Hello sir mai peshawar se hu mai ne app ke bohat courses dekhe hai (OS,SE,COMPILER CONSTRUCTION,ALGHORITHMS) our many more our muji bohat fayda howa welldone sir great job our sir aghar app (ICT) par be banaye tu bohat accha hoga ❤❤❤
sir kal 10 baje mera ai/ml ka paper hai maine 2 ghante pehle ye topic search kiya aur aapka video ni aaya aur aapne aaj hi is topic pe video daal di. thank you so much!
sir ap machine learning ka bhut acha pdha rhe hai. but ap isme defination theory ye sb bhi likhwate and coding like scipy and sklearn bhi krate.😊😊😊 to complete machine learning ho jate. but no problem sir wo hm khud se pdh lenge. thanku sir ❤❤❤❤❤
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
Please provide a playlist for the new aktu subject: Mathematical foundation in AI,ML and Data Science -KAI051.I have my exam in two weeks so if you could provide a one shot or a playlist in 2 week then I will be grateful. 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
Hello sir mai peshawar se hu mai ne app ke bohat courses dekhe hai (OS,SE,COMPILER CONSTRUCTION,ALGHORITHMS) our many more our muji bohat fayda howa welldone sir great job our sir aghar app (ICT) par be banaye tu bohat accha hoga ❤❤❤
sir kal 10 baje mera ai/ml ka paper hai maine 2 ghante pehle ye topic search kiya aur aapka video ni aaya aur aapne aaj hi is topic pe video daal di. thank you so much!
east aur west varun bhayia is best😍😍😍😍😍
sir ap machine learning ka bhut acha pdha rhe hai. but ap isme defination theory ye sb bhi likhwate and coding like scipy and sklearn bhi krate.😊😊😊
to complete machine learning ho jate.
but no problem sir wo hm khud se pdh lenge.
thanku sir ❤❤❤❤❤
Ap ka way of teaching buht achha hai sir
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
Watching this 3 hrs before exam :)
Thanku so much sir...❤
Thank you so much sir
Does decision forest comes under bootstrap aggregating
what is difference between random forest and bagging
please make video on deep learning
Please provide a playlist for the new aktu subject: Mathematical foundation in AI,ML and Data Science -KAI051.I have my exam in two weeks so if you could provide a one shot or a playlist in 2 week then I will be grateful.
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
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
th-cam.com/play/PLxCzCOWd7aiHcmS4i14bI0VrMbZTUvlTa.html&si=SD9jpkTrNk9zThvY
helllo Sir Notes Provide kijai na aur Daily Video Upload Kijai Na
Thanku............................................
👏
Hello sir plz advanced python
Apka channel kaafi Kamm video le rha hai aaj kal
Plz response