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Yarpiz Yarpiz
Scientific source code tutorial tutor
4,588
Learners
6
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11
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IT
Teaching on Alison since Aug 2021
Yarpiz is an online education platform for producing and providing educational content for both academic enthusiasts and professionals so that they can improve their knowledge and skills to make progress in their careers. The different types of courses and educational content that Yarpiz include disciplines ranging from Computer Science, Artificial Intelligence and Machine Learning to Engineering Optimization, Operational Research, Control Engineering and more. The goal is to help achieve educational equity, so that people in different geographical boundaries, cultures, languages, ethnicities and other differences can equally have easy access to quality education content. Yarpiz presents a diverse range of specialized courses, covering topics such as Particle ...
🏆 Top Course by this Course Creator
Principal Component Analysis in Python and MATLAB
836 Learners already enrolled
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Free Online Courses by Yarpiz Yarpiz
Principal Component Analysis in Python and MATLAB
1.5-3 hrs 836 learners
By Yarpiz Yarpiz
You Will Learn How To

By the end of this course, you will be able to:

  • Describe the theory and concepts of principal component analysis (PCA)
  • Define the mathematics behind PCA
  • Compute required random data to implement PCA without any PCA-specific function in Python and MATLAB
  • Analyze PCA outcome by plotting the data accordingly in both Python and MATLAB
  • Calculate and implement PCA on Iris datasets in Python using scikit-learn library
  • Apply PCA on handwritten digits datasets in Python using scikit-learn library
  • Evaluate PCA on Iris datasets in MATLAB using statistics and a machine learning toolbox
  • Solve PCA for handwritten digits datasets in MATLAB using statistics and a machine learning toolbox
  • Discuss the results of PCA implementation in both Python and MATLAB by plotting the data
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CERTIFICATE
Data Analysis
Principal Component Analysis in Python and MATLAB
1.5-3 hrs
836 enrolled
Practical Genetic Algorithms in Python and MATLAB
4-5 hrs 1,486 learners
By Yarpiz Yarpiz
You Will Learn How To

By the end of this course, you will be able to:

  • Describe what Crossover and Mutation in Genetic Algorithms is and which methods are used to implement them
  • Calculate and implement Real-Coded Genetic Algorithm in Python from scratch and step by step
  • Outline Crossover and Mutation along with their different methods in the main loop of GA in MATLAB and Python
  • Evaluate and Fine tuning the final GA codes in MATLAB and Python to get better results
  • Define Evolutionary Algorithms and Genetic Algorithms (GAs)
  • Discuss the selection phase in GAs and the different methods along with the Merging, Sorting, and Selection phase of GA
  • Compute and implement both Binary and Real-Coded Genetic Algorithms in MATLAB from scratch and step by step
  • Arrange the initialization phase of Binary and Real-Coded Genetic Algorithm in MATLAB and only Real-Coded GA in Python
  • Solve the selection phase in the main loop of GA in MATLAB and Python using Random or Roulette Wheel Selection methods
  • Apply and perform the Merging, Sorting, and Selection phase of GA both in Python and MATLAB
  • Evaluate and run Binary and Real Coded GA in MATLAB and run and get results from Real Coded GA in Python
  • Recognize and implement the Main Loop of Iteration in Binary and Real-Coded GA in MATLAB and Real-Coded GA in Python
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CERTIFICATE
Engineering
Practical Genetic Algorithms in Python and MATLAB
4-5 hrs
1486 enrolled
Numerical Root Finding Methods in Python and MATLAB
1.5-3 hrs 450 learners
By Yarpiz Yarpiz
You Will Learn How To

By the end of this course, you will be able to:

  • Explain the fundamental concepts of numerical root finding, starting with the Bisection Method
  • Apply the Bisection Method to approximate solutions for various equations
  • Evaluate the Bisection Method in MATLAB for precise root determination
  • Apply Python for practical implementation of the Secant Method in root finding
  • Calculate the Secant Method using MATLAB, enhancing your computational skills
  • Recognize the theoretical framework of the Newton's Method for root approximation
  • Generate Python code to apply Newton's Method for solving complex equations
  • Apply Newton's Method to MATLAB, broadening your numerical problem-solving capabilities
  • Evaluate and discuss the convergence behavior and efficiency of these numerical techniques in both Python and MATLAB
  • Generate Python scripts to implement the Bisection Method effectively
  • Analyze the principles and procedures underlying the Secant Method
  • Compare and contrast the Bisection and Secant Methods, understanding their advantages and limitations
course logo
CERTIFICATE
Engineering
Numerical Root Finding Methods in Python and MATLAB
1.5-3 hrs
450 enrolled
Linear Programming in MATLAB (With Solution to Transportation Problem)
1.5-3 hrs 1,265 learners
By Yarpiz Yarpiz
You Will Learn How To

By the end of this course, you will be able to:

  • Describe the fundamental principles of Linear Programming and its applications
  • Solve Linear Programming problems using vectors and matrices
  • Analyze and solve Linear Programming problems effectively using MATLAB
  • Define the structure and requirements of the Transportation Problem
  • Discuss step-by-step procedures for solving Transportation Problems in MATLAB
  • Recognize the significance of mathematical optimization in real-world scenarios
  • Apply problem-solving skills to tackle complex logistical and optimization challenges
  • Evaluate and compare various solutions for Transportation Problems
  • Plan and implement linear programming techniques for resource allocation
  • Relate the acquired knowledge to practical applications in supply chain management, engineering, and operations research
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CERTIFICATE
Programming
Linear Programming in MATLAB (With Solution to Transportation Problem)
1.5-3 hrs
1265 enrolled
Runge-Kutta Method in Python and MATLAB
1.5-3 hrs 228 learners
By Yarpiz Yarpiz
You Will Learn How To

By the end of this course, you will be able to:

  • Recognize the role of differential equations in modeling real-world scenarios in science and engineering
  • Generate custom implementations of numerical methods for solving differential equations
  • Apply the Runge-Kutta Method to numerically solve ordinary differential equations
  • Solve system-level nonlinear differential equations using the Runge-Kutta method
  • Discuss the theoretical foundations of the Runge-Kutta method for numerical solutions of ordinary differential equations
  • Evaluate and compare results obtained from implementing the Runge-Kutta method in Python and MATLAB
  • Explain the importance of numerical methods, particularly Runge-Kutta, in computational applications
  • Assess the accuracy and efficiency of Runge-Kutta implementations in solving specific problems
  • Relate Python and MATLAB programming skills to numerical computation in the context of differential equations
  • Outline the steps involved in implementing the Runge-Kutta method for various differential equation problems
  • Describe the process of implementing the Runge-Kutta 4th order algorithm in both Python and MATLAB
  • Analyze and simulate a Lotka-Volterra (Predator-Prey) system's dynamics through numerical solutions
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CERTIFICATE
Programming
Runge-Kutta Method in Python and MATLAB
1.5-3 hrs
228 enrolled
Particle Swarm Optimization in MATLAB
1.5-3 hrs 323 learners
By Yarpiz Yarpiz
You Will Learn How To

By the end of this course, you will be able to:

  • Explain the foundational principles of Particle Swarm Optimization (PSO)
  • Solve and implement a basic particle swarm optimization algorithm in MATLAB
  • Identify the key parameters and mathematical equations underlying the PSO technique
  • Summarize the key features of swarm intelligence in optimization
  • Explain how to convert a PSO MATLAB script implementation into a reusable function
  • Illustrate the steps involved in updating velocity and position in the PSO main loop
  • Apply boundary constraints to improve PSO performance
  • Calculate velocity limits to control particle movement
  • Apply and utilize the constriction coefficient to set PSO parameters
  • Analyze the effects of parameter configurations on PSO convergence
  • Evaluate multidimensional optimization problem solutions found by PSO
  • Plan and execute the translation of PSO code from MATLAB to other programming languages
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CERTIFICATE
Programming
Particle Swarm Optimization in MATLAB
1.5-3 hrs
323 enrolled
Showing 1 - 6 of 6
Learner Reviews & Feedback For
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PIYUSH P.
the course was great with easy explanation. it is a great course for those who have no idea what genetic algorithm is
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Very informative course , suggested for MATLAB aspiring learners
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