Course: Applications of Machine Learning & Artificial Intelligence in GIS.

Course Introduction: This  course on GIS in Machine Learning and Artificial Intelligence would likely introduce students to the use of Geographic Information Systems (GIS) in combination with machine learning and artificial intelligence techniques to analyze and solve real-world problems. The course may cover the following topics:

  1. Introduction to GIS: Students will learn about the basics of GIS, including data structures, data formats, and GIS software.

  2. Machine Learning and Artificial Intelligence: Students will learn about the concepts and techniques of machine learning and artificial intelligence, including supervised and unsupervised learning, neural networks, and deep learning.

  3. GIS and Machine Learning: Students will learn about how GIS data can be used as input for machine learning models, and how machine learning models can be applied to GIS data to solve problems such as spatial prediction, spatial clustering, and spatial optimization.

  4. GIS and Artificial Intelligence: Students will learn about how GIS data can be used as input for artificial intelligence systems, and how AI techniques can be applied to GIS data to solve problems such as image and signal processing, data mining, and natural language processing.

  5. Applications of GIS in Machine Learning and Artificial Intelligence: Students will learn about various real-world applications of GIS in machine learning and artificial intelligence, such as urban planning, natural resource management, and disaster management.

  6. Hands-on practice: The course will include practical exercises and projects to apply the concepts and techniques learned in the class, using GIS software and machine learning libraries.

The course is designed for students from both GIS and computer science background, who are interested in using geospatial data for machine learning and AI.

Aim of the course: The aim of a course on GIS in Machine Learning and Artificial Intelligence is to provide students with a comprehensive understanding of how Geographic Information Systems (GIS) can be used in combination with machine learning and artificial intelligence techniques to solve real-world problems. The course aims to achieve the following objectives:

  1. To introduce students to the basics of GIS, including data structures, data formats, and GIS software.

  2. To provide students with an understanding of the concepts and techniques of machine learning and artificial intelligence, including supervised and unsupervised learning, neural networks, and deep learning.

  3. To show students how GIS data can be used as input for machine learning models, and how machine learning models can be applied to GIS data to solve problems such as spatial prediction, spatial clustering, and spatial optimization.

  4. To demonstrate how GIS data can be used as input for artificial intelligence systems, and how AI techniques can be applied to GIS data to solve problems such as image and signal processing, data mining, and natural language processing.

  5. To give students a broad understanding of various real-world applications of GIS in machine learning and artificial intelligence, such as urban planning, natural resource management, and disaster management.

  6. To provide hands-on experience with GIS software and machine learning libraries, giving students the opportunity to apply the concepts and techniques learned in the class to real-world problems.

Overall, the course aims to equip students with the skills and knowledge to use GIS in combination with machine learning and AI to solve geospatial problems and make data-driven decisions.

Topics Covered: The course on GIS in Machine Learning and Artificial Intelligence would likely cover a range of topics, including:

  1. Introduction to Geographic Information Systems (GIS): Students will learn about the basics of GIS, including data structures, data formats, and GIS software. Topics may include GIS data models, vector and raster data, coordinate systems, and GIS software such as ArcGIS and QGIS.

  2. Machine Learning and Artificial Intelligence: Students will learn about the concepts and techniques of machine learning and artificial intelligence, including supervised and unsupervised learning, neural networks, and deep learning. Topics may include linear regression, decision trees, k-means, gradient descent, and deep learning frameworks such as TensorFlow and Keras.

  3. GIS and Machine Learning: Students will learn about how GIS data can be used as input for machine learning models, and how machine learning models can be applied to GIS data to solve problems such as spatial prediction, spatial clustering, and spatial optimization. Topics may include spatial data analysis, spatial statistics, and machine learning libraries such as scikit-learn and PyTorch.

  4. GIS and Artificial Intelligence: Students will learn about how GIS data can be used as input for artificial intelligence systems, and how AI techniques can be applied to GIS data to solve problems such as image and signal processing, data mining, and natural language processing. Topics may include image processing, computer vision, and natural language processing libraries such as NLTK and spaCy.

  5. Applications of GIS in Machine Learning and Artificial Intelligence: Students will learn about various real-world applications of GIS in machine learning and artificial intelligence, such as urban planning, natural resource management, and disaster management. Topics may include land use planning, ecosystem management, and emergency management.

  6. Hands-on practice: The course will include practical exercises and projects to apply the concepts and techniques learned in the class, using GIS software and machine learning libraries.

Tools Covered: The  course on GIS in Machine Learning and Artificial Intelligence would likely cover a range of tools, including:

  1. Geographic Information Systems (GIS) software: Students will learn about the use of GIS software such as QGIS, and open-source GIS libraries such as GDAL and GEOS to analyze, visualize and process geospatial data.

  2. Machine Learning libraries: Students will learn about the use of machine learning libraries such as scikit-learn, TensorFlow, Keras, and PyTorch to build and train machine learning models.

  3. Artificial Intelligence libraries: Students will learn about the use of artificial intelligence libraries such as OpenCV, NLTK, spaCy, and NLPI to build and train AI models.

  4. Programming languages: Students will learn about the use of programming languages such as Python, R, and SQL to work with GIS data and perform data analysis.

  5. Cloud computing platforms: Students will learn about the use of cloud computing platforms such as AWS, GCP, and Azure for storing and processing large amounts of data, and for running GIS and machine learning models.

  6. Data visualization tools: Students will learn about the use of data visualization tools such as Tableau, PowerBI, and Matplotlib to create interactive maps, charts and visualizations.

  7. Remote Sensing tools: Students will learn about the use of remote sensing tools such as aerial photography, satellite imagery, LiDAR, and thermal imaging for collecting and analyzing geospatial data.

Course Fee and Training Module

  • Course Name: Machine learning & Artificial Intelligence in GIS.
  • Class Duration: Up to 1 hour per class / 5 weekly classes 
  • Program Duration :1 Month 
  • Certificate of training completion
  • Post training support available 
  • Mode of Training : Instructor Led Interactive Live Classroom 1:1 Online / Offline Training.  
  • Class Timings: Available slots between 07:00 am to 09:00 pm (Indian Standard Time) 
  • Cost: Rs 24000.00/- (all inc.) 
  • For International Students : USD $400.00 /- (all inc.)
  • Payment Options :Secure Transactions with Online banking/Google Pay/NEFT/IMPS/RTGS/UPI/Western Union Money transfer. To join for the training please fill the admission form below. You will receive a joining confirmation via call/email from our team. Post payment you will automatically receive payment receipt and access further information via call/email. Kindly share a screen shot of the payment status once completed. For any information regarding please contact us.
  • Note: Fee includes course material, which will be made available to all participants both during and after the conclusion of the training. Participants will receive course manual in pdf or printed format, a zipped folder with exercise database and a certificate of training completion. 
  • For admissions please fill the online form attached below or contact us directly at +91-9916302284 / +91-7002649334.
Online Admission Form