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دسته بندی دوره ها

Learn Web Application Development with Machine Learning

سرفصل های دوره

Learn basic to advanced Machine Learning algorithms by creating web applications using Flask!!


1. Introduction
  • 1. Introduction

  • 2. Setup and Creating Environment
  • 1. Install anaconda on your machine
  • 2. Set up environment and Download Machine Learning Libraries

  • 3. Introduction to Machine Learning for Absolute Beginners
  • 1. Types of Data in Machine Learning
  • 2. Introduction to numpy
  • 3. Introduction to pandas
  • 4. Train and Test split of data
  • 5. Miscellaneous Concept of Machine Learning

  • 4. Linear Regression in detail
  • 1.1 all-about-regression.zip
  • 1. Lecture Intro to Linear Regression
  • 2. Lecture Learn about OLS [Ordinary Least Squares] algorithm
  • 3. Lecture Introduction to working of Linear Regression
  • 4. Lecture Introduction to MSE, MAE, RMSE
  • 5. Lecture Introduction to R squared
  • 6. Tutorial Implement Simple linear regression numerical [calculate best fit line]
  • 7.1 all-about-regression.zip
  • 7. Workshop Implement Simple Linear Regression
  • 8. Lecture Difference between Simple and Multiple Regression
  • 9.1 all-about-regression.zip
  • 9. Workshop Implement Multiple Linear Regression
  • 10. Workshop Implement Multiple Linear Regression

  • 5. Logistic Regression [optional]
  • 1. Lecture Learn about Logistic Regression
  • 2. Lecture Learn about hypothetical function [sigmoidlogit function]
  • 3. Lecture Logistic Math Overview
  • 4. Lecture Learn about decision boundary
  • 5. Lecture Learn about Cost function of Logistic Regression
  • 6. Lecture Learn about Gradient Descent
  • 7.1 logistic.zip
  • 7. Workshop Implement Logistic Regression
  • 8.1 logistic.zip
  • 8. Workshop final Implement Logistic Regression

  • 6. Neural Network in detail
  • 1. Introduction to Neural Networks
  • 2. Example of neural network
  • 3. Updating the weights [partial differentiation]
  • 4. Introduction to partial differentiation
  • 5. Introduction to the Activation Function
  • 6. Why do we need bias in the program
  • 7. Why we use regularization in the Neural Network
  • 8. Introduction to the gradient descent [review]
  • 9. Introduction to Stochastic Gradient Descent and Adam Optimizer
  • 10. Introduction to mini-batch SGD

  • 7. Coding Neural Network from Scratch [optional]
  • 1.1 Artificial-NN-from-scratch.zip
  • 1. Setting up environment and coding single neuron
  • 2.1 Artificial-NN-from-scratch.zip
  • 2. Coding neuron layer
  • 3. Using dot product to code neuron layer
  • 4. Coding dense layer [must know Object Oriented Programming]
  • 5.1 Activation-function.zip
  • 5. Introduction to Activation Function

  • 8. Activation Functions
  • 1.1 Activation-function.zip
  • 1. Implementation of activation function [step and sigmoid]
  • 2.1 Activation-function.zip
  • 2. Implementation of activation function [tanh and ReLu]

  • 9. Introduction to Tensorflow and Keras
  • 1. Introduction to Deep Learning
  • 2. Tensor Ranks in Tensorflow
  • 3. Program Elements in Tensorflow
  • 4. Coding in Tensorflow
  • 5. Introduction to Keras
  • 6. Keras Model [Most Important Video]
  • 7. Implementing neural network with Keras

  • 10. Creating Simple Flask Application (Hello World)
  • 1.1 app.zip
  • 1. Flask Display Hello World

  • 11. Web App Implementing Regression using Keras
  • 1.1 app.zip
  • 1. Introduction to the dataset
  • 2.1 app.zip
  • 2. Project structure
  • 3.1 app.zip
  • 3. Load the data
  • 4. Handle Missing values
  • 5. Dependent and Independent variable
  • 6. Train Test split of data
  • 7. Building the model
  • 8. Make predictions
  • 9. Save the model
  • 10. Load model and make predictions
  • 11. Finding range min and max value of each attributes
  • 12. Making range as dictionary
  • 13. Creating an Flask App to test API endpoint
  • 14. Testing the model
  • 15. Restrictions for Input to the model
  • 16. Using POSTMAN to test API endpoint

  • 12. Basics of Convolution Neural Network
  • 1. Introduction to Convolution Neural Network
  • 2. Kernel or filter
  • 3. Example of Kernel
  • 4. Stride
  • 5. Padding
  • 6. Pooling
  • 7. Flatten
  • 8. Layers of CNN

  • 13. Introduction to Transfer Learning
  • 1. What is Transfer Learning
  • 2. Traditional ML vs Transfer Learning
  • 3. How to use Transfer Learning
  • 4. MobileNet
  • 5. Architecture of MobileNet

  • 14. Web App Implementing CNN (Mobile Net)
  • 1.1 app.zip
  • 1. Introducing Project
  • 2. Creating function to check allowed files
  • 3. Creating basic route
  • 4. Loading all libraries for the model
  • 5. Instantiating the model
  • 6. The upload_image function
  • 7. Checking if image is uploaded
  • 8. Checking whether or not image is selected
  • 9. Load the image
  • 10. Transform the image and store in numpy array
  • 11. Make Predictions
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    تاریخ انتشار: 22 دی 1401
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