top of page

GIS in Artificial Intelligence & Machine Learning - Certificate Course by Avakaza Geoscience Research Technologies(AGSRT)

Updated: Apr 25

Ready to explore the dynamic fusion of GIS, Artificial Intelligence, and Machine Learning?

Join AGSRT's Certificate Course and unlock a world of possibilities!


Ai and machine learning

Course Modules: AI/ML Content

 1. Introduction to Machine Learning Concepts

  • Definition and Scope of Machine Learning

  • Historical Overview of Machine Learning

  • Types of Machine Learning: Supervised, Unsupervised, Reinforcement Learning

  • Key Terminology: Features, Labels, Training, Testing, etc.

  • Importance and Applications of Machine Learning

2. Common Machine Learning Algorithms and Their Applications

  • Supervised Learning Algorithms:

  • Linear Regression

  • Decision Trees

  • Random Forest

  • Support Vector Machines (SVM)

  • k-Nearest Neighbours (k-NN)

  • Applications of Each Algorithm in Real-world Scenarios

3. Pre-processing Data for Machine Learning

  • Handling Missing Data

  • Data Cleaning and Transformation

  • Feature Scaling and Normalization

  • Splitting Data into Training and Testing Sets

4. Model Training and Classification

  • Overview of Model Training Process

  • Evaluation Metrics: Accuracy, Precision, Recall, F1 Score

  • Cross-Validation Techniques

  • Hyperparameter Tuning

  • Ensemble Methods: Bagging and Boosting

  • Introduction to Neural Networks and Deep Learning

  • ANN-Artificial Neural Network

  • Convolutional Neural Network

5. Data Visualization & Validation

  • Importance of Data Visualization in Machine Learning

  • Tools and Libraries for Data Visualization (e.g., Matplotlib, Seaborn)

  • Exploratory Data Analysis (EDA)

  • Model Validation Techniques

  • Overfitting and Underfitting

  • Confusion Matrix and ROC Curve Analysis

  

GIS in AI & Machine Learning

Landslide Susceptibility Mapping & PM10 Concentration

1.     Spatial Data Gathering and Preparation

·       Overview of spatial data sources

·       Introduction to Geo-spatial data formats (Shape files, GeoTIFF, etc.)

·       Importance of metadata in spatial datasets

·       Hands-on experience with data acquisition from sources like remote sensing, GPS, and satellite imagery

2.     Spatial Data Cleaning and Pre-processing

·       Techniques for handling spatial data outliers

·       Handling spatial data noise and inaccuracies

·       Quality assurance in spatial datasets

·       Utilizing GeoPandas for cleaning and pre-processing

3.     Geo-spatial Analysis Libraries

·       Introduction to GeoPandas for GeoDataFrames and spatial operations

·       Utilizing GDAL (Geospatial Data Abstraction Library) for spatial data formats and transformations

·       Hands-on exercises with GeoPandas and GDAL for spatial data manipulation

4.     Splitting Training and Testing Data

·       Considerations specific to spatial data splitting

·       Spatial cross-validation techniques

·       Ensuring spatial independence in training and testing datasets

5.     Classification and Model Building

·       Application of machine learning algorithms to spatial data

·       Integrating GeoPandas and GDAL with machine learning models

·       Case studies on landslide susceptibility mapping and PM10 concentration prediction

6.     Model Evaluation and Validation

·       Spatial-specific evaluation metrics

·       Assessing the accuracy of spatial predictions

7.     Applying Built Models to Spatial Layers

·       Deployment of models to new spatial data

·       Real-world applications in environmental monitoring and geospatial analysis

8.     Data Visualization and Prediction Map

·       Visualization techniques for spatial data using Matplotlib and Seaborn

·       Creating prediction maps for landslide susceptibility and PM10 concentration

·       Interpretation and communication of spatial model results

 

 📆 Class Starts on Feb 3rd | Duration: 1 Month

🕰️ Time: Monday to Friday, 2:30 pm - 4:00 pm

🌐 Mode of Training:

🌆 Offline - In Bangalore

💻 Online is also Available


Why Choose AGSRT?

 

✅ Expert Trainers: Learn from industry professionals.

🌐 Practical Approach: Hands-on training with real-world applications.

🎓 Certification: Validate your skills with AGSRT's Certificate.

 


GIS in Ai and machine learning

📞 Call Now to Secure Your Spot!

 

📱 +91 9980083996

📱 +91 9742349602

📱 +91 8921038671

 

👥 Join AGSRT - Where Expertise Meets Innovation!

 

 

31 views0 comments

Yorumlar


bottom of page