- 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
Intermediate
Online
₹ 449 2,299
Quick facts
particular | details | |
---|---|---|
Medium of instructions
English
|
Mode of learning
Self study
|
Mode of Delivery
Video and Text Based
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Course and certificate fees
Fees information
certificate availability
certificate providing authority
The syllabus
Introduction (New Content)
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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