Machine Learning: Learn By Building Web Apps In Python

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
Requirements
Any laptop and an internet connection
Basic knowledge of Python programming is must
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: numpy, pandas2. Implement Machine Learning algorithms: Linear, Logistic Regression3. Implement Neural Network from scratch4. Introduction to Tensorflow and Keras5. Start with simple "Hello World" flask application6. Create flask application to implement linear regression and test the API's endpoints7. Implement transfer learning and built an app to implement image classification

Overview

Section 1: Introduction

Lecture 1 Introduction

Section 2: Setup and Creating Environment

Lecture 2 Install anaconda on your machine

Lecture 3 Set up environment and Download Machine Learning Libraries

Section 3: Introduction to Machine Learning for Absolute Beginners

Lecture 4 Types of Data in Machine Learning

Lecture 5 Introduction to numpy

Lecture 6 Introduction to pandas

Lecture 7 Train and Test split of data

Lecture 8 Miscellaneous Concept of Machine Learning

Section 4: Linear Regression in detail

Lecture 9 Lecture: Intro to Linear Regression

Lecture 10 Lecture: Learn about OLS [Ordinary Least Squares] algorithm

Lecture 11 Lecture: Introduction to working of Linear Regression

Lecture 12 Lecture: Introduction to MSE, MAE, RMSE

Lecture 13 Lecture: Introduction to R squared

Lecture 14 Tutorial: Implement Simple linear regression numerical [calculate best fit line]

Lecture 15 Workshop: Implement Simple Linear Regression

Lecture 16 Lecture: Difference between Simple and Multiple Regression

Lecture 17 Workshop: Implement Multiple Linear Regression

Lecture 18 Workshop: Implement Multiple Linear Regression

Section 5: Logistic Regression [optional]

Lecture 19 Lecture: Learn about Logistic Regression

Lecture 20 Lecture: Learn about hypothetical function [sigmoid/logit function]

Lecture 21 Lecture: Logistic Math Overview

Lecture 22 Lecture: Learn about decision boundary

Lecture 23 Lecture: Learn about Cost function of Logistic Regression

Lecture 24 Lecture: Learn about Gradient Descent

Lecture 25 Workshop: Implement Logistic Regression

Lecture 26 Workshop final: Implement Logistic Regression

Section 6: Neural Network in detail

Lecture 27 Introduction to Neural Networks

Lecture 28 Example of neural network

Lecture 29 Updating the weights [partial differentiation]

Lecture 30 Introduction to partial differentiation

Lecture 31 Introduction to the Activation Function

Lecture 32 Why do we need bias in the program

Lecture 33 Why we use regularization in the Neural Network

Lecture 34 Introduction to the gradient descent [review]

Lecture 35 Introduction to Stochastic Gradient Descent and Adam Optimizer

Lecture 36 Introduction to mini-batch SGD

Section 7: Coding Neural Network from Scratch [optional]

Lecture 37 Setting up environment and coding single neuron

Lecture 38 Coding neuron layer

Lecture 39 Using dot product to code neuron layer

Lecture 40 Coding dense layer [must know Object Oriented Programming]

Lecture 41 Introduction to Activation Function

Section 8: Activation Functions

Lecture 42 Implementation of activation function [step and sigmoid]

Lecture 43 Implementation of activation function [tanh and ReLu]

Section 9: Introduction to Tensorflow and Keras

Lecture 44 Introduction to Deep Learning

Lecture 45 Tensor Ranks in Tensorflow

Lecture 46 Program Elements in Tensorflow

Lecture 47 Coding in Tensorflow

Lecture 48 Introduction to Keras

Lecture 49 Keras Model [Most Important Video]

Lecture 50 Implementing neural network with Keras

Section 10: Creating Simple Flask Application ("Hello World")

Lecture 51 Flask: Display Hello World

Section 11: Web App: Implementing Regression using Keras

Lecture 52 Introduction to the dataset

Lecture 53 Project structure

Lecture 54 Load the data

Lecture 55 Handle Missing values

Lecture 56 Dependent and Independent variable

Lecture 57 Train Test split of data

Lecture 58 Building the model

Lecture 59 Make predictions

Lecture 60 Save the model

Lecture 61 Load model and make predictions

Lecture 62 Finding range: min and max value of each attributes

Lecture 63 Making range as dictionary

Lecture 64 Creating an Flask App to test API endpoint

Lecture 65 Testing the model

Lecture 66 Restrictions for Input to the model

Lecture 67 Using POSTMAN to test API endpoint

Section 12: Basics of Convolution Neural Network

Lecture 68 Introduction to Convolution Neural Network

Lecture 69 Kernel or filter

Lecture 70 Example of Kernel

Lecture 71 Stride

Lecture 72 Padding

Lecture 73 Pooling

Lecture 74 Flatten

Lecture 75 Layers of CNN

Section 13: Introduction to Transfer Learning

Lecture 76 What is Transfer Learning

Lecture 77 Traditional ML vs Transfer Learning

Lecture 78 How to use Transfer Learning

Lecture 79 MobileNet

Lecture 80 Architecture of MobileNet

Section 14: Web App: Implementing CNN (Mobile Net)

Lecture 81 Introducing Project

Lecture 82 Creating function to check allowed files

Lecture 83 Creating basic route

Lecture 84 Loading all libraries for the model

Lecture 85 Instantiating the model

Lecture 86 The "upload_image" function

Lecture 87 Checking if image is uploaded

Lecture 88 Checking whether or not image is selected

Lecture 89 Load the image

Lecture 90 Transform the image and store in numpy array

Lecture 91 Make Predictions

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