Intro

This respostory will contains the course work which is required to complete the task of assigned course - TDS - JAVA - MLP - DBMS

Update :

Will avialabe on weekly basis ;

TDS :

This course is structured around seven comprehensive modules that collectively provide a strong foundation in data science. Starting with essential development and deployment tools, learners will progress through advanced topics such as leveraging large language models (LLMs), sourcing and preparing data, analyzing datasets, and creating impactful visualizations. Each module is designed to build practical skills and knowledge, ensuring learners are well-equipped for real-world applications in data science and beyond.

• We will be covering 7 modules in this course.
  • Module 1: Development Tools
  • Module 2: Deployment Tools
  • Module 3: LLM
  • module 4: Data Sourcing
  • Module 5: Data Preparation
  • module 6: Data Analysis
  • Module 7: Data Visualization

JAVA :

This course will cover following modules: - WEEK 1: Basic Object Oriented Programming: Class Hierarchy - WEEK 2: Basic Object Oriented Programming: Inheritance, Overriding - WEEK 3: Basic Object Oriented Programming: Polymorphism - WEEK 4: Basic Object Oriented Programming: Abstract Classes -QUIZ 1: - WEEK 5: Collections. Iterators. - WEEK 6: Generics. Callbacks. -OPPE 1: - WEEK 7: Cloning. I/O serializations. Packages - WEEK 8: Cloning. I/O serializations. Packages (Continued) -QUIZ 2: - WEEK 9: Exception handling - WEEK 10: Concurrent programming - OPPE 2: - WEEK 11: Concurrent programming (Continued) - WEEK 12: Concurrent programming (Continued) - END TERM: ### Prescribed Books : o Concepts in Programming Languages by John C. Mitchell o Java The Complete Reference Eleventh Edition Paperback by Herbert Schildt o Effective Java Second Edition Paperback by Joshua Bloch o Core Java - Vol 1 & 2, 11e Paperback by Cay S. Horstmann

MLP :

This course will cover mostly the coding which will be done on Google Colab.

Goal:

- Understand the life cycle of a machine learning project: typical steps involved and tools that can be used in each step.
- Using machine learning algorithms to solve practical problems using libraries like scikit-learn and tensorflow.
- Fine tuning the algorithms through regularization, feature selection, and better models.    
- Develop an understanding of evaluation of machine learning algorithms and decide the next steps based on the analysis.

• WEEK 1: End-to-end machine learning project on scikit-learn
  • KA1 :
• WEEK 2: Graph Theory(VOL 3)
• WEEK 3: Regression on scikit-learn - Linear regression Gradient descent - batch and stochastic.
• WEEK 4: Polynomial regression, Regularized models
  • KA2 :
  • OPPE 1:
• WEEK 5: Logistic regression
• WEEK 6: Classification on scikit-learn - Binary classifier
• WEEK 7: Classification on scikit-learn - Multiclass classifier
  • KA3 :
• WEEK 8: Support Vector Machines using scikit-learn
  • OPPE 2:
• WEEK 9: Decision Trees, Ensemble Learning and Random Forests
• WEEK 10: Decision Trees, Ensemble Learning and Random Forests (Continued)
• WEEK 11: Neural networks models in scikit-learn
• WEEK 12: Unsupervised learning
  • END TERM:

The prescribed textbook for this course is :

  - Hands-On Machine Learning with Scikit-Learn and Tensorflow: Concepts, Tools, and Techniques to Build Intelligent Systems by Aurélion Géron.
  - [Scikit-learn user guide.](https://scikit-learn.org/stable/user_guide.html)

DBMS :

This couse will cover SQL

• WEEK 1:Course Overview
  • WEEK 2: Relational Model and Basic SQL
  • WEEK 3: Intermediate and Advanced SQL
  • WEEK 4: Relational Query Languages and Database Design
    • QUIZ 1:
  • WEEK 5: Functional Dependency and Normal Forms
  • WEEK 6: Functional Dependency and Normal Forms (cont.) -OPPE 1:
  • WEEK 7: Application Development
  • WEEK 8: Storage Management
    • QUIZ 2:
  • WEEK 9: Indexing and Hashing
  • WEEK 10: Transactions
  • WEEK 11: Backup and Recovery
  • WEEK 12: Query Optimization and Conclusion
    • END TERM:

Prescribed Books :

 - Database Management Systems - Abraham Silberschatz , Henry F. Korth, S. Sudarshan

Projects :

In this section we will be covering the projects which we are gona work on :

TDS :

Project_1: TDS Virtual TA

- Creating a virtual Teaching Assistant Discourse responder.

MLP :

Yet to decide !