AI for Healthcare Training

Introduction

• Course Structure
• How To Make The Most Out Of This Course
• AI in Healthcare
• What is Neuron
• What is Deep Learning
• What is ANN
• What is keras
• Introduction to Pandas Part 1
• Introduction to Pandas Part 2
• Data Visualization with Pandas
• Data Preprocessing by Pandas
• How to install Anaconda
• Important terms in Neural Network

Activation Function

• What is activation function
• What is sigmoid function
• What is tanh function
• What is Rectified Linear Unit function
• What is Leaky ReLU function
• What is The Exponential Linear Unit Function
• What is The Swish function
• What is The softmax function
• Time to code all the activation functions

DNA Classification Project

• Introduction to DNA Classifier
• Importing library and data
• Showing data
• Generating a DNA sequence
• Splitting the dataset into training test and test set
• Scoring method and results
• Summary of the project

Heart Disease Classification Project

• Introduction to the project
• Important Parameters
• Objective of this project
• Importing library and data
• Exploratory analysis
• Handling missing data in Python
• Data scaling
• Data visualization
• Splitting training set into test set and Evaluating the model
• Summary of the project

Diagnosing Coronary Artery Disease Project

• Introduction
• Importing data and Analyzing data
• Fixing missing data
• Splitting the dataset into training test and test set
• Training Neural Network
• A comparison of categorical and binary problem
• Summary of the project

Breast Cancer Detection Project

• Introduction to the project
• Importing library and data and Preprocessing data
• Data visualization
• Understanding Machine Learning Algorithm
• Training model
• Make a Prediction
• Summary of the project

Predicting Diabetes with Multilayer Perceptrons Project

• Introduction
• Importing datas and libraries
• Visualizing data
• Handling missing values
• Data standardization
• Splitting the data into training, testing, and validation sets
• Model building
• Model compilation
• Model training
• Testing accuracy
• Confusion matrix
• ROC curve
• Further improvement
• Summary of the project

Medical Treatment Project

• Introduction to the project
• Problem Analysis
• Importing library and data
• Analyzing Data
• Preprocessing data
• Removing missing data
• Creating training, test and validation data
• Visualizing data
• Building a random model
• Confusion, Precision and Recall Matrix
• One-hot Encoding
• Response Encoding- Theory
• Response Encoding- Implementation Part 1
• Response Encoding- Implementation Part 2
• Response Encoding- Implementation Part 3
• Evaluating variation column Part 1
• Evaluating variation column Part 2
• Evaluating text column Part 1
• Evaluating text column Part 2
• Data Preparation for machine learning model
• Combine all 3 feature together
• Naive Bayes- Implementation Part 1
• Naive Bayes- Implementation Part 2
• K Nearest Neighbour Classification Implementation
• Logistic Regression Implementation
• Logistic Regression Implementation without balancing data Part 1
• Logistic Regression Implementation without balancing data Part 2
• Linear Support Vector Machines
• Fixing mistakes (Please watch)
• RF with Response Coding Implementation Part 1
• RF with Response Coding Implementation Part 2
• Random Forest Classifier Implementation
• Stacking model Implementation
• Maximum voting Classifier