Data Science & Machine Learning for COVID-19 using Python

Course Curriculum

Chapter 1: Introduction

Lecture 1: Course Introduction – Welcome to the Course!

Chapter 2: Classifying COVID-19 patients Using Symptoms

Lecture 1: Covid-19 Classifying Symptoms – Introduction Problem Statement

Lecture 2: Classifying Symptoms – Coding Part 1 – Loading the Data

Lecture 3: Classifying Symptoms – Coding Part 2 – Building Model

Lecture 4: Classifying Symptoms – Coding Part 3 – Testing the Model

Lecture 5: 5. Classifying Symptoms – Coding Part 4 – Resampling

Lecture 6: Classifying Symptoms – Coding Part 5 – Assignment Resampling

Chapter 3: COVID-19 Dashboard Using Dash & Plotly

Lecture 1: Dash Library Introduction

Lecture 2: Adding Graphs Using Plotly

Lecture 3: Callbacks

Lecture 4: High-level Overview of the Dashboard

Lecture 5: Adding Indicators

Lecture 6: Creating Content Cards For Overview of the COVID-19 Cases

Lecture 7: Creating Line Plots

Lecture 8: Creating & Adding Table to the Dashboard for Countrywise Statistics

Lecture 9: Adding Geo-plots World Maps

Lecture 10: Adding Geoplot – United States

Lecture 11: Adding Shadowed Lineplots and Dropdowns for Vaccination Data

Lecture 12: Adding Bar Plots with Radio Buttons

Lecture 13: Adding News Cards

Chapter 4: COVID-19 Tweets Classification

Lecture 1: Tweet Classification Problem Statement

Lecture 2: Loading & Pre-processing the Data

Lecture 3: Creating Word Embeddings Using Word2Vec

Lecture 4: Preprocessing Data for Neural Networks

Lecture 5: Defining & Training LSTM Model

Lecture 6: Evaluating & Testing our Model

Lecture 7: Assigment: Build a GRU Model Using Keras!

Chapter 5: COVID-19 Diagnosis and FAQ Chatbot

Lecture 1: Introduction – COVID-19 Chatbot

Lecture 2: Introduction – Whats Rasa

Lecture 3: Why Rasa?

Lecture 4: Rasa – NLU (Natural Language Understanding)

Lecture 5: Creating our First Bot – Rasa NLU

Lecture 6: Rasa NLU Pipeline & Training NLU

Lecture 7: Rasa Core

Lecture 8: Domain

Lecture 9: Conversation Training – Stories

Lecture 10: Conversation Training – Rules

Lecture 11: Forms

Lecture 12: Creating Forms

Lecture 13: Defining Actions

Lecture 14: Adding Custom Validations

Lecture 15: Small Talk & FAQ in Rasa

Lecture 16: Creating Retrieval Intents

Lecture 17: Testing Retrieval Intents

Lecture 18: Connecting the Bot to Telegram

Chapter 6: Detecting Social Distancing using OpenCV & Object Detection

Lecture 1: Social Distancing – Introduction – Problem Statement

Lecture 2: Introduction to Open CV – Part 1

Lecture 3: Introduction to OpenCV – Part 2

Lecture 4: Social Distancing – Coding – Setting up Object Detection

Lecture 5: Applying Non-Max Suppression

Lecture 6: Checking Distance between 2 Persons

Lecture 7: Social Distancing Coding – Detecting Person Class & Marking it

Chapter 7: Face Mask Detection Using Object Detection

Lecture 1: Introduction – FaceMask Detection – Problem Statement

Lecture 2: Loading & Setting Up the Data

Lecture 3: Building Image Classification Model

Lecture 4: Augmenting the Data & Training the Model

Chapter 8: Stanford mRNA degradation Prediction using LSTMs

Lecture 1: Introduction to mRNA Degradation Prediction – Problem Statement

Lecture 2: Approach to Solve the Problem

Lecture 3: Loading & Visualizing the Data

Lecture 4: Preprocessing the Data

Lecture 5: Building & Training the Model

Lecture 6: Evaluating the Model

Lecture 7: Making Predictions Using the Model

Chapter 9: Chest X-Ray Classification

Lecture 1: Introduction – Chest X Ray Classification – Problem

Lecture 2: Approaches to Handle Class Imbalance

Lecture 3: Approaches to Handle Class Imbalance – Part 2

Lecture 4: Introduction to Tensorflow – Keras

Lecture 5: Data Loading & Pre-processing in Keras

Lecture 6: Layers in Keras

Lecture 7: Models in Keras

Lecture 8: Augmenting the Dataset and Using Pre-trained Weights from Tensorflow Hub

Lecture 9: Configuring & Training the Convolution Neural Network Model

Lecture 10: Evaluating the Model & Making Predictions on Unseen Images

Chapter 10: COVID-19 Cases Time Series Analysis Using Facebook Prophet

Lecture 1: Introduction to Time Series Analysis

Lecture 2: Facebook Prophet Model

Lecture 3: Facebook Prophet – Analyst in the loop

Lecture 4: Metrics to measure the performance of TSA Model

Lecture 5: Loading, Visualising Data & Building our Initial Model