Foundational course in Advance Machine Learning, SQL and R Analytics

Module of Advance Course in Embedded System Design with AI

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Course Objective

The Advanced Machine Learning, SQL and R Analytics course aims to equip learners with advanced knowledge and practical skills in machine learning, database analytics, and statistical computing using SQL and R. The course covers data extraction and management, predictive modelling, classification, clustering, regression, model evaluation, and data visualization to solve real-world analytical problems. Through hands-on projects and industry-relevant case studies, learners will develop the ability to design, implement, and optimize machine learning models while leveraging SQL for efficient data management and R for statistical analysis and visualization. Upon completion, participants will be prepared to develop data-driven solutions and support advanced analytics, artificial intelligence, business intelligence, and research applications across diverse industries.

Course Structure

The course is designed as an 8-credit program, with a total duration of 180 hours. (Theory 60 and Practical 120) Classes will be conducted six days a week (Monday to Saturday). Each day will comprise 2 hours of theory lectures followed by 2 hours of practical laboratory sessions, providing learners with a balanced combination of conceptual knowledge and hands-on experience.

Course Eligibility

Second yearB.Sc. (Maths and Stat)/ B. Com (Maths) /BCA/BCS, 2 nd Year B.E (Any stream) Diploma or Equivalent

Prerequisite

The candidate must know basic statistical formulas and have an understanding of Microsoft Excel. Basic understanding of machine learning concepts, including supervised and unsupervised learning, is desirable but not compulsory

Course Content

Advanced SQL Complex Queries and Subqueries, Advanced JOIN Operations, Window Functions, Common Table Expressions (CTEs), Recursive Queries, Indexing and Performance Tuning, Stored Procedures and Functions, Triggers and Events, Dynamic SQL, SQL Security Measures, Transaction Management and Concurrency Control, Handling Large Datasets, SQL with JSON and XML, Integrating SQL with Other Programming Languages Time Series Introduction to Time Series Analysis, Components of Time Series, Time Series Data Collection and Preparation, Moving Averages, Exponential Smoothing, ARIMA Models, Seasonal Adjustments, Trend Analysis, Cyclical and Irregular Components, Forecasting Techniques, Model Diagnostics and Validation, Spectral Analysis, Multivariate Time Series Analysis Data Analysis Data Analysis using R Programming – Introduction to R, RStudio Interface, Data Structures in R (Vectors, Lists, Data Frames, Matrices), Data Import and Export in R, Data Manipulation with dplyr package, Data Cleaning Techniques, Exploratory Data Analysis in R, Statistical Analysis with R, Regression Analysis, Classification Techniques, Data Visualization with ggplot2 Advanced Machine Learning Ensemble Methods, Neural Networks, Unsupervised Learning, Reinforcement Learning, Advanced Regression Techniques, Support Vector Machines, Natural Language Processing, Generative Models, Graphical Models, Advanced Optimization Techniques, Model Evaluation and Hyperparameter Tuning, Advanced Feature Engineering, Model Interpretability and Explainability, Deployment of Machine Learning Models

Admission and Selection Process

The application form is available on the website. Candidates must submit the completed application form and appear for the Common Entrance Test (CET), which will be conducted offline at our centre. The CET syllabus and assessment will be based on the specific course module selected by the candidate. Candidates who successfully complete the written assessment will be required to appear for a Viva Voce (oral interview) as part of the admission process. Final selection will be based on the candidate’s performance in both the CET and the Viva Voce.

Fee Structure

Course Fees: Rs.25,000/