Bagging/Bootstrap Aggregating in Machine Learning with examples

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00:00 – Intro
00:16 – Bagging/Bootstrap
02:28 – Ensemble learning

Bagging, short for Bootstrap Aggregating, is an ensemble learning technique used to improve the stability and accuracy of machine learning models by reducing variance. It involves creating multiple versions of a predictor and using these to get an aggregated result.

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Mrahmad

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Sir please cover this topics also ❤thank you sir
Introduction to Microprocessor, Components of a Microprocessor: 4 8%
Registers, ALU and control & timing, System bus (data, address and control
bus), Microprocessor systems with bus organization
2 Microprocessor Architecture and Operations, Memory, I/O devices, 4 7%
Memory and I/O operations
3 8085 Microprocessor Architecture, Address, Data And Control Buses, 8085 6 12%
Pin Functions, Demultiplexing of Buses, Generation Of Control Signals,
Instruction Cycle, Machine Cycles, T-States, Memory Interfacing
4 Assembly Language Programming Basics, Classification of Instructions, 6 13%
Addressing Modes, 8085 Instruction Set, Instruction And Data Formats,
Writing, Assembling & Executing A Program, Debugging The Programs
5 Writing 8085 assembly language programs with decision, making and 6 12%
looping using data transfer, arithmetic, logical and branch instructions
6 Stack & Subroutines, Developing Counters and Time Delay Routines, Code 6 13%
Conversion, BCD Arithmetic and 16-Bit Data operations

Funnyfacts

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SYLLABUS:Descriptive Statistics: Diagrammatic representation of data, measures of central tendency, measures of dispersion, measures of skewness and kurtosis, correlation, inference procedure for correlation coefficient, bivariate correlation, multiple correlations, linear regression and its inference procedure, multiple regression.

Probability: Measures of probability, conditional probability, independent event, Bayes' theorem, random variable, discrete and continuous probability distributions, expectation and variance, markov inequality, chebyshev's inequality, central limit theorem.

Inferential Statistics: Sampling & Confidence Interval, Inference & Significance. Estimation and Hypothesis Testing, Goodness of fit, Test of Independence, Permutations and Randomization Test, t- test/z-test (one sample, independent, paired), ANOVA, chi-square.

Linear Methods for Regression Analysis: multiple regression analysis, orthogonalization by Householder transformations (QR); singular value decomposition (SVD); linear dimension reduction using principal component analysis (PCA).

Pseudo-Random Numbers: Random number generation, Inverse-transform, acceptance-rejection,

transformations, multivariate probability calculations. Monte Carlo Integration: Simulation and Monte Carlo integration, variance reduction, Monte Carlo hypothesis testing, antithetic variables/control variates, importance sampling, stratified sampling Markov chain Monte Carlo (McMC): Markov chains; Metropolis-Hastings algorithm; Gibbs

08

08

sampling; convergence

08

IV

Vector Spaces- Vector Space, Subspace, Linear Combination, Linear Independence, Basis, Dimension, Finding a Basis of a Vector Space, Coordinates, Change of Basis

08

Inner Product Spaces- Inner Product, Length, Orthogonal Vectors, Triangle Inequality, Cauchy- Schwarz Inequality, Orthonormal (Orthogonal) Basis, Gram-Schmidt Process

V

Linear Transformations- Linear Transformations and Matrices for Linear Transformation, Kernel

and Range of a Linear Transformations, Change of Basis Eigenvalues and Eigenvectors Definition of Eigenvalue and Eigenvector, Diagonalization Symmetric Matrices and Orthogonal Diagonalization

kartikjhirwar

Sir make vedio on design and analysis of algorithms course and if you have any refference vedio then put the link below. It's important

SantoshYadav-xbhg

helllo Sir Notes Provide kijai na aur Daily Video Upload Kijai Na

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Apka channel kaafi Kamm video le rha hai aaj kal

dharampreetsingh

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