About the Program
The program on ‘AI Techniques and ML Algorithms’ covers a wide range of topics in AI and ML, including principles and techniques, data handling, SQL and NoSQL for data engineering, ML algorithms and model building, data visualizations, and deep learning and NLP basics.L&T collaborates with industry experts to ensure that the course content is up-to-date and relevant to current industry trends and practices. The course likely includes practical, hands-on projects that allow students to apply their learning in real-world scenarios.
Courses
    Credits Semester
  • AI Principles and Techniques 3 III
  • Python Programming for Data Handling and Preprocessing 3 IV
  • Data-Engineering Foundations with SQL and NOSQL 3 V
  • ML Algorithms and Model Building 3 V
  • Data Storytelling and Visualizations 3 VI
  • Deep Learning and NLP Basics 3 VII
  • Project Work - The AI Capstone: Exploring Data to Deep Learning 3 VIII
    Credits Semester
  • IT World Essentials: Your Digital Entrypoint 3 I
  • Critical Thinking, Design Thinking, Leadership and Teamwork 3 II
  • Project Work - The AI Capstone: Exploring Data to Deep Learning 3 VIII
    Credits Semester
  • Critical Thinking, Design Thinking, Leadership and Teamwork 3 II
  • Career Readiness in Digital Era 3 VI
Mode of Delivery
  • Self-paced learning – 12 hours
  • Expert sessions + Project work – 33 hours
  • Face-to-face instructor led sessions / VILT sessions (including project work) – 45 hours
  • Self-paced learning + Expert session – 45 hours
Job Roles
  • Machine Learning Engineer
  • Data Scientist
  • AI Research Scientist
  • Data Engineer
  • AI Product Manager
  • Data Analyst
Software Tools
  • Python
  • SQL
  • NoSQL
  • Apache Hadoop
  • Apache Spark
  • Apache Pig
  • Apache Hive
  • Microsoft Excel
  • Scala
  • Power BI
  • PyTorch
  • Tableau
  • TensorFlow
  • Keras
Skills
  • Understanding core AI principles, including machine learning, NLP, and computer vision.
  • Applying mathematical concepts (linear algebra, probability, statistics, calculus) to solve AI problems.
  • Programming in Python and R, using libraries like TensorFlow, PyTorch, and scikit-learn for AI tasks.
  • Cleaning, preprocessing, and reducing data dimensions for optimal model performance.
  • Implementing machine learning algorithms (regression, classification, clustering) and evaluating models with appropriate metrics.
  • Working with deep learning models (NNs, CNNs, RNNs) for image recognition and natural language processing.
  • Visualizing data and creating interactive dashboards using Power BI and Tableau for data insights.