Artificial Intelligence Fundamentals

• Introduction of Trainosys
• Courses Trainosys Offered
• Trainer’s Profile

Course Expectancy Survey

• Introduction of Artificial Intelligence 

• Weak Ai vs Strong AI
• Generative Models

Artificial Intelligence Applications

08:31 AM – 09:30 AM 

• Introduction of Machine Learning 

• How does ML work?
• Machine Learning vs Deep Learning vs Neural Network
  
  

• Machine Learning Platforms
• Machine Learning Use cases
• Machine Learning Fields (NLP, Geospatial, etc)

• Validation of Required Libraries & Platforms to install
• Machine Learning vs Data Science
• Machine Learning Pipeline 
• Data Science Pipeline

• Learning by Doing – Supervised Model – Classification Use Case 1

• Data Preparation

• Data Cleaning 
• Data Visualization
• Data Ingestion
• Data Transformation & Validation 
• Feature Engineering
• Data Splitting

• Learning by Doing – Supervised Model – Classification Use Case 1
  

• Model Building Choosing the Right Model

•  Training Optimization
• Model Performance Evaluation
• Model Validation

• Take Home Exercise Discussion

• Learning by Doing – Supervised Model – Classification Use Case 1

• Exporting and Importing a model in .pkl or .sav

• ML Supervised Model – Classification Part 2 Recap

• Learning by Doing – Supervised Model – Regression Use Case 2
  

• Data Preparation:

•  Data Cleaning
• Data Visualization
• Data Integration

• Learning by Doing – Supervised Model – Regression Use Case 2 

• Data Preparation:

• Data Transformation & Validation
• Feature Engineering
• Data Splitting

• Learning by Doing – Supervised Model – Regression Use Case 2

• Model Building Choosing the Right Model

• Training Optimization
• Model Performance Evaluation
• Model Validation

• ML Supervised Model – Regression Use Case 2 

• Exporting and Importing a Model in .pkl or .sav

• Take Home Exercise Discussion

• Learning by Doing – Unsupervised Model – Clustering Use Case 3

• Data Preparation:

• Data Transformation & Validation
• Feature Engineering
• Data Splitting 

• Learning by Doing – Unsupervised Model – Clustering Use Case 3

• Model Building: Choosing the Right Model/Algorithm of Clustering

• Training Optimization
• Model Performance Evaluation
• Model Validation

• Learning by Doing – Unsupervised Model – Clustering Use Case 3

• Exporting and Importing a Model in .pkl or .sav

• What is Deep Learning?

• Deep Learning vs Machine Learning
• Deep Learning vs Neural Network
• How does Deep Learning Work?

• One Learning Applications 

• Deep Learning Use Cases
• Pros & Cons of Deep Learning 
• Deep Learning Platforms

• Learning by Doing – Deep Learning Model: Use Case 4

• Data Preparation:

• Data Transformation & Validation
• Feature Engineering
• Data Splitting

• Learning by Doing – Deep Learning Model: Use Case 4

• Model Building: Choosing the Right Algorithm 

• Training Optimization
• Model Performance Evaluation
• Model Validation

03:01 AM – 05:00 PM

• Short Quiz and End-of-course Survey