Core data structures
Build confidence with indexing, references, stacks, queues and complexity.
- Big-O & arrays
- Strings & hashing
- Linked lists
- Stacks, queues, recursion
- Elementary sorting
Aamir Hussain
AI/ML Engineer • Machine Learning • Problem Solving
AI/ML EngineeringML • GenAI • DSA • Deployment
AI/ML Engineer Portfolio & Knowledge Hub
Building practical ML systems
from data to deployment.
A focused engineering workspace for machine learning, AI, algorithms and production projects—covering data preparation, model building, evaluation, explainability, APIs, RAG and interview-ready problem solving.
End-to-end MLEDA → features → training → evaluation → deployment
Production thinkingFastAPI, Streamlit, Docker, MLflow and reproducible workflows
Problem solvingDSA patterns, Java/Python solutions and dry-run reasoning
Aamir AI/MLEngineer • Build • Deploy
MLGenAIDSA
Python
NumPy
Pandas
SQL
scikit-learn
Regression
Random Forest
XGBoost
SVM
K-Means
PCA
TensorFlow
PyTorch
NLP
Transformers
RAG
Computer Vision
FastAPI
Streamlit
Docker
MLflow
Git
DSA
Hover to pause • technology-focused front-page orbit
Production stackTechnologies used across CodeHub & projects
PyPythonSKscikit-learnTFTensorFlowPTPyTorchFAFastAPISTStreamlitDKDockerMFMLflowDBSQLGTGit
Explore CodeHub
Everything important.
Nothing distracting.
Choose a workspace. Detailed notes and tools open inside the built-in CodeHub browser while the homepage stays clean and professional.
Keep CodeHub in your browserInstall it, bookmark it, pin the tab, or set it as a startup page.
●codehub://workspace/home
Complete DSA Algorithm Galaxy
Every algorithm stays visible while the rings circulate.
Arrays & SearchTrees & GraphsSorting & GreedyStrings & PatternsDP & AdvancedMath & Bit
The complete algorithm atlas is projected into color-coded rotating rings. Every algorithm name stored in CodeHub appears in the circulation area. Hover anywhere over the galaxy to pause it, then use the searchable Algorithm Atlas below for definitions, complexity and examples.
Clear visibility: wider rings, more spacing, upright labels, category colors and pause-on-hover make the rotating names easier to read.
DSA
GALAXY120+ concepts
GALAXY120+ concepts
Hover to pause • color = category • names remain upright
Loading algorithm names…
01 / Structured learning path
Basic → Intermediate → Advanced.
Learn in dependency order. Fundamentals first, then reusable patterns, then trees/graphs/DP and advanced range/string techniques.
Problem-solving patterns
Recognize the shape of the problem before choosing code.
- Two pointers & windows
- Prefix sum & intervals
- Binary search
- Trees, heaps, backtracking
- Greedy & 1D/2D DP
Algorithms & optimization
Master graph algorithms, advanced DP, range queries and string matching.
- Topological sort & DSU
- MST & shortest paths
- Knapsack, LCS, LIS, edit distance
- Trie, KMP, monotonic structures
- Segment & Fenwick trees
02 / Guided DSA tracks
Two separate study systems. One CodeHub.
Use the Kunal track for concept-first Java foundations and the Striver track for structured interview problem practice. The explanations and solution labs below are original companion material; source buttons open the official resources.
Module 01
Java + Programming Foundations
Flow, Java setup, I/O, conditionals, loops, functions, scope
Big-OArraysFunctions
Module 02
Arrays, Search & Sort
Arrays/ArrayList, linear & binary search, modified binary search, elementary + divide-and-conquer sorting
ArraysBinary SearchSorting
Module 03
Strings + Pattern Questions
String operations, builders, comparisons, palindrome logic and pattern construction
StringsStringBuilderPatterns
Module 04
Maths + Bitwise for DSA
Prime checks, sieve, factors, modulo, HCF/LCM, Euclidean algorithm, bit operations
SieveGCDBitwise
Module 05
Recursion + Backtracking
Call stack, recursion trees, merge/quick sort, subsets, permutations, maze, N-Queens, Sudoku thinking
RecursionBacktrackingDivide & Conquer
Module 06
Complexity Analysis
Big-O/Theta/Omega, recurrences, divide-and-conquer analysis and auxiliary space
ComplexityRecurrencesSpace
Module 07
Linked Lists
Singly/doubly/circular list, reversal, fast-slow pointers, cycle detection
Linked ListFast-SlowCycle
Module 08
Stacks + Queues
Stack/queue implementations, circular queue, conversion patterns and interview applications
StackQueueDeque
Module 09
Trees + Range Structures
Binary trees, BST, DFS/BFS, AVL concepts, segment tree and Fenwick tree
TreesBSTRange Query
Module 10
Heaps + Hash Maps
Priority queues, heap sort, top-k, k-way merge, hashing, chaining/probing and rolling hash
HeapHashingTop-K
Module 11
Subarray Patterns
Sliding window, two pointers, prefix ideas, Kadane and monotonic structures
WindowTwo PointersKadane
Module 12
Graphs
BFS, DFS, components, MST, Dijkstra, topo sort, Bellman-Ford and all-pairs thinking
GraphsShortest PathMST
Module 13
Dynamic Programming + Greedy
Memoization/tabulation, knapsack families, subsequences, string DP and greedy decisions
DPKnapsackGreedy
Module 14
Tries + Advanced Algorithms
Trie, advanced number theory, matrix exponentiation, Catalan ideas and decomposition techniques
TrieNumber TheoryAdvanced
474Total items
152Easy
186Medium
136Hard
Learn the basics54 problems / learning items
Important Sorting7 problems / learning items
Arrays: Easy → Hard40 problems / learning items
Binary Search32 problems / learning items
Strings: Basic + Medium15 problems / learning items
Linked List31 problems / learning items
Recursion Pattern-wise25 problems / learning items
Bit Manipulation18 problems / learning items
Stack & Queues30 problems / learning items
Sliding Window + Two Pointer12 problems / learning items
Heaps17 problems / learning items
Greedy Algorithms15 problems / learning items
Binary Trees38 problems / learning items
Binary Search Trees16 problems / learning items
Graphs53 problems / learning items
Dynamic Programming55 problems / learning items
Tries7 problems / learning items
Advanced Strings9 problems / learning items
Original companion solutions
Striver-pattern Solution + Dry Run Lab
Each example is written for CodeHub with clear logic, Python, Java and a variable-by-variable dry run. Use the native category dashboard and the worked solution lab on this page; CodeHub explanations and implementations are original companion material.
Linear scanEasy
Largest Element in an Array
Track the maximum seen so far.
Two pointersEasy
Remove Duplicates from Sorted Array
Keep one write pointer for the next unique value.
Two pointersEasy
Move Zeroes
Compact non-zero values, then fill the rest with zero.
Hash mapEasy
Two Sum
Store each value's index and check whether its complement has appeared.
KadaneMedium
Maximum Subarray Sum
Keep the best subarray ending at the current position.
Binary searchEasy
Binary Search
Discard half of a sorted search space after each comparison.
Modified binary searchMedium
Search in Rotated Sorted Array
At least one half is sorted; use that fact to decide where target can lie.
Pointer reversalEasy
Reverse Linked List
Reverse one next pointer at a time while preserving the rest of the list.
Fast & slow pointersEasy
Linked List Cycle
Two pointers moving at different speeds meet if a cycle exists.
StackEasy
Valid Parentheses
Match every closing bracket with the most recent unmatched opener.
BFS queueMedium
Binary Tree Level Order
Process one queue level at a time.
Grid DFS/BFSMedium
Number of Islands
Every unvisited land cell starts one connected-component traversal.
Priority queueMedium
Dijkstra Shortest Path
Always expand the currently cheapest known node when edge weights are non-negative.
1D DPEasy
Climbing Stairs
Ways to reach step i equals ways to reach i-1 plus i-2.
Dynamic programmingMedium
0/1 Knapsack
For each item, choose skip or take once.
2D DPMedium
Longest Common Subsequence
If characters match, extend the diagonal; otherwise keep the best after skipping one side.
TrieMedium
Implement Trie Prefix Search
Each edge represents one character; prefixes share nodes.
Kahn BFSMedium
Topological Sort / Course Schedule
Process zero-indegree nodes; if all nodes are processed, the dependency graph is acyclic.
03 / Separate Striver DSA workspace
Striver A2Z Sheet.
Solution + code + dry run.
A dedicated interview-practice workspace inside CodeHub. Search worked companion problems, open the logic first, compare Python and Java, then dry-run every important variable before marking a problem solved.
474Official A2Z problems
152Easy
186Medium
136Hard
Basics 54Sorting 7Arrays 40Binary Search 32Strings 15Linked List 31Recursion 25Bit Manipulation 18Stack & Queue 30Sliding Window 12Heaps 17Greedy 15Binary Trees 38BST 16Graphs 53DP 55Tries 7Advanced Strings 9
0 / 40 solvedStored in this browser
CodeHub is an independent companion workspace. Explanations, implementations and dry runs here are original; the source button above opens the official Striver A2Z sheet.
02 / Complete DSA notes
Learn the logic, then trace it.
Every deep-dive opens a full lesson with recognition signals, step-by-step logic, complexity, Python, Java, dry run, and interview notes.
Stay inside CodeHub: the category dashboard, progress, notes, Java/Python solutions and dry runs below run locally in this page. The original TakeUForward page is kept only as an attribution/source reference; this CodeHub does not copy its full copyrighted explanations verbatim. Source reference ↗
Native CodeHub A2Z Workspace
Striver A2Z structure — inside your website
Official category roadmap + CodeHub-original explanations, Java/Python solutions and dry runs.
474 Total152 Easy186 Medium136 Hard
Source structure: TakeUForward Striver A2Z. This native dashboard uses the public category structure while the explanations, code and dry runs on CodeHub are original companion material. Click a category to filter the built-in Striver solution workspace below.
0 / 58 studied
03 / Algorithm atlas
One searchable map of the syllabus.
A fast-reference catalog of core and advanced algorithms, data structures, patterns, DP families, graph methods, string algorithms and math techniques.
Machine Learning • Complete Study Workspace
Learn ML topic by topic.
Chapter-wise GPT-style Machine Learning course from foundations to production ML. Every chapter contains every major sub-concept, easy definition, working process, assumptions, formulas with symbol/parameter names, worked examples, Python code, complete runnable code, line-by-line explanation, dry run, advantages, disadvantages, metrics, use cases, mistakes, revision points and interview Q&A.
HOW TO STUDY THIS SECTIONFollow Basic → Intermediate → Advanced. Open a chapter and study Complete Note → All Concepts → Formula/Parameters → Worked Example → Python → Complete Code → Code Explain → Dry Run → Mistakes → Interview → Revision. Use the Chapter Jump, Previous/Next controls and Mark Learned progress.
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Complete chapter-wise ML course inside CodeHub: every chapter keeps concepts, formulas, named parameters, examples, Python, complete code, line-by-line explanation, dry runs, mistakes, evaluation, use cases, interview Q&A and revision together.
AIML Engineer Complete Notes • inside CodeHub
AI/ML from Basics → Advanced
One searchable learning system for Python, data, mathematics, statistics, preprocessing, machine learning, deep learning, NLP, computer vision, Transformers, Generative AI, RAG, deployment, MLOps and interview revision. Open any concept for an easy definition, key ideas, formula and interview question.
PythonNumPyPandasSQLStatisticsScikit-learnXGBoostDeep LearningNLPComputer VisionTransformersRAGFastAPIMLflowMLOps
Recommended AI/ML order
01Python → NumPy → Pandas → SQL
02Math → statistics → probability → EDA
03Cleaning → encoding → scaling → feature engineering
04Regression → classification → trees → ensembles
05Clustering → PCA → anomaly detection → time series
06Evaluation → CV → tuning → explainability
07Deep learning → NLP → CV → Transformers
08Embeddings → RAG → agents → deployment → MLOps
Searchable AIML syllabus
All core concepts in the same website.
Search “Logistic Regression”, “SHAP”, “LSTM”, “RAG”, “XGBoost” or “MLflow”. The voice assistant can also open or explain these notes.
0 visible concepts0 total conceptsClick any card for notes + formula + interview question
05 / Logic building system
How to think before you code.
Use the same reasoning loop in interviews: understand, model, choose a pattern, prove the logic, trace an example, then implement and test edge cases.
01
Restate
Explain input, output, constraints and edge cases in your own words.
02
Brute force
Write the obvious solution first and calculate its complexity.
03
Find the signal
Sorted? contiguous? repeated lookup? tree? graph? overlapping states?
04
Choose pattern
Select pointers, window, hash, stack, BFS/DFS, greedy, DP, etc.
05
Dry run
Trace variables on a tiny example before writing final code.
06
Prove & test
Explain why each movement/state is safe and test boundaries.
Problem signalThink firstTypical examples
Sorted array / pair targetTwo pointers or binary searchTwo Sum II, 3Sum, search boundaries
Contiguous subarray / substringSliding window / prefix sum / KadaneLongest unique substring, max window, subarray sum
Fast membership / frequenciesHash map or setTwo Sum, anagram, longest consecutive
Nearest greater/smallerMonotonic stackDaily temperatures, histogram
Shortest path / levelsBFS if unweighted; Dijkstra if non-negative weightsGrid distance, network path
Dependencies / prerequisitesTopological sortCourse Schedule, build order
Repeated subproblemsDynamic programmingKnapsack, LCS, coin change
06 / Interview practice sheet
Practice the patterns.
Curated LeetCode-style problems mapped to the exact patterns in the notes. Each button opens the working question + solution page.
07 / Working projects
Algorithms + applied AI/ML.
Practical projects show how data structures, Python, SQL, ML and deployment skills translate into complete products.
Indian Crop Optimization & Yield Prediction
Machine-learning workflow using crop, weather, soil and historical production data.
Retail Sales & Customer Insights
Python, SQL, KPI, segmentation and business analytics dashboard.
Mental Health Score Prediction
Prediction interface backed by a trained model and API workflow.
LeetCode 150 Repository
Java solutions for high-frequency interview problems and patterns.
Zerodha Clone
Trading-platform clone demonstrating reusable components and full-stack architecture.
Airbnb Clone
Tourism/stays application covering listings, responsive UI and server workflows.
Smart visitor concierge
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Aamir AI answers first.
Visitors stay inside CodeHub. The assistant answers DSA, AIML, project and interview questions immediately. Personal or advanced requests can be prepared for Aamir in the same panel.
● Internal AI firstName-awareDSA + AIML knowledgeSmart WhatsApp hand-off
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Aamir AI ConciergeCodeHub knowledge assistant
Hello 👋 I’m Aamir Hussain’s CodeHub assistant. Tell me your name, then ask about DSA, algorithms, Striver problems, AIML concepts, projects, résumé, skills or interview preparation. If your request needs Aamir personally, I’ll prepare the WhatsApp hand-off for you.
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08 / Social & contact
Everything in one place.
Direct contact routes, professional profiles, portfolio, CodeHub and résumé.
Aamir Hussain
AI/ML Engineer • DSA • Data
Open to AI/ML, data, analyst and software opportunities.