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Data Structures and Algorithms for AI

Course Code: GEN AI-016
Duration: 5 Hours
Price: Contact For Pricing

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Course Description

Uplift your tech and AI career with our “Data Structures and Algorithms for AI” program. Uncover the limitless potential of essential concepts that form the backbone of artificial intelligence application.

Objectives

Our program trains the fundamental data structures like arrays, linked lists, trees, graphs, and hash tables, alongside algorithms for sorting, searching, and optimization. Special emphasis is placed on AI-specific techniques such as dynamic programming, heuristic search, and graph traversal. The course integrates theoretical knowledge with practical coding exercises, preparing students to solve complex AI problems efficiently. By the end, learners will be equipped with the skills to implement and optimize algorithms crucial for advanced AI development.

Audience

Looking to take your career in AI integrated with data structures to new heights? Our Data Structures and Algorithms for AI program is designed with foundational knowledge and skills in data structures and algorithms, focusing on their applications in artificial intelligence which covers essential concepts such as arrays, linked lists, trees, graphs, sorting, and searching algorithms, emphasizing efficiency and optimization beneficial for developers, ML engineers, AI software Architects. With our program's comprehensive curriculum, you'll master the art of data structures that is integrated with Artificial Intelligence in managing information. By enrolling in our program, you'll be able to fetch the infinite opportunities in the dynamic world of AI and technology.

Prerequisites

Anyone can attend the course.

Content

Modules Included:

 

1. Introduction to Data Structures and Algorithms       

  1. Overview of data structures and algorithms in AI.         
  2. Importance of efficiency and complexity analysis.      
  3. Notion of Time and Space Complexity

 

2. Utilizing Data Structures for AI Problem Solving       

  1. Applying DSA concepts to solve AI problems.
  2. Machine Learning- Decision Trees and Linear Algebra Operations      
  3. Deep Learning- Neural Networks, Graph Neural Networks      
  4. Natural Language Processing-Tries, Dynamic Programming   
  5. Search Algorithms-Graph Traversal, Heirostic Search
  6. Optimization Problems- Genetic Algorithms, Linear Programming      
  7. Robotics and Perception-Spatial Data Structures, Convolutional Neural Networks    
  8. Recommender Systems- Matrix Factorization, Graph Databases         
  9. Anomaly Detection-Clustering Algorithms      

 

3. Linear Data Structures          

  1. Arrays 
  2. Linked Lists      
  3. Stacks 
  4. Queues              
  5. Lab- Implementation of Anomaly Detection using Array/Linked Lists/Graphs in Clustering Algorithms
  6. Lab- Execution of Tree and Array Data Structures in Decision Tree Classifier

 

4. Non-Linear Data Structures

  1. Trees   
  2. Graphs                
  3. Hash tables
  4.  Lab-Execution of Recommender Systems using Graph data structures        Large Language Models
  5. Lab-Neural networks: understanding the data structures behind ANNs, CNNs, and RNNs.

 

5.  Advanced Data Structures  

  1. Heaps 
  2. priority queues
  3. Red Black Trees             
  4. B Trees,B+ Trees            
  5. Prefix Trees(Tries)         
  6. Lab-Execution of Heuristic Search Algorithms (A* search/Hill Climbing) using Priority Queue data structure
  7. Lab-Implementation of Storing Dictionaries in NLP using Prefix Trees

 

6. Basic Sorting and Searching Algorithms       

  1. Sorting algorithms: Quick sort, merge sort ,heap sort 
  2. Uninformed Searching algorithms: Binary search, depth-first search (DFS), breadth-first search (BFS)               
  3. Informed Searching algorithms: A* Search, Heuristic Search, Hill Climbing   
  4. Lab-Solving Maze Problem using DFS/BFS
  5. Lab-Web Crawling /Finding Shortest Path problem using BFS

 

 

What does an AI /ML Engineer or AI software Architect do?

 

An AI /ML Engineer or AI software Architect possesses a diverse set of skills and knowledge required to perform various tasks and responsibilities related to AI based Data Structures and algorithms for meaningful data management and retrieval. With exposure to data structures integrated with AI and ML, an AI/ML Engineer or AI Software Architect applies data structures and algorithms to create powerful, scalable AI systems. Utilizing structures like trees and graphs, they manage data efficiently. Algorithms such as dynamic programming and search strategies drive AI methods, including neural networks and decision trees. This knowledge ensures they can optimize performance, build resilient models, and address intricate AI challenges.

 

 

What are the domains where an AI /ML Engineer or AI software Architect work?

 

An AI /ML Engineer or AI software Architect works across various domains and industries that leverage software development and AI based application development. Some of the common domains where an AI /ML Engineer or AI software Architect may work include.

 

    • Virtual Assistants
    • Customer Support bots
    • AI Research and development
    • Large Language Model Fine-Tuning
    • Natural language Generation
    • Social Network, Media, and Communication
    • Industries that provide software as a service
    • E-Commerce and Finance

 

 

Top Job Roles of Data Structures and Algorithms for AI course

 

    • AI/ML Engineer

    • Data Scientist
    • AI software Architect
    • AI Research Scientist
    • NLP Engineer
    • Computer Vision Engineer
    • Big Data Engineer

 

 

Salary of an AI /ML Engineer or AI software Architect in India.

 

According to recent surveys and reports, the average annual pay package for an AI /ML Engineer or AI software Architect in India is around ₹12 lakhs per year. However, the salary may vary depending on factors such as years of experience, skills, location, and the organization you work for. An Entry level Semantic LLM gets an offer of ₹7 lakhs per year. Senior professionals with a considerable amount of experience can earn up to ₹36 lakhs per annum. Some startups and large technology firms offer higher salaries to attract top talent for critical roles in Software Architecture and Application Development.

 

 

Is it worth to pursue a career in Data Structures and Algorithms for AI?

 

Pursuing a career in Data Structures and Algorithms for AI is highly valuable. These foundational skills are crucial for developing efficient, scalable AI solutions, optimizing performance, and solving complex problems. Mastery in these areas open up opportunities in various AI fields, making you a sought-after professional in the rapidly growing AI industry.