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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تولید کننده: Udemy-Training