Learning Path

Module 1

  • Understanding problems and analytical thinking
  • Logical reasoning and structured decision making 
  • Creative thinking and innovation approaches
  • Troubleshooting and solution-oriented mindset
  • User-centric design and ideation practices

Module 2

  • Introduction to AI and machine learning concepts
  • Understanding the evolution of intelligent systems
  • Key terminologies across data and AI domains
  • Emerging trends and applications in modern AI
  • Ethical considerations in AI-driven solutions
  • Programming foundations for AI development

Module 3

  • Working with structured and unstructured data
  • Data manipulation and transformation concepts
  • Exploratory analysis and pattern identification
  • Foundational statistical concepts for ML
  • Data cleaning, preparation, and feature engineering

Module 4

  • Overview of machine learning approaches
  • Predictive modeling and data-driven decision making
  • Classification and regression techniques
  • Model evaluation and performance understanding
  • Clustering and pattern discovery methods
  • Model optimization and tuning strategies

Module 5

  • Fundamentals of neural networks
  • Learning mechanisms and optimization concepts
  • Deep learning architectures and workflows
  • Working with unstructured data such as images
  • Advanced model structures and feature extraction

Module 6

  • Text data processing and language understanding
  • Representation and transformation of textual data
  • Sequence modeling and contextual learning
  • Introduction to generative and advanced AI models
  • Recommendation systems and intelligent applications

Module 7

  • Deploying AI models into real-world applications
  • End-to-end lifecycle management of ML systems
  • Integration of models with applications
  • Cloud computing concepts for scalable AI solutions
  • Modern deployment and infrastructure practices

Module 8

  • Problem identification and solution design
  • Data preparation and model development workflow
  • Model evaluation and validation approaches
  • Deployment and real-world implementation
  • Insight presentation and solution storytelling

Tools Covered

Scholarship Test

The general aptitude test will cover the following topics:

  • Numerical Ability
  • Logical Reasoning
  • Verbal Ability
  • Data Manipulation

* The duration of the test will be 60 mins.

Elgibility

The program is open to the following candidates:

  • Engineering or science graduates, or those with a three-year diploma in any engineering branch, with foundational knowledge in Mathematics and Computer Fundamentals (equivalent to Plus Two).
  • Students who have completed their graduation but are awaiting final results may also apply.

* Please note, the ICT Academy of Kerala reserves the right to cancel the candidature if found ineligible at any point.

Highlights

  • Online and offline sessions tailored for graduates and professionals
  • Scholarships for meritorious candidates via ICTAK
  • 3 to 6 months of access to LinkedIn Learning / Unstop Premium
  • Comprehensive training in employability skills
  • 100% placement assistance for eligible candidates
  • Expert sessions by industry professionals

Our Alumni In Top Companies

Testimonial

Stories From
Our Learners

Discover how learners achieved their goals through our programs. These testimonials highlight the value and real-world outcomes we deliver.

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Candidate Testimonial – Mr. Sooraj S.

Candidate Testimonial – Ms. Krishna I.

Candidate Testimonial – Mr. Ajmal M. S.

Testimonial

Stories From
Our Learners

Discover how learners achieved their goals through our programs. These testimonials highlight the value and real-world outcomes we deliver.

View More

Candidate Testimonial – Ms. Indubala S.

Candidate Testimonial – Ms. Krishna I.

Candidate Testimonial – Ms. Indubala S.