• Introduction of AI.

• Hyperspectral Image Introductions and concepts.

• Digital Soil Mapping workflows.

• Deep learning applications in Hyperspectral image analysis.

• Hyperspectral Image Classifications, Unmixing and Dimensionality Reduction.

• Demo sessions.

• Introduction and Applications of AI.

• AI - Linear and Logistic Regression.

• Deep Learning and CNN

• Machine Learning , Problem Faced in Industry and solutions approach implemented.

Deep Learning Activities: 

• Case Study on Movies revies, 

• Case Study on training models, face detection. 

• Case Study on Object Detection.

• Case study on ANN.

• System Security and Crypt-analysis

• Infrastructure and Network Security

• Security in Evolving Technology

• Cyber Law and Digital Forensics

• Data Science Tools and Methods.

• Data Exploration and visulaization.

• Python for Data Science.

• Random Forest Classifications, Bagging Boosting and Stacking Techniques.

• Tensor flow, Stastical Methods, Naive Bayes classifier.

• K-means clustering, Association Rules, Logistic Regression.

• Data Analytics - Decision Trees, Time Series, Text analytics.

Google Earth Engine