Building Quantity Estimation & Bbs With Excel For Beginners
QUANITY ESTIMATION, BAR BENDING SCHEDULE , THUMB RULE FOR QUICK ESTIMATION, BRICK WORKS & UNDERGROUND WATER TANK COSTING
Created by Akshay K - B.E [ Civil Engineering ] MTech [ Structural Engineering ]
Students: 2479, Price: Free
This Courses is a Beginner Course for those students who want to choose Quantity Estimation as their Subject & Become a Professional in this Field.
The course is prepared with the intention that the learners will understand the basic of quantity Estimation .
After the Course completion the learner can try to apply these concepts practically in their Homes or near by buildings and get a Rough Estimate of the total Building cot and how much Cement bags, Sand, Coarse Aggregate & Water has been used in the structure .
We all know that an "Estimate" is a rough calculation of the quantities of various items of work,like cement , Sand , Coarse aggregate and the expenses likely to be incurred for the construction projects that one may undertake to complete successfully. Hence, the total of these probable expenses to be incurred on the work is known as the estimated cost of the work. The estimated cost of a work is actually a close approximation of its actual cost.
Thumb Rules for Quick Calculations of Shuttering & Concrete Materials
Thumb rule for finding Footing Shuttering Quantity with footing Concrete Quantity.
Thumb rule for Finding Beam, Column & Slab Shuttering & Centering Quantity with Concrete Quantity.
Estimation & Costing of Under Ground Water tank of 50 Kl ( EXCEL CALCULATIONS )
Introduction to Calculus 2: Area Estimation
Learn the basic method of area estimation: Riemann Sum
Created by Gina Chou - Physics PhD candidate
Students: 603, Price: Free
HOW THIS COURSE WORK:
This course, Introduction to Calculus 2: Area Estimation, includes the first section you will learn in Calculus 2, including video, notes from whiteboard during lectures, and practice problems (with solutions!). I also show every single step in examples and theorems. The course is organized into the following topics:
Area Estimation (Riemann Sum)
Limit of a Riemann Sum (Signed Area)
CONTENT YOU WILL GET INSIDE EACH SECTION:
Videos: I start each topic by introducing and explaining the concept. I share all my solving-problem techniques using examples. I show a variety of math issue you may encounter in class and make sure you can solve any problem by yourself.
Notes: In each section, you will find my notes as downloadable resource that I wrote during lectures. So you can review the notes even when you don't have internet access (but I encourage you to take your own notes while taking the course!).
Assignments: After you watch me doing some examples, now it's your turn to solve the problems! Be honest and do the practice problems before you check the solutions! If you pass, great! If not, you can review the videos and notes again.
BONUS #1: Downloadable lecture notes so you can review the lectures without having a device to watch/listen.
BONUS #2: Step-by-step guide to help you solve problems.
BONUS #3: A secret Facebook group for you to ask questions and discuss with your classmates.
See you inside the course!
- Gina :)
Maximum Likelihood Estimation- An Introduction
Introduction to Maximum Likelihood Estimation
Created by Zeeshan Ahmad - Machine Learning and Statistical Signal Processing
Students: 330, Price: Free
The purpose of the maximum likelihood estimation (MLE) is to find or tune the parameters of the distribution in a way to explain the data OR how to learn the distribution/ model parameters from this data?
The purpose of fitting distribution to this data is to find the parameters of the distribution such that using those parameters we can extract more data of similar nature.
Maximum likelihood will look for the values of parameters that maximizes the likelihood function that is the value of parameters that says that this data is most likely to belong this distribution.
In this course, students will learn about the fundamental concept of Maximum likelihood Estimation, Parameters of Maximum Likelihood Estimation, Derivations of the Parameters, Solved Example on Maximum Likelihood Estimation. By the end of this course, students will be able to derive the parameters from the distribution. For brevity, we are only covering Gaussian distribution in this course since Gaussian is the most commonly used distribution.
Following is the breakdown of the course.
1. Events, outcomes and probability.
2. Concept of Maximum Likelihood Estimation.
3. Parameters of Maximum Likelihood Estimation.
4. Calculations of Parameters.
5. Multivariate Gaussian Distribution.
6. Derivation of Parameters.
7. Numerical Example on MLE.
8. Dealing with arrays in Python.
9. Plotting and visualization.