Recursion (Implementation), OS begins, Ensembles: Bagging & Random Forest

CampusX "100 Days of ML": Bootstrap Aggregation (Bagging), Out-of-Bag (OOB) evaluation, how bagging reduces model variance + StatQuest: "Bagging, Clearly Explained"

Why OS? Types of Operating Systems (Batch, Time-sharing, Distributed, Real-time, Embedded), OS as resource manager, Dual-Mode Operation (User Mode vs Kernel Mode, Privileged Instructions), Traps, Interrupts & Exceptions, System calls, Process vs Program vs Thread, Process States, Process Control Block (PCB) + Quiz: Introduction to Operating Systems

CampusX: Random Forest algorithm, Feature Bagging (random feature subsets), Gini/Entropy in forests + StatQuest: "Random Forest, Clearly Explained," Parts 1 & 2

Process Creation & Termination, fork(), exec(), wait(), exit(), Zombie & Orphan processes, Daemon Processes, Context Switching; Multiprogramming vs Multitasking vs Multiprocessing vs Multithreading; Inter-Process Communication (IPC Decision Guide: Shared Memory, Message Passing, Pipes, FIFOs, Sockets, Signals, mmap) + Quiz - Daemons and IPC Basics

scikit-learn: Train a RandomForestClassifier, tune n_estimators, max_depth, max_features; Andrew Ng ML Spec Course 2 (Tree Ensembles module) + explain variance reduction out loud

Implement bootstrap aggregating and feature bagging from scratch; compare variance reduction against single Decision Tree.

Quant M11 Algebra (Basic & Advance) + Verbal: Mixed Practice

Enter a live Kaggle Playground / Getting Started competition (e.g. Tabular Playground Series); train baseline Random Forest model.

Logical: Calendars (Basic & Advance) + CPU Scheduling Basics (FCFS, SJF, SRTF, RR, Priority, Multi-level Queue) + mixed revision test