The Complete Healthcare Artificial Intelligence Course

BY
Udemy

Lavel

Intermediate

Mode

Online

Fees

₹ 499 2499

Quick Facts

particular details
Medium of instructions English
Mode of learning Self study
Mode of Delivery Video and Text Based

Course and certificate fees

Fees information
₹ 499  ₹2,499
certificate availability

Yes

certificate providing authority

Udemy

The syllabus

Introduction (New Content)

  • 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 (New Content)

  • 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 (New Content)

  • 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 (New Content)

  • 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 (New Content)

  • Introduction
  • Importing data and Analysing 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 (New Content)

  • 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 (New content)

  • 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 (New Content)

  • Introduction to the project
  • Problem Analysis
  • Importing library and data
  • Analysing 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

Thank you

  • Extra Link
  • Thank you

Bonus- Extra projects outside the healthcare scope (New Content)

  • Introduction to the project
  • Bonus project dataset
  • Deep feedforward networks
  • Importing library and data
  • Visualizing geolocation data
  • Analysing Data
  • Handling missing data and anomalies in Python
  • Temporal features
  • Geolocation features
  • Feature Scaling
  • Model building
  • Analysing Results
  • Summary of the project

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