Topic: Practical Geometry

Introduction

Constructing a Quadrilateral

Some Special Cases

Topic: Data Handling

Looking for Information

Organising Data

Grouping Data

Circle Graph or Pie Chart

Chance and Probability

Topic: Squares and Square Roots

Introduction

Properties of Square Numbers

Some More Interesting Patterns

Finding the Square of a Number

Square Roots

Square Roots of Decimals

Estimating Square Root

Topic: Cubes and Cube Roots

Introduction

Cubes

Cubes Roots

Topic: Comparing Quantities

Recalling Ratios and Percentages

Looking for the Increase and Decrease Percent

Finding Discounts

Prices That Are  Related to Buying and Selling i.e.Profit and Loss

Sales Tax/Value Added Tax/Goods and Services Tax

Compound Interest

Deducing a Formula for Compound Interest

Rate Compounded Annually or Half Yearly (Semi-Annually)

Applications of Compound Interest Formula

Topic: Algebraic Expressions and Identities

What are Expressions?

Terms, Factors and Coefficients

Monomials, Binomials and Polynomials

Like and Unlike Terms

Subtraction and Addition of Algebraic Expressions

Introduction to Multiplication of Algebraic Expressions

Multiplying a Monomial by a Monomial

Multiplying a Monomial by a Polynomial

Multiplying a Polynomial by a Polynomial

What is an Identity?

Standard Identities

Applying Identities

Topic: Visualising Solid Shapes

 Introduction

 View of 3D-Shapes

 Mapping Space Around Us

Faces, Edges and Vertices

Topic: Mensuration

Introduction

Let us Recall

Area of Trapezium

Area of General Quadrilateral

Area of Polygons

Solid Shapes

Surface Area of Cube, Cuboid and Cylinder

The volume of Cube, Cuboid and Cylinder

Volume and Capacity            

Topic: Exponents and Powers

Introduction

Powers with Negative Exponents

Laws of Exponents

Using Exponents to Express Small Numbers and convert them into Standard Form

Topic: Direct and Inverse Proportions

Introduction

Direct Proportion

Inverse Proportion

Topic: Playing with Numbers

Introduction

Numbers in General Form

Game with Numbers

Letters for Digits

Test of Divisibility

Topic: Factorisation

Introduction

What is Factorisation?

Division of Algebraic Expressions

Division of Algebraic Expressions Continued (Polynomial / Polynomial)

Can you Find the Error?

Topic: Introduction to Graphs

 Introduction

 Linear Graphs

Some Applications

When it comes to mathematics, skipping any chapter is never advised. However, you can make sure your child studies regularly to avoid any issues. Some tips will make studies for exams easier, read ahead to know more.

How To Study From Maths NCERT Books?

Using NCERT books is very helpful because;

  • The language used in the book is very simple, meaning, your child can read it himself.
  • Reputed experts and teachers with vast experience have written the books.
  • NCERT books are a genuine source of information.
  • NCERT books are great for understanding the concepts easily.

Start Chapter-Wise

One of the best ways to make use of NCERT books is to study them chapter-wise. It means, your child studies the chapter in the textbook once and then do the exercises and then move on to the NCERT books. This will ensure your child is completely prepared for the exams. It is important to give equal time to both books to ensure your child is completely prepared. Solving all the problems present in the book means more practice and more knowledge.

Make A Study Schedule

A study schedule is very important when it comes to studying for the exam. Now, you can make separate study schedules for your child- one for the textbook and the other for the NCERT book. Or you can have one study schedule including both. Don’t overburden your child, but make sure you cover all the chapters from both books. When it comes to Mathematics, practice makes perfect.

Focus on Challenging Chapters

Your child needs to concentrate more on difficult topics, but without taking too much time. Maths is a subject where your child can score full marks. Therefore, make sure all chapters are covered.

Sample Question Papers

Solving sample papers is another great way to make sure your child is thorough. It can also help you understand their strengths and weaknesses, and since you will have ample time before the exams, you can work on any weaknesses.

Always Clear The Doubts

If your child has any doubts regarding a concept, make sure it gets cleared immediately. You can either talk to the teacher or hire an online tutor. It is also possible to understand the concept clearly with the help of the NCERT books.

Finally, remember, make sure your child remains calm and panic-free before and during the exam as it will help him remember everything more clearly. Also, try to make learning more fun, with quizzes, interactive games, and more as this will help your child learn better.

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EDUCATION

All You Need To Know About Molecular Spectroscopy

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All You Need To Know About Molecular Spectroscopy

Spectroscopy involves the investigation and measurement of spectra produced due to matter interacting to give off electromagnetic radiation. In other words, it refers to the dispersion of light into component colours. It involves the interactions between molecules and electromagnetic radiation. Therefore that tells you that molecules oscillate from a lower energy level to a higher energy one and back repeatedly. In the process, the molecules absorb and radiate electromagnetic radiation. Here includes everything you need to learn about Molecular Spectroscopy.

Understand the meaning of a Molecule

A molecule is a group of positively charged atoms surrounded by a considerable number of negatively charged electrons. That explains molecular stability since there is a balance between the attractive and repulsive forces of the negatively charged electrons and positively charged nuclei. Moreover, a molecule is also characterized by the resulting energy that emerges from these interacting forces.

Types of Molecular Spectroscopy

The electromagnetic spectrum of a series of wavelengths is produced whenever a matter is exposed to any electromagnetic radiation. As a result, the Molecules will absorb a certain amount of wavelengths to the vibrational, higher electronic, and rotational energy levels. Therefore, the series of wavelengths a given molecule absorbs gives off a distinct molecular spectrum that lies in the specific region of the electromagnetic spectrum. Here are three types of molecular spectra you should know:

  • Pure Rotational Spectra

Pure rotational spectra occur when a molecule absorbs the least amount of energy that compels it to transit from one rotational level to the next but within the same vibrational level. It is possible to observe rotational spectra using the spectral region of Far Infrared and Microwaves. Moreover, the energies in these types of spectral regions are very small. That’s why they are referred to as all rotational spectra, the microwave spectra.

  • Vibrational Rotational Spectra

Vibrational, rotational spectra occurs when a molecule absorbs sufficient energy that causes the molecule to move from one vibrational level to the next within the same electronic level. However, in this case, both vibrational and rotational transition takes place. That is how you end up with vibrational, rotational spectra. With vibrational spectra, it is easy to observe the spectra in the Near-Infrared Spectral region. That’s called vibrational, rotational spectra, the Infrared spectra.

  • Electronic Band Spectra

Lastly, electronic band spectra happen when the radiation’s exciting energy is large enough to aid the successful transition from one electronic level to the next. Both rotational and vibrational level changes accompany this transition. Additionally, a set of closely spaced lines appear for each vibrational transition. These closely spaced lines are known as bands Because the corresponding rotational level changes.

Molecular Spectroscopy primarily involves the excitation of atoms and molecules using photons. They are excited by either resonant vibrations or electronic transitions based on the induced quantum mechanical changes. While vibrational transitions correspond to changes made within the molecular vibrational states, they typically appear in the infrared region. On the other hand, Electronic transitions specifically correspond to changes in the electronic state of the molecules. Moreover, it often appears in the UV-visible region.

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EDUCATION

Difference between an Algorithm and a Model in Machine Learning

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Difference between an Algorithm and a Model in Machine Learning

If you are at a crossroads, wondering which data science certification to take for a highly relevant career upgrade, opt for a Machine Learning course at a reputed institute. It teaches you all the essentials of big data analytics and steers your career path to a more focused data scientist job role as a machine learning engineer. 

Before registering for the course, you may like to clear some concepts about machine learning algorithms and models. So here we are, discussing what is an algorithm and a model? What is the difference between the two?

What is Machine Learning

IBM lays down the definition of machine learning: “Machine learning is a branch of artificial intelligence (AI) and computer science which focuses on the use of data and algorithms to imitate the way that humans learn, gradually improving its accuracy.” It provides systems with the ability to automatically and iteratively learn and improve from experience. Machine learning is also an important element of data science, where algorithms train to make classifications or predictions, to uncover insights from massive quantities of data.

Embed Youtube video URL here: https://www.youtube.com/embed/ukzFI9rgwfU

What is an Algorithm in Machine Learning

Some definitions of machine learning encapsulate the “algorithm” component of machine learning.

A McKinsey & Co. Insight states that “Machine learning is based on algorithms that can learn from data without relying on rules-based programming.” A University of Washington paper mentions how “Machine learning algorithms figure out how to perform important tasks by generalizing from examples.”

So what is an “algorithm” in machine learning? A machine learning algorithm is a program that provides systems the ability to learn on their own and improve from experience without being explicitly programmed. Machines adjust themselves to perform better as they are exposed to more data. 

What is a Machine Learning Model

A machine learning model is a file that has been trained to recognize certain types of patterns. A set of data is used and provided with an algorithm to reason and learn from the data, and this is called training the model. Once the model is trained with the given set of training data, it can be used to reason with unseen data and make predictions about that data. For example, if you want to build an application that recognizes a user’s emotions based on facial expressions. The model can be trained by providing it with images of faces tagged with a certain emotion and then used in an application that can then recognize any user’s emotion. 

A machine learning model is thus a condensed representation of what a machine learning system has learned.  It is similar to a mathematical function that takes a request as input data, makes a prediction on that input data, and then serves a response. 

The final set of trainable parameters of a model depends on the type of model. All machine learning models are categorized as either supervised or unsupervised. Where the model is a supervised model, it is further sub-categorized as either a regression or classification model. 

Difference between a Machine Learning Algorithm and a Machine Learning Model

An “algorithm” creates a machine learning “model.” A “model” is the output of a machine learning algorithm. The model represents what is learned by the algorithm. The Machine Learning model is “the “thing” that is saved after running an algorithm on training data and represents the rules, numbers, and any other algorithm-specific data structures required to make predictions.”

Examples

Examples of machine learning algorithms are linear regression, decision trees, convolutional neural networks, and reinforced learning. Some examples of machine learning models are Regression Models, Clustering Models, and Dimensionality Reduction Models.

Data set

Machine learning algorithms are executed in the code and run on data. Machine learning models are the output of the algorithms and consist of the model data and a prediction algorithm.

When you train an algorithm with known data, it becomes a model. 

Thus, Model = Training (of an Algorithm + Data)

Mathematical equation

Every algorithm has a mathematical underpinning, which, when executed in a machine, forms a machine learning algorithm. A model is an equation structured by discovering the parameters in the equation of the algorithm. The model specifies which family of functions the learning algorithm can choose from when varying the parameters to reduce the training objective. (Deep learning book, Goodfellow et al., 2016).

Algorithms are methods undertaken to get a task done or solve a problem, while Models are well-defined computations that are a product of an algorithm.

An algorithm takes some value as input and produces some value as output and is thus a sequence of steps with flow and loops, etc., transforming input to output.

Approach

An algorithm is an approach you take to solve a problem. The model is what you get when you run the algorithm on the training data and what you further use to make predictions on new data. A new model may be generated using the same algorithm but with different data, and a new model can be generated from the same data using a different algorithm.

Training data vs. new data

An algorithm is implemented in a programming language. An algorithm takes an input set of data and outputs an equation which is a model. Model is the output of the machine learning algorithm.

An algorithm can be used on different sets of training data. A model is then used as the deployment vehicle, which can take any unseen data in the future and produce an output prediction. That model has both data and a procedure for how to use the training data to make a prediction on new data, almost like a prediction algorithm.

Storing the entire dataset

Models are always triggered by the algorithm but not always dependent on the data. Based on the purpose that your model serves, some models like the k-nearest neighbors store the entire dataset, which acts as the prediction algorithm. 

Interrelationship

The algorithm behind the model matters most, as it is important to know which algorithm to apply to your model to yield the best predictions and right results. If the algorithm used is right, you will likely get a good model that works well on new data sets. However, a robust algorithm does not always yield a good model, as a machine learning model strategy depends on various factors other than the algorithm.

Ultimately, an algorithm is a few lines of code that you implement after much deliberation, while a perfectly working model is dependent on many other factors other than the algorithm or the training data.

Conclusion

Ultimately, a machine learning engineer works with big data to execute algorithms and build models for predictions. And a good way to kick-start a career in machine learning is to take a certification.

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