#linear equations in one variable for class 8

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Class-8 Mathematics NCERT Solution Execrise 2.4 Question 3,4,5

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Class 8 chapter 2 Linear equations in one variable Ex 2.2 solved problems

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Benefits of Learning: Digital Classes for 9th & 10th Math and Science

We all know that 9th and 10th class math and science could be difficult. It is generally the first important year of examinations for students, which can be difficult. Parents frequently believe that taking their children to extra classes would help them perform better. While this may be true, it might be difficult for students.

That’s the reason why online courses come in useful. They work similarly to smart classrooms, allowing students to view and engage with the material they are studying. Instead of simply remembering information, people may observe how things function. Learning becomes as simple as installing an app!

Now, students in 9th and 10th grade do not have worries about their classes. Using digital class apps allows them to follow along with their math and science classes. It helps them understand every detail and increases their problem-solving abilities.

Students can also access lessons from other educational platforms when taking classes digitally. Therefore, you are free to explore different syllabi while you are studying the current one. These days, learning is more about truly understanding things than taking exams.

Also, look at the most recent 9th and 10th class syllabuses, which may be helpful!

Class 9 Math's: Unit-wise

Unit 1-Real numbers

Unit 2-Polynomials and Factorization

Unit 3-The Elements of Geometry

Unit 4-Lines and Angles

Unit 5-Co-Ordinate Geometry

Unit 6-Linear Equations in Two Variables

Unit 7-Triangles

Unit 8-Quadrilaterals

Unit 9-Statistics

Unit 10-Surface Areas and Volumes

Unit 11-Areas

Unit 12-Circles

Unit 13-Geometrical Constructions

Unit 14-Probability

Unit 15-Proofs in Mathematics

Class 9 Physics Syllabus: Chapter-wise

Chapter 1-Matter Around Us

Chapter 2-Motion

Chapter 3-Laws of Motion

Chapter 4-Refraction of Light at Plane Surfaces

Chapter 5-Gravitation

Chapter 6-Is Matter Pure?

Chapter 7-Atoms and Molecules

Chapter 8-Floating Bodies

Chapter 9-What is inside the Atom?

Chapter 10-Work and Energy

Chapter 11-Heat

Chapter 12-Sound

Class 9 Biology Syllabus: Chapter-wise

Chapter 1-Cell its structure and functions

Chapter 2-Plant tissues

Chapter 3-Animal tissues

Chapter 4-Plasma membrane

Chapter 5-Diversity in Living Organism

Chapter 6-Sense Organs – I

Chapter 7-Sense Organs – II

Chapter 8-Animal behavior

Chapter 9-Challenges in Improving Agricultural Products

Chapter 10-Adaptations in Different Ecosystems

Chapter 11-Soil pollution

Chapter 12-Bio geochemical cycles

The Telangana State Board (TS) students who are looking for the Class 9 Physics, Biology Syllabus can find it in the syllabus mentioned above. moreover, review the AP Class 9th syllabus also.

Class 10 Math and Science Syllabus:

The Telangana State Board Class 10 math's syllabus is given below; class tenth With the use of this TS 10th Class Math's Textbooks Syllabus, students may prepare for their final exams and have an effective understanding of the subject matter, therefore achieving the highest marks possible.

10th Class Math's: Unit Wise

Unit 1-Real numbers

Unit 2-Sets

Unit 3-Polynomials

Unit 4-Pair of Linear Equations in Two Variables

Unit 5-Quadratic Equations

Unit 6-Progressions

Unit 7-Coordinate Geometry

Unit 8-Similar Triangles

Unit 9-Tangents and Secants to a Circle

Unit 10-Mensuration

Unit 11-Trigonometry

Unit 12-Applications of Trigonometry

Unit 13-Probability

Unit 14-Statistics

Class 10 Physics Syllabus: Chapter Wise

Chapter 1-Reflection of Light by Different Surfaces

Chapter 2-Chemical Reactions and Equations

Chapter 3-Acids, Bases and Salts

Chapter 4-Refraction of Light at Curved Surfaces

Chapter 5-Human Eye and Colorful World

Chapter 6-Structure of Atom

Chapter 7-Classification of Elements – Periodic Table

Chapter 8-Chemical Bonding

Chapter 9-Electric Current

Chapter 10-Electromagnetism

Chapter 11-Principles of Metallurgy

Chapter 12-Carbon and Compounds

10th Class Biology Syllabus: Chapter-Wise

Chapter 1-Nutrition

Chapter 2-Respiration

Chapter 3-Transportation

Chapter 4-Excretion

Chapter 5-Coordination

Chapter 6-Reproduction

Chapter 7-Coordination in Life Processes

Chapter 8-Heredity

Chapter 9-Our Environment

Chapter 10-Natural Resources

The TS Board Class 10 Math's & Science topic syllabus is available above, complete with chapter-by-chapter details. Students can go through the most recent math and science syllabus to be aware of all the key subjects they need to study for exams.

Additionally, visit the below pages. We hope you found these pages useful:

Telangana Board Syllabus Related Links

Telangana Board Class 6 Syllabus

Telangana Board Class 7 Syllabus

Telangana Board Class 8 Syllabus

Telangana Board Class 9 Syllabus

[studyupindia]

NCERT / CBSE Class-8-Mathematics ( English Medium )

(All books are 100℅ original and as per the latest NCERT syllabus. Prices includes Rs. 32 per book mandatory binding charges.)

This Class 8 Books Contains :

Chapter 1: Rational Numbers Chapter 2: Linear Equations in one Variable Chapter 3: Understanding Quadrilaterals Chapter 4: Data Handling Chapter 5: Square and Square Roots Chapter 6: Cube and Cube Roots Chapter 7: Comparing Quantities Chapter 8: Algebraic Expressions and Identities Chapter 9: Mensuration Chapter 10: Exponents and Powers Chapter 11: Direct and Inverse Proportions Chapter 12: Factorization Chapter 13: Introduction to Graphs

As Studyupindia provides products with quick delivery, easy exchanges from trusted sellers . NCERTS Books

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CBSE Class 10 Syllabus has been designed to help students gain a deeper understanding of the subject matter.

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WBBSE solutions for class 8 maths algebra chapter 12 equations

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Linear Equations in One Variable Class 8 Notes Maths Chapter 2

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Kuta software algebra 1 word problems

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Free Algebra 1 Worksheets.Īccess Free Kuta Software Infinite Algebra 1 Writing Linear Equations Answer Key backcountry to find him. Create the worksheets you need with Infinite Algebra 1.

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Learning from data and use two machine learning concepts explored in the Python code snippets as POC

1. Linear Regression:

1 1.Concept: It's a statistical approach for modeling the relationship between a dependent variable (what you want to predict) and one or more independent variables (what you're basing your prediction on). The resulting model is a linear equation that approximates the underlying trend in the data.

 1 2.Example: In the code, linear regression predicts house prices (dependent variable) based on house size and number of bedrooms (independent variables). The model learns the linear relationship between these features and prices from the training data and uses that knowledge to estimate prices for new houses.

2. K-Nearest Neighbors (KNN):

2.1.Concept: A non-parametric classification algorithm that classifies data points based on their similarity to labeled data points in the training set. It finds the k nearest neighbors (data points most similar) to the new data point and assigns the majority class label from those neighbors.

2.2.Example: The code employs KNN to classify data points as "red" or "blue." For a new data point, KNN identifies the 3 closest labeled points (k=3) from the training data. If the majority of those 3 points are classified as "red," the new data point is also predicted as "red."

Here's a Python snippet showcasing both Linear Regression and K-Nearest Neighbors (KNN) for illustration purposes:

import numpy as np

from sklearn.linear_model import LinearRegression

from sklearn.neighbors import KNeighborsClassifier

# Sample data for linear regression (house prices)

data = np.array([[1500, 2], [2000, 3], [2500, 4], [3000, 5]])

prices = np.array([250000, 300000, 350000, 400000])

# Linear Regression model and prediction

reg_model = LinearRegression()

reg_model.fit(data, prices)

new_data = np.array([[3500, 6]])

predicted_price = reg_model.predict(new_data)

print("Predicted house price using linear regression:", predicted_price[0])

# Sample data for KNN classification (data points)

X = np.array([[1, 2], [3, 4], [5, 6], [7, 8], [1, 4], [3, 6], [5, 8]])

y = np.array(["red", "red", "blue", "blue", "red", "red", "blue"])

# KNN model and prediction

knn_model = KNeighborsClassifier(n_neighbors=3)

knn_model.fit(X, y)

new_data = np.array([[2, 5]])

predicted_class = knn_model.predict(new_data)

print("Predicted class using KNN:", predicted_class[0])

This code demonstrates the core principles of both algorithms:

 Linear Regression: It learns a linear equation to approximate the relationship between house size/bedrooms (features) and house prices (target variable).

K-Nearest Neighbors (KNN): It classifies new data points based on the majority class of their k nearest neighbors in the training data (k=3 in this case).

These are just two fundamental ML algorithms, but they represent different approaches to learning from data. Linear regression excels at modeling continuous relationships, while KNN is well-suited for classification tasks.

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Source https://github.com/RevanthK/Predicting-Book-Sales

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Class-8 Mathematics NCERT Soluction Ex-2.5 chapter-2 #Mathswithnarendrasir #Narendrasir

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Class 8 Ex 2.4 chapter 2. Linear Equations in one variable solved problems

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AI and ML

This article was only an introduction of these machine learning algorithms. If you want to know more, check out  our online Artificial Intelligence & Machine Learning Course contains the perfect mix of theory, case studies, and extensive hands-on assignments to even turn a beginner into a pro by the end. Our ML and artificial intelligence certification courses are perfect for students and working professionals to get mentored directly from industry experts, build your practical knowledge, receive complete career coaching, be a certified AI and ML Engineer.

Top 10 Machine Learning Algorithms You should Know in 2021

 Living in an era of speedy technological development isn't easy. Especially, when you are interested in Machine Learning!

New Machine Learning Algorithms are coming up everyday with an unmatchable pace to get a hold of them! This article will help you grasp at least some of these algorithms being commonly used in the data science community. Data Scientists have been enhancing the data-crunching machines everyday to build a sophisticatedly advanced technology.

 Here we are listing top 10 Machine learning algorithms for you to learn in 2021 -

 1. Linear Regression

 This is a fundamental algorithm, used to model relationships between a dependent variable and one or more independent variables by fitting them to a line. This line is known as the regression line and is represented by a linear equation Y = a 'X + b

 2. Logistic Regression

 This type of regression is very similar to linear regression but this one in particular is used to model the probability of a discrete number of outcomes, which is typically two - usually binary values like 0/1 from a set of independent variables. It calculates the probability of an event by fitting data to a logit function. This may sound complex but it only has one extra step as compared to linear regression!

   3. Naive Bayes

 This algorithm is a classifier. It assumes the presence of a particular feature in a class which is unrelated to the presence of any other feature.  It may seem like a daunting algorithm because it necessitates preliminary mathematical knowledge in conditional probability and Bayes Theorem, but it's extremely simple to use.

 4.KNN Algorithm

 KNN Algorithms can be applied to both - classification and regression problems. This algorithm stores all the available cases and classifies any new cases by taking a majority vote of its k neighbours. Then, the case is transferred to the class with which it has the most in common.

 5. Dimensionality Reduction Algorithm

 This algorithm like Decision Tree, Missing Value Ratio, Factor Analysis, and Random Forest can help you find relevant details.

 6. Random Forest Algorithm

 Random forests Algorithms are an ensembles learning technique that builds off of decision trees. It generally involved creating multiple decision trees using bootstrapped datasets of the original data. It randomly selects a subset of variables at each step of the decision tree. Each tree is classified and the tree "votes" for that class. 

    7. SVM Algorithm

 SVM stands for Support Vector Machine. In this algorithm, we plot raw data as points in an n-dimensional space (n = no. Of features you have). Then the value of each feature is tied to a particular coordinate, making it extremely easy to classify the data provided.

 8.  Decision Tree

 This algorithm is a supervised learning algorithm which is used to classify problems. While using this algorithm, we split the population into two or more homogenous sets based on the most significant attributes or independent variables.

 9. Gradient Boosting Algorithm

 This algorithm is used as a boosting algorithm, which is used when massive data loads have to be handled to make predictions with high accuracy rates.

 10. AdaBoost

 AdaBoost also known as Adaptive Boost is an ensemble algorithm that leverages bagging and boosting methods and developed an enhanced predictor. The predictions are taken from the decision trees.

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Selina Concise Mathematics Class 8 ICSE Solutions Chapter 14 Linear Equations in one Variable

Selina Concise Mathematics Class 8 ICSE Solutions Chapter 14 Linear Equations in one Variable

Selina Concise Mathematics Class 8 ICSE Solutions Chapter 14 Linear Equations in one Variable (With Problems Based on Linear equations)

Selina Publishers Concise Mathematics Class 8 ICSE Solutions Chapter 14 Linear Equations in one Variable (With Problems Based on Linear equations)

Linear Equations in one Variable Exercise 14A – Selina Concise Mathematics Class 8 ICSE Solutions

Solve the…

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ncert solutions for class 8 maths

NCERT Solutions for class 8 Maths

in case if you are looking for accurate, comprehensive and free ncert solutions for class 8 maths then you are at very right place. These ncert solutions for class 8 maths are available in the PDF format, which can be easily shared and accessed among friends. The diligent efforts of faculties at Entrancei have made sure the best of all solutions are available for students.

In order to make the learning process very easy, the students need to just go throw the study material. These ncert solutions for class 8 maths are the best tool for a quick revision of your concepts. The complete syllabus has been broke down into various parts in the manner of chapters. Students need to just sign up at Entracei. The categorization of chapters is made below

Ø  Chapter 1: Rational Number

This chapter of ncert solutions for class 8 maths helps the students with giving them a deep understanding of number system and rational numbers.

Ø  Chapter2: Linear Equations in One Variable

This chapter of ncert solutions for class 8 maths helps the students in getting acquainted with the method of solving contextual problems based upon linear equations

Ø  Chapter 3: Understanding Quadrilaterals

This chapter of ncert solutions for class 8 maths helps the students with knowledge of angles and their sum. This chapter helps the students with convex, concave and parallelograms.

Ø  Chapter 4: Practical Geometry

This chapter of ncert solutions for class 8 maths helps the students with enhancing their knowledge regarding rhombus, parallelograms, squares, and rectangles.

Ø  Chapter 5: Data Handling

This chapter enhances students’ knowledge with the method of representing data in the form of bar graphs and charts.

Ø  Chapter 6: Square and Square roots

This chapter of ncert solutions for class 8 maths teaches the students in solving critical square roots through factor method and division method

Ø  Chapter 7: Cube and Cube roots

This method presents a stepwise representation of methods to determine cube roots.

Ø  Chapter 8: Comparing Quantities

This chapter of ncert solutions for class 8 maths helps the students with unit quantities like percentage, ratio, sales tax, and compound interests.

Ø  Chapter 9: Algebraic expressions and Identities

This chapter of ncert solutions for class 8 maths helps the students in solving algebraic expression with addition, subtraction, division, and multiplication.

Ø  Chapter 10: Visualising solid shapes

This chapter of ncert solutions for class 8 maths helps the students regarding 3D objects and their plotting on map.

Ø  Chapter 11: Mensuration

This chapter of ncert solutions for class 8 maths helps the students in determining areas of polygon and trapezium.

Ø  Chapter 12: Exponents and powers

This chapter of ncert solutions for class 8 maths helps the students regarding the integers in the number system.

Ø  Chapter 13: Direct and Inverse Proportions

This chapter of ncert solutions for class 8 maths helps the students regarding the variation which is direct and in-direct quantities in NCERT book.

Ø  Chapter 14: Factorization

This chapter helps the students in enhancing their concepts associated with algebraic identities and factorization.

Ø  Chapter 15: Introduction to Graphs

This chapter of ncert solutions for class 8 maths helps the students reading numbers, perimeters, and length of square graphs.

Ø  Chapter 16: Playing with numbers

This chapter of ncert solutions for class 8 maths is more of like a fun game where students need to plot 2 digits and 3 digit number from the puzzles.

 Why ncert solutions for class 8 maths at Entancei

Ø  Since maths has been crucial in building strong foundation, we make sure you get the best of all.

Ø  The ncert solutions for class 8 maths are up-to-date and as per the latest NCERT regulations

Ø  The expert provided ncert solutions for class 8 maths is sufficient to score great marks in Examination

Ø  While solving the NCERT class 8 maths the most important think you required is maths formula. Check out all maths formula in one page