Learn Web Application Development with Machine Learning

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Sachin Kafle

18:01:50

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1. Introduction

1. Introduction.mp4

02:11

2. Setup and Creating Environment

1. Install anaconda on your machine.mp4

07:14

2. Set up environment and Download Machine Learning Libraries.mp4

06:33

3. Introduction to Machine Learning for Absolute Beginners

1. Types of Data in Machine Learning.mp4

08:41

2. Introduction to numpy.mp4

16:41

3. Introduction to pandas.mp4

27:36

4. Train and Test split of data.mp4

16:08

5. Miscellaneous Concept of Machine Learning.mp4

18:59

4. Linear Regression in detail

1.1 all-about-regression.zip

1. Lecture Intro to Linear Regression.mp4

15:51

2. Lecture Learn about OLS [Ordinary Least Squares] algorithm.mp4

28:37

3. Lecture Introduction to working of Linear Regression.mp4

32:09

4. Lecture Introduction to MSE, MAE, RMSE.mp4

12:20

5. Lecture Introduction to R squared.mp4

10:55

6. Tutorial Implement Simple linear regression numerical [calculate best fit line].mp4

27:36

7.1 all-about-regression.zip

7. Workshop Implement Simple Linear Regression.mp4

22:50

8. Lecture Difference between Simple and Multiple Regression.mp4

20:08

9.1 all-about-regression.zip

9. Workshop Implement Multiple Linear Regression.mp4

20:08

10. Workshop Implement Multiple Linear Regression.mp4

30:57

5. Logistic Regression [optional]

1. Lecture Learn about Logistic Regression.mp4

06:01

2. Lecture Learn about hypothetical function [sigmoidlogit function].mp4

18:17

3. Lecture Logistic Math Overview.mp4

11:23

4. Lecture Learn about decision boundary.mp4

10:26

5. Lecture Learn about Cost function of Logistic Regression.mp4

18:51

6. Lecture Learn about Gradient Descent.mp4

05:47

7.1 logistic.zip

7. Workshop Implement Logistic Regression.mp4

18:34

8.1 logistic.zip

8. Workshop final Implement Logistic Regression.mp4

30:12

6. Neural Network in detail

1. Introduction to Neural Networks.mp4

23:54

2. Example of neural network.mp4

13:06

3. Updating the weights [partial differentiation].mp4

18:42

4. Introduction to partial differentiation.mp4

15:30

5. Introduction to the Activation Function.mp4

22:55

6. Why do we need bias in the program.mp4

09:16

7. Why we use regularization in the Neural Network.mp4

03:17

8. Introduction to the gradient descent [review].mp4

15:38

9. Introduction to Stochastic Gradient Descent and Adam Optimizer.mp4

17:47

10. Introduction to mini-batch SGD.mp4

03:15

7. Coding Neural Network from Scratch [optional]

1.1 Artificial-NN-from-scratch.zip

1. Setting up environment and coding single neuron.mp4

15:42

2.1 Artificial-NN-from-scratch.zip

2. Coding neuron layer.mp4

17:46

3. Using dot product to code neuron layer.mp4

09:21

4. Coding dense layer [must know Object Oriented Programming].mp4

21:06

5.1 Activation-function.zip

5. Introduction to Activation Function.mp4

14:56

8. Activation Functions

1.1 Activation-function.zip

1. Implementation of activation function [step and sigmoid].mp4

11:36

2.1 Activation-function.zip

2. Implementation of activation function [tanh and ReLu].mp4

12:42

9. Introduction to Tensorflow and Keras

1. Introduction to Deep Learning.mp4

09:36

2. Tensor Ranks in Tensorflow.mp4

10:12

3. Program Elements in Tensorflow.mp4

08:30

4. Coding in Tensorflow.mp4

11:37

5. Introduction to Keras.mp4

05:05

6. Keras Model [Most Important Video].mp4

12:15

7. Implementing neural network with Keras.mp4

04:56

10. Creating Simple Flask Application (Hello World)

1.1 app.zip

1. Flask Display Hello World.mp4

15:34

11. Web App Implementing Regression using Keras

1.1 app.zip

1. Introduction to the dataset.mp4

04:00

2.1 app.zip

2. Project structure.mp4

03:11

3.1 app.zip

3. Load the data.mp4

08:43

4. Handle Missing values.mp4

04:23

5. Dependent and Independent variable.mp4

04:51

6. Train Test split of data.mp4

05:29

7. Building the model.mp4

17:49

8. Make predictions.mp4

05:58

9. Save the model.mp4

04:46

10. Load model and make predictions.mp4

15:25

11. Finding range min and max value of each attributes.mp4

15:14

12. Making range as dictionary.mp4

02:58

13. Creating an Flask App to test API endpoint.mp4

22:17

14. Testing the model.mp4

05:10

15. Restrictions for Input to the model.mp4

14:23

16. Using POSTMAN to test API endpoint.mp4

12:34

12. Basics of Convolution Neural Network

1. Introduction to Convolution Neural Network.mp4

04:47

2. Kernel or filter.mp4

08:13

3. Example of Kernel.mp4

03:24

4. Stride.mp4

02:44

5. Padding.mp4

03:15

6. Pooling.mp4

03:54

7. Flatten.mp4

01:31

8. Layers of CNN.mp4

01:43

13. Introduction to Transfer Learning

1. What is Transfer Learning.mp4

05:40

2. Traditional ML vs Transfer Learning.mp4

04:51

3. How to use Transfer Learning.mp4

05:19

4. MobileNet.mp4

09:11

5. Architecture of MobileNet.mp4

06:59

14. Web App Implementing CNN (Mobile Net)

1.1 app.zip

1. Introducing Project.mp4

03:37

2. Creating function to check allowed files.mp4

11:35

3. Creating basic route.mp4

02:46

4. Loading all libraries for the model.mp4

06:58

5. Instantiating the model.mp4

09:21

6. The upload_image function.mp4

13:13

7. Checking if image is uploaded.mp4

07:40

8. Checking whether or not image is selected.mp4

08:15

9. Load the image.mp4

09:08

10. Transform the image and store in numpy array.mp4

12:41

11. Make Predictions.mp4

18:35

Description

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

What You'll Learn?

Master Machine Learning on Python
Learn about Regression, Classification tasks
Learn about neural networks
Learn about Deep neural networks with projects
Create web applications using flask
Simple Model building with Scikit-Learn , TensorFlow and Keras
Creating REST API for Machine Learning models
Learn about Exploratory Data Analysis
Implement linear, logistic regression
Implement convolution neural network
Learn about Postman to test API endpoints

Who is this for?

Programmer who wants to learn machine learning by creating web applications

Data Scientists who want to know how to test & monitor their models beyond

Beginner Python programmer

Machine Learning engineer who wants to create fun projects using their basic skills

What You Need to Know?

Any laptop and an internet connection

Basic knowledge of Python programming is must

More details

Description
Machine learning is a branch of artificial intelligence (AI) focused on building applications that learn from data and improve their accuracy over time without being programmed to do so.
In data science, an algorithm is a sequence of statistical processing steps. In machine learning, algorithms are 'trained' to find patterns and features in massive amounts of data in order to make decisions and predictions based on new data. The better the algorithm, the more accurate the decisions and predictions will become as it processes more data.
Machine learning has led to some amazing results, like being able to analyze medical images and predict diseases on-par with human experts.
Google's AlphaGo program was able to beat a world champion in the strategy game go using deep reinforcement learning.
Machine learning is even being used to program self driving cars, which is going to change the automotive industry forever. Imagine a world with drastically reduced car accidents, simply by removing the element of human error.
Topics covered in this course:
1. Warm-up with Machine learning Libraries:Â numpy, pandas
2. Implement Machine Learning algorithms:Â Linear, Logistic Regression
3. Implement Neural Network from scratch
4. Introduction to Tensorflow and Keras
5. Start with simple "Hello World" flask application
6. Create flask application to implement linear regression and test the API's endpoints
7. Implement transfer learning and built an app to implement image classification
Who this course is for:
Programmer who wants to learn machine learning by creating web applications
Data Scientists who want to know how to test & monitor their models beyond
Beginner Python programmer
Machine Learning engineer who wants to create fun projects using their basic skills

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Machine Learning

Sachin Kafle

Instructor's Courses

Sachin Kafle is a Python and Java developer, ethical hacker and social activist. His interest's lies in software development and integration practices in the areas of computation, quantitative fields of trade. His technological interests include Python, C, Java, C# programming. He has been involved in teaching since 2013.Sachin is a engineer of Computer Science (B.E. Computer Science). He is also an instructor on his previously made some geek Youtube channel. He has been giving free classes mostly for students who have not been able to pay for expensive classes in his country.

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