# Amol Shukla — AI Developer, Trainer & Agentic AI Expert

> Amol Shukla is an AI Developer, Trainer, and Agentic AI Expert building agentic AI systems, training on LLMs and generative AI, and developing production software with Python. Explore his projects, experience, and technical blog.

This is the personal site and free learning library of Amol Shukla. It holds a portfolio (projects, experience, skills, open-source contributions), a technical blog, and 7 free courses covering 135 lessons on agentic AI, large language models, prompt engineering, Python, MySQL and SQL, NumPy, pandas, Matplotlib, seaborn, Plotly, data science, digital marketing and stock market fundamentals. Everything on the site is free to read; no lesson is gated behind an account.

Content is written in English (`inLanguage: en`) and served worldwide. Lesson notes are rendered to HTML on the server, so the full text is available without executing JavaScript. Structured data (JSON-LD) describes the person, each article, each course and the site hierarchy.

## Core pages

- [Home](https://www.amolshukla.online): Profile, featured projects, latest writing and course highlights.
- [Skills](https://www.amolshukla.online/skills): AI, agentic AI, Python and full-stack engineering skill set.
- [Projects](https://www.amolshukla.online/projects): Personal and professional software and AI projects.
- [Experience](https://www.amolshukla.online/experience): Professional timeline: AI training, development and engineering roles.
- [Contributions](https://www.amolshukla.online/contributions): Open-source work and community contributions.
- [Contact](https://www.amolshukla.online/contact): How to get in touch about AI, training and software work.
- [Learning Hub](https://www.amolshukla.online/courses): Catalog of all 7 courses and 135 lessons.
- [Blog](https://www.amolshukla.online/blogs): Long-form articles on LLMs, RAG, Python and web engineering.

## About

Amol Shukla is an AI Developer, Trainer and Agentic AI Expert. He builds agentic AI systems (ReAct-style agent pipelines, tool-calling systems and production LLM workflows), trains developers and data professionals on Python, machine learning and generative AI, and ships production software with Python, FastAPI, Next.js, React and TypeScript.

## Courses

Each course hub links to every lesson. Lesson URLs follow `/courses/{course}/{lesson}`.

### AI Tools: LLM & Prompt Engineering Mastery

Two courses in one: master how LLMs work — context windows, temperature, hallucinations, agentic loops — and become a pro at prompt engineering. 10 modules, 26 lessons, 48 hrs. Difficulty: Beginner.

- [Course hub](https://www.amolshukla.online/courses/ai-tools): Syllabus, module list and every lesson in one page.

#### Module 1: [LLM] How Large Language Models Work

- [Lesson 1: What Is an LLM?](https://www.amolshukla.online/courses/ai-tools/what-is-an-llm): Understand what large language models are, why they predict the next token, and the mental model that unlocks everything else.
- [Lesson 2: The Transformer & Self-Attention](https://www.amolshukla.online/courses/ai-tools/transformer-self-attention): Peek inside the model: embeddings, the transformer stack, and the self-attention mechanism that decides which words matter.
- [Lesson 3: The Training Pipeline — Pre-training, Fine-tuning & RLHF](https://www.amolshukla.online/courses/ai-tools/llm-training-pipeline): How a raw neural network becomes ChatGPT: pre-training on trillions of tokens, supervised fine-tuning, and reinforcement learning from human feedback.

#### Module 2: [LLM] The Context Window

- [Lesson 4: What Is a Context Window?](https://www.amolshukla.online/courses/ai-tools/context-window-basics): The context window is everything the model can 'see' at once — input plus output. Understand limits, truncation, and memory.
- [Lesson 5: Managing Long Contexts](https://www.amolshukla.online/courses/ai-tools/managing-long-contexts): Chunking, sliding windows, summarization, and retrieval — the techniques that let you work with text far larger than any window.

#### Module 3: [LLM] Temperature & Sampling

- [Lesson 6: Temperature Explained](https://www.amolshukla.online/courses/ai-tools/temperature-explained): What temperature actually does to the probability distribution, and when to use low, medium, or high values.
- [Lesson 7: Top-K & Top-P (Nucleus) Sampling](https://www.amolshukla.online/courses/ai-tools/top-k-top-p-sampling): The other sampling dials: top-k trims the candidate list, top-p trims by probability mass. Learn to combine them.

#### Module 4: [LLM] Hallucinations

- [Lesson 8: Why LLMs Hallucinate](https://www.amolshukla.online/courses/ai-tools/why-llms-hallucinate): Hallucinations are a feature of how LLMs work, not a bug you can switch off. Understand the root causes.
- [Lesson 9: Detecting & Reducing Hallucinations](https://www.amolshukla.online/courses/ai-tools/detecting-reducing-hallucinations): Grounding, RAG, citations, self-checking prompts, and workflow design — the practical toolkit for trustworthy LLM output.

#### Module 5: [LLM] Agentic Loops & AI Agents

- [Lesson 10: What Is an Agentic Loop?](https://www.amolshukla.online/courses/ai-tools/agentic-loop-basics): One prompt = one answer. An agentic loop = the model plans, acts, observes, and repeats until the job is done.
- [Lesson 11: Tools & Function Calling](https://www.amolshukla.online/courses/ai-tools/tools-function-calling): Tools are how agents touch the world. Learn function calling, tool schemas, and the ReAct pattern with a real example.
- [Lesson 12: Building a Simple Agent](https://www.amolshukla.online/courses/ai-tools/build-a-simple-agent): Hands-on: assemble a minimal agent with tools, memory, and stop conditions — then meet the frameworks that do it for you.

#### Module 6: [LLM] Advanced LLM Topics

- [Lesson 13: RAG — Retrieval-Augmented Generation](https://www.amolshukla.online/courses/ai-tools/rag-deep-dive): RAG grounds LLM answers in your own data: index, retrieve, augment, generate. The professional standard for trustworthy AI.
- [Lesson 14: Embeddings & Vector Databases](https://www.amolshukla.online/courses/ai-tools/embeddings-vector-databases): Embeddings turn text into coordinates where meaning = proximity. Learn similarity search and choosing a vector database.
- [Lesson 15: Evaluating LLM Systems](https://www.amolshukla.online/courses/ai-tools/evaluating-llms): Benchmarks, metrics, and LLM-as-judge — how to measure quality, catch regressions, and know when your AI is good enough.

#### Module 7: [Prompt] Prompt Engineering Foundations

- [Lesson 16: The Anatomy of an Effective Prompt](https://www.amolshukla.online/courses/ai-tools/prompt-anatomy): Role, context, task, format, constraints — the five building blocks that turn vague requests into precise instructions.
- [Lesson 17: Zero-Shot, Few-Shot & Chain-of-Thought](https://www.amolshukla.online/courses/ai-tools/zero-shot-few-shot-cot): The three core techniques every prompt engineer reaches for — and when to use each one.

#### Module 8: [Prompt] Advanced Prompting Patterns

- [Lesson 18: Structured Outputs & Reusable Templates](https://www.amolshukla.online/courses/ai-tools/structured-outputs-templates): Force consistent, machine-readable outputs with JSON, tables, and XML tags — and stop rewriting prompts from scratch.
- [Lesson 19: Prompting for Agentic Workflows](https://www.amolshukla.online/courses/ai-tools/prompting-agentic-workflows): System prompts for agents, tool-use instructions, prompt chaining, and evaluation — production prompting for AI tools.

#### Module 9: [Prompt] Advanced Reasoning & Techniques

- [Lesson 20: Self-Consistency — Sample & Vote](https://www.amolshukla.online/courses/ai-tools/self-consistency-sampling): Run the same prompt several times and take the majority answer to lift accuracy on hard reasoning tasks, with examples.
- [Lesson 21: Tree of Thought & Persona Prompting](https://www.amolshukla.online/courses/ai-tools/tree-of-thought-prompting): Explore several reasoning branches before deciding, and assign the model a persona to unlock specialized perspectives — with examples.
- [Lesson 22: Prompt Chaining & Multi-Step Workflows](https://www.amolshukla.online/courses/ai-tools/prompt-chaining-workflows): Break big tasks into focused prompts where each output feeds the next — with a full worked chain and validation checkpoints.

#### Module 10: [Prompt] Real-World Prompt Playbook

- [Lesson 23: Prompting for Code Generation](https://www.amolshukla.online/courses/ai-tools/pe-code-generation): The CODE framework, before/after examples, and the mistakes that make AI write broken code.
- [Lesson 24: Prompting for Writing & Content](https://www.amolshukla.online/courses/ai-tools/pe-content-writing): The WRITE framework for audience, tone, and format — with before/after examples for posts, emails, and articles.
- [Lesson 25: Prompt Injection — Attacks & Defenses](https://www.amolshukla.online/courses/ai-tools/pe-prompt-injection-safety): How attackers smuggle instructions into your prompts, and the defensive patterns that keep AI tools safe — with examples.
- [Lesson 26: Evaluating & Iterating on Prompts](https://www.amolshukla.online/courses/ai-tools/pe-prompt-evaluation): Golden sets, accuracy metrics, A/B testing prompt versions, and the iteration loop that turns prompting into engineering.

### Complete Python Course: From Zero to Professional

The easiest, most visual Python course — 25 lessons with code walk-throughs, visualization tips, pro tricks, 20 code quiz questions, and 25 LeetCode-style coding challenges in every lesson. 6 modules, 25 lessons, 35 hrs. Difficulty: Beginner.

- [Course hub](https://www.amolshukla.online/courses/python-basics): Syllabus, module list and every lesson in one page.

#### Module 1: Python Fundamentals

- [Lesson 1: Hello, Python! Setup & Your First Program](https://www.amolshukla.online/courses/python-basics/hello-world-python): Installing Python, the REPL, print statements, comments, and why indentation matters.
- [Lesson 2: Variables, Data Types & Input/Output](https://www.amolshukla.online/courses/python-basics/variables-io): The five core data types (int, float, str, bool, None), dynamic typing, type casting, memory references, and reading standard input.
- [Lesson 3: Strings — Slicing, Methods & f-Strings](https://www.amolshukla.online/courses/python-basics/strings-mastery): String indexing and slicing, powerful string methods, and modern formatted strings.
- [Lesson 4: Operators & Expressions](https://www.amolshukla.online/courses/python-basics/operators-expressions): Arithmetic, comparison, logical, and assignment operators — plus expression evaluation order.

#### Module 2: Control Flow

- [Lesson 5: Conditional Branches & Loops](https://www.amolshukla.online/courses/python-basics/loops-conditionals): Evaluating truth tables, if/elif/else routing, for and while loops, break, and continue.
- [Lesson 6: Loop Control, Nested Loops & The else Clause](https://www.amolshukla.online/courses/python-basics/loop-control-nested): Master break, continue, pass, loop else clauses, and nested loops with trace tables.
- [Lesson 7: Comprehensions — Clean, Fast Loops](https://www.amolshukla.online/courses/python-basics/comprehensions): Build lists, dicts, and sets in one elegant line with comprehensions and generator expressions.

#### Module 3: Data Structures

- [Lesson 8: Lists & Tuples](https://www.amolshukla.online/courses/python-basics/lists-tuples): Mutable lists, immutable tuples, slicing, sorting, and common list methods.
- [Lesson 9: Sets & Dictionaries](https://www.amolshukla.online/courses/python-basics/sets-dictionaries): Unordered sets for lightning-fast membership, and key-value dictionaries for real-world data.
- [Lesson 10: Advanced Collections — Counter, defaultdict & deque](https://www.amolshukla.online/courses/python-basics/advanced-collections): Supercharge your data with Counter, defaultdict, deque, and namedtuple from the collections module.
- [Lesson 11: Nested Structures, Aliasing & Copies](https://www.amolshukla.online/courses/python-basics/nested-copies): Build nested dicts and lists, understand how references behave, and copy safely.

#### Module 4: Functions & Modularity

- [Lesson 12: Functions — Reusable Building Blocks](https://www.amolshukla.online/courses/python-basics/functions-basics): Define functions, parameters, default arguments, return values, and docstrings.
- [Lesson 13: Lambda, *args & **kwargs](https://www.amolshukla.online/courses/python-basics/lambda-args-kwargs): Anonymous one-line functions and flexible functions that accept any number of arguments.
- [Lesson 14: Scope, Closures & Decorators](https://www.amolshukla.online/courses/python-basics/scope-closures-decorators): LEGB scoping rules, closures that remember, and decorators that wrap functions with extra behavior.
- [Lesson 15: Modules, Packages & Imports](https://www.amolshukla.online/courses/python-basics/modules-packages): Organize code into files, import between them, use the __name__ guard, and install packages with pip.

#### Module 5: Object-Oriented Python

- [Lesson 16: Classes, Instances & Inheritance](https://www.amolshukla.online/courses/python-basics/classes-objects): Object structure, __init__ constructor, instance parameters, methods, and parent-child overrides.
- [Lesson 17: Inheritance & Polymorphism](https://www.amolshukla.online/courses/python-basics/inheritance-polymorphism): Deep-dive into inheritance, method overriding, polymorphism, isinstance, and the MRO.
- [Lesson 18: Encapsulation, Properties & Magic Methods](https://www.amolshukla.online/courses/python-basics/encapsulation-magic-methods): Private attributes, @property for smart access, and dunder methods that customize objects.
- [Lesson 19: Dataclasses & Modern OOP](https://www.amolshukla.online/courses/python-basics/dataclasses): Write less boilerplate with @dataclass, frozen data, and professional OOP best practices.

#### Module 6: Files, Errors & Professional Python

- [Lesson 20: File Handling & Context Managers](https://www.amolshukla.online/courses/python-basics/file-handling): Read and write files safely with the with statement, work with paths, and handle CSV data.
- [Lesson 21: Exception Handling — Fail Gracefully](https://www.amolshukla.online/courses/python-basics/exception-handling): try/except/else/finally, raising your own errors, and writing code that never crashes the user.
- [Lesson 22: Iterators, Generators & itertools](https://www.amolshukla.online/courses/python-basics/iterators-generators): Lazy iteration with yield, memory-efficient pipelines, and the itertools toolbox.
- [Lesson 23: JSON & Working with Data](https://www.amolshukla.online/courses/python-basics/json-data): Serialize Python objects to JSON, load API data, and build real-world data workflows.
- [Lesson 24: Testing & Debugging Like a Pro](https://www.amolshukla.online/courses/python-basics/testing-debugging): assert, pytest, the debugger, and logging — write code that proves itself.
- [Lesson 25: Capstone — Build a CLI Expense Tracker](https://www.amolshukla.online/courses/python-basics/mini-project-expense-tracker): Bring everything together: functions, dicts, files, JSON, and a menu loop in one complete project.

### MySQL: Practical SQL from Beginner to Professional

Learn SQL on a ready-made retail database with 566 practice problems you can run and check in the browser. 11 modules, 21 lessons, 42 hrs. Difficulty: Beginner.

- [Course hub](https://www.amolshukla.online/courses/mysql): Syllabus, module list and every lesson in one page.
- [MySQL playground](https://www.amolshukla.online/courses/mysql/playground): Run SQL in the browser on the course database; no install.

Key facts: 21 lessons, 566 practice problems (Easy to Professional) with automatic answer checking in the browser, 78 quiz questions, about 42 hours, MySQL 8.0.16+ (8.4 LTS recommended). Every worked example shows output captured from a real MySQL 8 server. Each lesson page has four steps (Lesson, Database, Guided, Practice) that are all present in the server HTML; append #practice to a lesson URL to open its problems.

#### Module 1: Foundations

- [What is a database? Tables, rows and your first SQL query](https://www.amolshukla.online/courses/mysql/what-is-a-database): No SQL experience needed. Learn what a database, table, row, column and primary key are, then write your first SELECT statement. Q: What is a database and what is SQL? A: A database is an organised store of data kept in tables: each table lists one kind of thing, each row is one record and each column is one fact about it. SQL (Structured Query Language) is the language you use to ask the database questions. MySQL is a database server that understands SQL.
- [How to install MySQL 8.4 and Workbench and run your first query](https://www.amolshukla.online/courses/mysql/install-connect-mysql): Install MySQL Community Server and Workbench on Windows, macOS or Linux, connect on port 3306, and fix the three most common connection errors. Q: How do I install MySQL and run my first query? A: Download MySQL Community Server 8.4 (LTS) and MySQL Workbench from dev.mysql.com, run the installer, set a root password, and keep the server running. In Workbench create a connection to host 127.0.0.1, port 3306, user root, then open a SQL tab and run SELECT VERSION();.
- [Import a sample MySQL database and explore its tables](https://www.amolshukla.online/courses/mysql/import-explore-retail): Import the Learning Shop practice database, then explore it with USE, SHOW TABLES, DESCRIBE and SELECT * before writing any analysis. Q: How do I import a .sql file into MySQL Workbench? A: In MySQL Workbench choose File → Open SQL Script, select the .sql file, then click the lightning bolt to execute the whole script. Refresh the Schemas panel, run USE database_name; to select it, and SHOW TABLES; to confirm the tables arrived.

#### Module 2: One table

- [SELECT, WHERE, NULL and ORDER BY in MySQL](https://www.amolshukla.online/courses/mysql/setup-select-filter): Choose columns with SELECT, keep matching rows with WHERE, combine conditions with AND / OR / NOT, handle NULL correctly and sort results with ORDER BY. Q: How do you filter and sort rows in MySQL? A: Use WHERE to keep only rows that match a condition and ORDER BY to sort them: SELECT product_name, price FROM products WHERE price >= 300 ORDER BY price DESC;. Combine conditions with AND / OR (add brackets when you mix them) and test missing values with IS NULL, never = NULL.
- [MySQL IN, BETWEEN, LIKE, DISTINCT and LIMIT with examples](https://www.amolshukla.online/courses/mysql/filtering-patterns): Filter with lists (IN), ranges (BETWEEN) and text patterns (LIKE), remove duplicates with DISTINCT, and page through results with LIMIT and OFFSET. Q: What do IN, BETWEEN and LIKE do in MySQL? A: IN matches any value in a list (city IN ('Pune','Mumbai')). BETWEEN matches an inclusive range (price BETWEEN 100 AND 300 includes both ends). LIKE matches text patterns: % stands for any number of characters and _ for exactly one, so name LIKE 'E%' finds names starting with E.
- [MySQL string, number and date functions, COALESCE and CASE](https://www.amolshukla.online/courses/mysql/functions-and-case): Clean and label data inside a query: CONCAT, UPPER, SUBSTRING, ROUND, COALESCE, IFNULL, YEAR, DATEDIFF and CASE WHEN — with the NULL traps explained. Q: How does CASE WHEN work in MySQL? A: CASE checks conditions in order and returns the value of the first one that is true: CASE WHEN price >= 800 THEN 'premium' WHEN price >= 100 THEN 'mid' ELSE 'budget' END. If nothing matches and there is no ELSE, it returns NULL. Use it in SELECT to label rows or in ORDER BY to control sorting.
- [COUNT, SUM, AVG, GROUP BY and HAVING in MySQL](https://www.amolshukla.online/courses/mysql/aggregate-group-having): Summarise rows with COUNT, SUM, AVG, MIN and MAX, make one row per group with GROUP BY, and filter groups with HAVING instead of WHERE. Q: What is the difference between WHERE and HAVING in SQL? A: WHERE filters individual rows before they are grouped; HAVING filters whole groups after GROUP BY has calculated aggregates. So WHERE city = 'Pune' keeps Pune customers, while HAVING COUNT(*) > 1 keeps only cities that have more than one customer. Aggregates such as COUNT can only be tested in HAVING.

#### Module 3: Many tables

- [MySQL JOIN explained: INNER, LEFT, self join and double counting](https://www.amolshukla.online/courses/mysql/joins-and-grain): Combine tables with INNER JOIN and LEFT JOIN, find rows with no match, join a table to itself, and avoid the double counting that row expansion causes. Q: What is the difference between INNER JOIN and LEFT JOIN? A: INNER JOIN returns only rows that have a match in both tables. LEFT JOIN returns every row from the left table and fills the right table's columns with NULL where there is no match. Use LEFT JOIN … WHERE right.id IS NULL to find rows with no match, such as customers who never ordered.
- [MySQL subqueries, EXISTS and CTEs (WITH) step by step](https://www.amolshukla.online/courses/mysql/subqueries-ctes): Use a query inside a query: scalar subqueries, IN and EXISTS / NOT EXISTS, derived tables, and readable multi-step reports with WITH (common table expressions). Q: What is a CTE in MySQL? A: A CTE (common table expression) is a named, temporary result defined with WITH name AS (SELECT …) at the start of a query. The main query can then read it like a table. It exists only while that one statement runs, and it makes multi-step logic readable. MySQL supports CTEs from version 8.0.
- [MySQL UNION vs UNION ALL, INTERSECT and EXCEPT](https://www.amolshukla.online/courses/mysql/union-set-operations): Stack the results of several queries with UNION and UNION ALL, compare sets with INTERSECT and EXCEPT, add total rows, and emulate a FULL OUTER JOIN. Q: What is the difference between UNION and UNION ALL? A: Both stack the rows of two SELECTs into one result. UNION removes duplicate rows, which costs a sort or hash step; UNION ALL keeps every row, including duplicates, and is faster. Use UNION ALL unless you specifically need duplicates removed. Both SELECTs must return the same number of columns.

#### Module 4: Analysis

- [MySQL date reporting and window functions: ROW_NUMBER, RANK, LAG, running totals](https://www.amolshukla.online/courses/mysql/dates-case-windows): Report by month with DATE_FORMAT, then use window functions — ROW_NUMBER, RANK, DENSE_RANK, LAG, LEAD and SUM OVER — on the shop and on a 4,000-order dataset. Q: What is a window function in MySQL? A: A window function calculates across a set of related rows but, unlike GROUP BY, keeps every row in the result. You write it with OVER: SUM(revenue) OVER (ORDER BY month) gives a running total, and ROW_NUMBER() OVER (PARTITION BY customer_id ORDER BY order_date) numbers each customer's orders. MySQL supports them from 8.0.

#### Module 5: Build and change

- [MySQL CREATE TABLE: data types, primary keys, foreign keys and CHECK constraints](https://www.amolshukla.online/courses/mysql/create-tables-constraints): Design tables: choose data types, add PRIMARY KEY, FOREIGN KEY, UNIQUE, NOT NULL, DEFAULT and CHECK constraints, use ALTER TABLE, and normalise. Q: How do you create a table with a primary key and foreign key in MySQL? A: List each column with its data type, then the constraints: CREATE TABLE reviews (review_id INT AUTO_INCREMENT PRIMARY KEY, product_id INT NOT NULL, rating INT NOT NULL CHECK (rating BETWEEN 1 AND 5), FOREIGN KEY (product_id) REFERENCES products(product_id));. MySQL then rejects duplicates, missing values, out-of-range ratings and reviews for products that do not exist.
- [MySQL INSERT, UPDATE, DELETE and transactions (COMMIT and ROLLBACK)](https://www.amolshukla.online/courses/mysql/insert-update-delete-transactions): Change data safely: INSERT rows, UPDATE and DELETE with a previewed WHERE, delete child rows first, and wrap changes in a transaction. Q: How do you safely update data in MySQL? A: Preview first with a SELECT using the exact WHERE you plan to use and note the row count. Then run START TRANSACTION, the UPDATE, and check ROW_COUNT() and the changed rows. If the count and values are right, COMMIT; otherwise ROLLBACK and nothing is changed.
- [MySQL views, indexes and EXPLAIN: reusable queries and faster lookups](https://www.amolshukla.online/courses/mysql/views-indexes-explain): Save a query as a view, add the right index, read EXPLAIN (type, key, rows) on a 4,000-order dataset, and turn data-quality checks into reusable views. Q: What does EXPLAIN show in MySQL? A: EXPLAIN shows how MySQL plans to run a query without running it. Read three columns first: type (ALL = full table scan; ref, range or const = using an index), key (the index chosen, or NULL) and rows (MySQL's estimate of rows to examine). A full scan on a large table usually means a missing or unusable index.
- [MySQL capstone project: build an audited retail sales report](https://www.amolshukla.online/courses/mysql/capstone-retail-report): Build a Q1 2026 sales report for Learning Shop: KPIs, breakdowns, reconciliation, data-quality checks and a README, scored with a rubric. Q: What makes a good SQL portfolio project? A: A clear business question, a reproducible script, stated definitions (period, revenue rule, grain), results that reconcile to one control total, data-quality checks, and a short README covering limitations. Reviewers trust a report they can rerun and check, not one with the most complex query.

#### Module 6: Real life database: Hospital records

- [Real life database practice: hospital records (50 SQL problems)](https://www.amolshukla.online/courses/mysql/real-life-hospital): Practise 50 SQL problems on a 2,000-appointment hospital database — filtering, joins, grouping and window functions, checked in your browser. Q: What does the hospital practice database contain? A: Three related tables: 600 patients, 60 doctors in six departments, and 2,000 appointments that link a patient to a doctor with a date, status, diagnosis, amount and rating. Every practice problem runs on it in the browser, with the database already selected.

#### Module 7: Real life database: Retail banking

- [Real life database practice: retail banking (50 SQL problems)](https://www.amolshukla.online/courses/mysql/real-life-bank): Practise 50 SQL problems on a 2,000-transaction bank database — customer segments, balances, channels and monthly reporting, checked in your browser. Q: What is in the retail bank practice database? A: 400 customers, around 600 accounts and 2,000 transactions in four branches and six cities. Customers have a segment and credit score, accounts have a branch, type and balance, and transactions carry a type, channel and amount. The database is already selected in every practice problem.

#### Module 8: Real life database: E-commerce marketplace

- [Real life database practice: e-commerce marketplace (50 SQL problems)](https://www.amolshukla.online/courses/mysql/real-life-marketplace): Practise 50 SQL problems on a 2,000-order marketplace database — catalog, customers, revenue and returns, checked in your browser. Q: What does the marketplace practice database contain? A: Five related tables: 50 sellers, 200 products, 500 customers, 2,000 orders and roughly 4,000 order lines. Revenue is quantity × unit_price minus discount; order_items holds the lines, so any revenue question has to join it to orders. The database is already selected in every problem.

#### Module 9: Real life database: College records

- [Real life database practice: college records (50 SQL problems)](https://www.amolshukla.online/courses/mysql/real-life-college): Practise 50 SQL problems on a 2,000-result college database — students, courses, enrolments, marks and pass rates, checked in your browser. Q: What is in the college practice database? A: Four tables: 400 students, 40 courses, 1,200 enrollments and 2,000 exam results with marks and a letter grade. Enrollments answer 'who is taking what' while exam_results answer 'how they did', so joins must name which one they mean. The database is already selected in every problem.

#### Module 10: Real life database: Ride-hailing

- [Real life database practice: ride-hailing (50 SQL problems)](https://www.amolshukla.online/courses/mysql/real-life-rides): Practise 50 SQL problems on a 2,000-trip ride-hailing database — drivers, riders, fares, surge and city performance, checked in your browser. Q: What does the ride-hailing practice database contain? A: 200 drivers, 500 riders and 2,000 trips across ten cities. Each trip has a rider, an optional driver, distance, fare, status and surge multiplier. Revenue means SUM(fare) over completed trips only — the other statuses store 0. The database is already selected in every problem.

#### Module 11: Real life database: Streaming service

- [Real life database practice: streaming service (50 SQL problems)](https://www.amolshukla.online/courses/mysql/real-life-streaming): Practise 50 SQL problems on a 2,000-session streaming database — plans, titles, watch time and completion rates, checked in your browser. Q: What is in the streaming practice database? A: Four tables: four plans, 400 users, 150 titles and 2,000 watch sessions. A session stores minutes watched, device and whether the title was finished, so watch-time questions aggregate watch_sessions while revenue questions use plans and active users. The database is already selected in every problem.

### Complete Digital Marketing Course: From Foundations to Mastery

Master SEO, content marketing, social media, and analytics with practical, real-world strategies for 2026. 2 modules, 6 lessons, 35 hrs. Difficulty: Beginner.

- [Course hub](https://www.amolshukla.online/courses/digital-marketing): Syllabus, module list and every lesson in one page.

#### Module 1: Foundations of Digital Marketing

- [Lesson 1: Introduction to Digital Marketing](https://www.amolshukla.online/courses/digital-marketing/intro-digital-marketing): What is digital marketing, why it matters in 2026, and the evolution from traditional to digital channels.
- [Lesson 2: Buyer Personas & Customer Journey Mapping](https://www.amolshukla.online/courses/digital-marketing/buyer-personas): Create detailed buyer personas and map the customer journey from awareness to advocacy.
- [Lesson 3: Setting SMART Marketing Goals](https://www.amolshukla.online/courses/digital-marketing/smart-goals): Learn how to set Specific, Measurable, Achievable, Relevant, and Time-bound goals for your marketing campaigns.

#### Module 2: Search Engine Optimization (SEO) Fundamentals

- [Lesson 4: How Search Engines Work](https://www.amolshukla.online/courses/digital-marketing/how-search-engines-work): Understanding crawling, indexing, and ranking — the three pillars of search engine functionality.
- [Lesson 5: Keyword Research Mastery](https://www.amolshukla.online/courses/digital-marketing/keyword-research): Find the right keywords to target using research tools and data-driven strategies.
- [Lesson 6: On-Page SEO Optimization](https://www.amolshukla.online/courses/digital-marketing/on-page-seo): Optimize your content, meta tags, headings, and internal links for maximum search visibility.

### Complete Stock Market Course: From Beginner to Confident Investor

Master stock market investing with fundamental analysis, technical analysis, portfolio construction, dividend strategies, global markets, advanced valuation, SEC filings, sector analysis, and risk management. 10 modules, 30 lessons, 80 hrs. Difficulty: Beginner.

- [Course hub](https://www.amolshukla.online/courses/stock-marketing): Syllabus, module list and every lesson in one page.

#### Module 1: Foundations of the Indian Stock Market

- [Lesson 1: What is the Indian Stock Market and How Does It Work?](https://www.amolshukla.online/courses/stock-marketing/intro-indian-stock-market): A beginner-friendly introduction to the Indian stock market — what it is, why it exists, how NSE and BSE work, and how you can start investing today.
- [Lesson 2: Types of Indian Stocks and How to Choose Between Them](https://www.amolshukla.online/courses/stock-marketing/types-of-indian-stocks): Understand the different types of Indian stocks — large-cap, mid-cap, small-cap — and learn how to build a balanced portfolio that matches your goals.
- [Lesson 3: How to Evaluate if an Indian Stock is Worth Buying](https://www.amolshukla.online/courses/stock-marketing/fundamental-analysis): Learn how to check a company's financial health, understand key numbers, and decide whether a stock is a good investment — all explained in simple language.

#### Module 2: Technical Analysis — Reading Charts and Understanding Market Movements

- [Lesson 1: How to Read Stock Charts (Beginner's Guide)](https://www.amolshukla.online/courses/stock-marketing/chart-basics-indian): Understand candlestick charts, support and resistance levels, and how to read price movements on Indian stocks — explained in simple language.
- [Lesson 2: Simple Indicators That Help You Make Better Decisions](https://www.amolshukla.online/courses/stock-marketing/technical-indicators-indian): Learn about moving averages, RSI, and MACD — three simple tools that help you understand trends, momentum, and whether a stock is overbought or oversold.
- [Lesson 3: Understanding Volume — The Fuel Behind Price Movements](https://www.amolshukla.online/courses/stock-marketing/volume-analysis-indian): Learn why volume matters, how to use delivery volume on NSE to spot genuine moves, and how to track FII and DII activity in the Indian market.

#### Module 3: Building Your Investment Portfolio — A Beginner's Complete Guide

- [Lesson 1: What is Asset Allocation and Why Does It Matter?](https://www.amolshukla.online/courses/stock-marketing/portfolio-theory-indian): Understand how to divide your money across different investments to balance risk and reward — explained with Indian examples and real numbers.
- [Lesson 2: Why SIP Beats Trying to Time the Market](https://www.amolshukla.online/courses/stock-marketing/dollar-cost-averaging-indian): Understand why investing consistently every month works better than trying to guess when the market is low — with real Indian market examples.
- [Lesson 3: How to Pay Less Tax and Keep More of Your Investment Returns](https://www.amolshukla.online/courses/stock-marketing/tax-efficient-investing-indian): Learn about Indian tax rules for investments, how to use PPF, ELSS, and NPS to save tax, and how to make your portfolio tax-efficient.

#### Module 4: Why Smart People Make Dumb Money Decisions — Behavioral Finance for Indian Investors

- [Lesson 1: The Psychology of Investing — Why Your Brain is Your Biggest Enemy](https://www.amolshukla.online/courses/stock-marketing/behavioral-finance-indian): Learn about the cognitive biases that cause Indian investors to buy high and sell low, and how to protect yourself from your own emotions.
- [Lesson 2: Your Investment Policy Statement — The Document That Will Make You Rich](https://www.amolshukla.online/courses/stock-marketing/ips-and-discipline): Create a simple, written guide for all your investment decisions that will protect you from emotional mistakes during market volatility.
- [Lesson 3: How to Survive and Profit from Indian Market Crashes](https://www.amolshukla.online/courses/stock-marketing/crisis-behavior): Learn the exact steps to take during Indian market crashes — from preparation to execution to recovery — so you can stay calm while others panic.

#### Dividend Investing & Income Strategies in India

- [Understanding Dividends — How Indian Companies Pay You](https://www.amolshukla.online/courses/stock-marketing/51)
- [Building Your Dividend Income Portfolio](https://www.amolshukla.online/courses/stock-marketing/52)
- [Dividend Tax Planning & SWP Strategies](https://www.amolshukla.online/courses/stock-marketing/53)

#### International & Global Diversification for Indian Investors

- [Why Indian Investors Should Look Beyond India](https://www.amolshukla.online/courses/stock-marketing/61)
- [Global Markets & International Stock Analysis](https://www.amolshukla.online/courses/stock-marketing/62)
- [Tax Planning for International Investments](https://www.amolshukla.online/courses/stock-marketing/63)

#### Advanced Stock Valuation Techniques

- [Discounted Cash Flow (DCF) Valuation — The Gold Standard](https://www.amolshukla.online/courses/stock-marketing/71)
- [Relative Valuation — P/E, EV/EBITDA, and Comparables](https://www.amolshukla.online/courses/stock-marketing/72)
- [Sum-of-Parts, Asset-Based Valuation & Other Methods](https://www.amolshukla.online/courses/stock-marketing/73)

#### Reading Annual Reports & Indian Company Research

- [How to Read an Indian Annual Report — A Complete Guide](https://www.amolshukla.online/courses/stock-marketing/81)
- [SEBI Filings, BSE/NSE Disclosures & Regulatory Research](https://www.amolshukla.online/courses/stock-marketing/82)
- [Building a Company Research Process — From Information to Decision](https://www.amolshukla.online/courses/stock-marketing/83)

#### Sector & Industry Analysis for Indian Stock Investors

- [Understanding Indian Sectors — A Complete Guide](https://www.amolshukla.online/courses/stock-marketing/91)
- [Sector Rotation & Business Cycle Investing](https://www.amolshukla.online/courses/stock-marketing/92)
- [Building a Sector-Optimized Portfolio](https://www.amolshukla.online/courses/stock-marketing/93)

#### Risk Management & Capital Preservation for Indian Investors

- [Understanding Investment Risk — Types, Measures, and Management](https://www.amolshukla.online/courses/stock-marketing/101)
- [Portfolio Insurance, Hedging & Crisis Management](https://www.amolshukla.online/courses/stock-marketing/102)
- [Building Long-Term Wealth — The Final Framework](https://www.amolshukla.online/courses/stock-marketing/103)

### Complete Prompt Engineering Course: From Basics to Mastery

Master the art of crafting effective prompts for ChatGPT, Claude, and other AI models to 10x your productivity. 4 modules, 12 lessons, 40 hrs. Difficulty: Beginner.

- [Course hub](https://www.amolshukla.online/courses/prompt-engineering): Syllabus, module list and every lesson in one page.

#### Module 1: Foundations of Prompt Engineering

- [Lesson 1: Introduction to Prompt Engineering](https://www.amolshukla.online/courses/prompt-engineering/intro-prompt-engineering): What is prompt engineering, why it matters in 2026, and how LLMs process your inputs.
- [Lesson 2: Core Prompting Techniques](https://www.amolshukla.online/courses/prompt-engineering/core-prompting-techniques): Master zero-shot, few-shot, chain-of-thought, and other essential prompting strategies.
- [Lesson 3: Structured Outputs & Prompt Templates](https://www.amolshukla.online/courses/prompt-engineering/structured-outputs): Master techniques for getting consistent, structured outputs and building reusable prompt templates.

#### Module 2: Advanced Prompting Techniques

- [Lesson 4: Chain-of-Thought Variants](https://www.amolshukla.online/courses/prompt-engineering/chain-of-thought-variants): Explore different chain-of-thought approaches: zero-shot CoT, few-shot CoT, and auto-CoT.
- [Lesson 5: Self-Reflection & Self-Critique](https://www.amolshukla.online/courses/prompt-engineering/self-reflection-techniques): Teach AI models to evaluate and improve their own outputs through reflection techniques.
- [Lesson 6: Error Recovery & Edge Case Handling](https://www.amolshukla.online/courses/prompt-engineering/error-recovery-patterns): Learn techniques for handling errors, edge cases, and unexpected outputs in your prompts.

#### Module 3: Domain-Specific Prompting

- [Lesson 7: Prompting for Code Generation](https://www.amolshukla.online/courses/prompt-engineering/prompting-for-code): Write effective prompts for generating, debugging, and refactoring code across programming languages.
- [Lesson 8: Prompting for Content & Writing](https://www.amolshukla.online/courses/prompt-engineering/prompting-for-writing): Master prompts for blog posts, marketing copy, technical writing, and creative content.
- [Lesson 9: Prompting for Data Analysis & Research](https://www.amolshukla.online/courses/prompt-engineering/prompting-for-analysis): Master prompts for analyzing data, conducting research, and generating insights.

#### Module 4: Building AI Workflows

- [Lesson 10: Prompt Chaining & Sequential Processing](https://www.amolshukla.online/courses/prompt-engineering/prompt-chaining): Connect multiple prompts to build complex workflows where each output feeds into the next.
- [Lesson 11: Multi-Agent Orchestration](https://www.amolshukla.online/courses/prompt-engineering/multi-agent-orchestration): Coordinate multiple AI agents to work together on complex tasks, each specializing in different aspects.
- [Lesson 12: Real-World Applications & Case Studies](https://www.amolshukla.online/courses/prompt-engineering/real-world-applications): Apply everything you've learned to real-world scenarios: content creation, customer service, data analysis, and more.

### Data Science: NumPy, pandas, Matplotlib, seaborn & Plotly

Learn five data-analysis libraries through guided notebook workshops, downloadable sales data and Easy-to-Professional practical assignments. 10 modules, 15 lessons, 50 hrs. Difficulty: Beginner.

- [Course hub](https://www.amolshukla.online/courses/data-science): Syllabus, module list and every lesson in one page.

#### NumPy: foundations to professional practice

- [NumPy foundations: arrays, shapes and indexing](https://www.amolshukla.online/courses/data-science/numpy-arrays-indexing): Move from a Python list to a typed array, inspect shape and select rows with slices and boolean masks.
- [NumPy applied: broadcasting, statistics and safe division](https://www.amolshukla.online/courses/data-science/numpy-broadcasting-statistics): Calculate revenue, weighted prices and normalized features with explicit axis and zero-division checks.

#### pandas: foundations to professional practice

- [pandas foundations: loading, selecting and validating data](https://www.amolshukla.online/courses/data-science/pandas-load-clean-validate): Read a CSV, inspect types, select rows and repair a controlled dirty copy without silently changing the original.
- [pandas applied: groupby, joins, pivots and time series](https://www.amolshukla.online/courses/data-science/pandas-group-merge-time): Turn row-level sales into reconciled regional and monthly reports, validating joins before they duplicate revenue.

#### Matplotlib: foundations to professional practice

- [Matplotlib foundations: figures, axes and honest charts](https://www.amolshukla.online/courses/data-science/matplotlib-chart-foundations): Create labelled bar, line and scatter charts using the explicit Figure/Axes API.
- [Matplotlib professional layouts, annotations and export](https://www.amolshukla.online/courses/data-science/matplotlib-report-layout): Compose consistent multi-panel reports, annotate findings and export a reproducible chart pack.

#### seaborn: foundations to professional practice

- [seaborn foundations: distributions and category comparisons](https://www.amolshukla.online/courses/data-science/seaborn-distributions-categories): Use tidy DataFrames with histplot, scatterplot and boxplot while distinguishing transactions from aggregates.
- [seaborn applied: faceting, correlation and honest EDA](https://www.amolshukla.online/courses/data-science/seaborn-facets-correlation): Separate groups with facets, inspect correlations and write cautious exploratory conclusions.

#### Plotly: foundations to professional practice

- [Plotly foundations: interactive charts and useful hover data](https://www.amolshukla.online/courses/data-science/plotly-interactive-foundations): Build interactive bar, scatter and line charts with deliberate hover fields and export them without a hosted account.
- [Plotly professional workflow: subplots and an analytical handoff](https://www.amolshukla.online/courses/data-science/plotly-professional-dashboard): Compose an offline two-panel sales report with definitions, source data and a clear business conclusion.

#### Module 1: Foundations of Python & Ecosystem

- [1: The Python Data Science Ecosystem](https://www.amolshukla.online/courses/data-science/python-basics): Jupyter setup, virtual environments, variables, data structures, and list comprehensions.

#### Module 2: Vectorized Computing & Wrangling

- [2: NumPy Arrays & Pandas Wrangling](https://www.amolshukla.online/courses/data-science/numpy-pandas): N-dimensional arrays, vectorized functions, DataFrame operations, grouping, and handling missing data.

#### Module 3: Visual Analytics

- [3: Visualizing Patterns with Seaborn & Matplotlib](https://www.amolshukla.online/courses/data-science/visualization): Building distribution charts, relational scatter plots, and correlation heatmaps.

#### Module 4: Machine Learning Foundations

- [4: Predictive Modeling with Scikit-Learn](https://www.amolshukla.online/courses/data-science/supervised-learning): Features vs Labels, train-test splitting, fitting Linear Regression, and checking MSE / R2.

#### Module 5: Agentic AI Pipelines

- [5: Developing Intelligent Agentic Data Pipelines](https://www.amolshukla.online/courses/data-science/agentic-ai): Tool-calling patterns, ReAct architecture, safety sandboxing, and orchestrating analytical agents.

## Articles

- [I Crawled All 131 Pages of My Next.js Portfolio: What the Technical SEO Audit Found](https://www.amolshukla.online/blogs/i-crawled-all-131-pages-of-my-nextjs-portfolio): A full crawl of amolshukla.online's 131 URLs, the two real SEO defects it exposed, and the Next.js App Router fixes that resolved them. (2026-11-01; tags: SEO, Next.js, Technical SEO, Web Development, Case Study)
- [Designing a Website That LLMs Can Actually Read: llms.txt, Schema, and Server-Rendered HTML](https://www.amolshukla.online/blogs/designing-a-website-that-llms-can-read): How amolshukla.online is structured for AI crawlers: a generated llms.txt, JSON-LD, and server-rendered lesson notes — and where the trade-offs are. (2026-10-31; tags: AI, LLMs, SEO, AEO, Next.js)
- [Shipping 104 Free Lessons Without a CMS: How the Next.js Learning Hub Is Built](https://www.amolshukla.online/blogs/shipping-104-free-lessons-without-a-cms): Six courses and 104 lessons live as typed config, prerendered at build time. The content architecture, the crawler trade-offs, and how to add a lesson in three steps. (2026-10-29; tags: Next.js, Content Architecture, Education, React, Static Site Generation)
- [From Stateless JWT to Verified Sessions: Hardening Portal Auth in Next.js 16](https://www.amolshukla.online/blogs/verified-sessions-hardening-nextjs-portal-auth): When users say sign-in 'doesn't work', the credentials are often fine. Three real defects behind that complaint, and the session design that fixes them. (2026-10-27; tags: Authentication, Next.js, Security, JWT, MongoDB)
- [Designing an Agentic AI Curriculum: What 10 Modules of LLM Engineering Should Cover](https://www.amolshukla.online/blogs/designing-an-agentic-ai-curriculum): Why a 10-module, 26-lesson agentic AI course teaches LLM internals before prompting tricks — and what it deliberately leaves out. (2026-10-25; tags: AI, Agentic AI, LLM, Prompt Engineering, Curriculum)
- [Deep Dive into Transformers and Self-Attention Mechanisms](https://www.amolshukla.online/blogs/deep-dive-into-transformers-and-self-attention-mechanisms): An intuitive and mathematical breakdown of self-attention, multi-head attention, and how transformers revolutionized natural language processing. (2026-08-01; tags: AI, Transformers, NLP, Deep Learning)
- [How to Build an LLM RAG Pipeline with Next.js and FastAPI](https://www.amolshukla.online/blogs/how-to-build-an-llm-rag-pipeline-with-nextjs-and-fastapi): Step-by-step guide to building a production-ready Retrieval-Augmented Generation (RAG) system using Next.js on the frontend and FastAPI on the backend. (2026-07-22; tags: AI, RAG, Next.js, FastAPI, LLM)
- [Mastering Python Generators for Memory-Efficient Data Pipelines](https://www.amolshukla.online/blogs/mastering-python-generators-for-memory-efficient-data-pipelines): Learn how to use Python generators and yield statements to process massive datasets without running out of RAM. (2026-07-12; tags: Python, Generators, Performance, Data Engineering)
- [Understanding Vector Databases: Pinecone vs Chroma vs Qdrant](https://www.amolshukla.online/blogs/understanding-vector-databases-pinecone-vs-chroma-vs-qdrant): Compare top vector databases on performance, ease of use, self-hosting options, and suitability for production LLM systems. (2026-06-22; tags: AI, Vector Databases, Pinecone, ChromaDB, Qdrant)

## Projects

- [Portfolio Card](https://www.amolshukla.online/projects/portfolio-card): Forged an immersive 3D Portfolio Card utilizing the prowess of Three.js and Blender, where art and technology converge in an interactive masterpiece. Conceptualized and realized a distinct portfolio card using Three.js, highlighting creative exploration. Crafted a mesmerizing 3D model enhanced by thoughtful lighting arrangements, resulting in a captivating visual voyage.
- [AI-Powered Academic Learning Assistant](https://www.amolshukla.online/projects/academic-learning-assistant): An LLM-based tutoring assistant designed to support learners in DSA, DBMS, and Python with context-aware answers. Designed a context-aware learning assistant for DSA, DBMS, and Python. Improved student engagement through faster, relevant answers.
- [Smart Resume Analyzer](https://www.amolshukla.online/projects/smart-resume-analyzer): An AI-driven resume screening system that compares resumes against job descriptions and generates ATS-friendly suggestions. Created an AI resume screening workflow using NLP and LLMs. Generated skill-gap insights and keyword recommendations for better job matching.
- [Ikigai School of AI Website](https://www.amolshukla.online/projects/ikigai-website): Built a professional, modern website for Ikigai School of AI to present its programs, training, and learning experience. Built a polished website for Ikigai School of AI. Focused on clarity, modern design, and better presentation of training offerings.

## Experience

- [Full-Time AI Trainer — Ikigai School of AI](https://www.amolshukla.online/experience/ikigai-ai-trainer): India, 2026–present. Deliver full-time training in Python, AI, and Data Science to aspiring developers and data professionals.
- [Full-Time Developer — Indian Digital Rey](https://www.amolshukla.online/experience/indian-digital-rey-developer): India, 2023–present. Develop and ship production software across the full stack — React and Next.js front-ends backed by robust Python services.
- [PHP + AI Trainer — RCPL](https://www.amolshukla.online/experience/rcpl-trainer): India, 2025–2025. Conducted dual-track training in PHP development and artificial intelligence.
- [AI & Python Trainer — True Mind](https://www.amolshukla.online/experience/true-mind-trainer): India, 2024–2024. Delivered end-to-end training in Python from fundamentals to OOPs, file handling, and modular coding.
- [Big Data & Data Science Trainer — Hitbull Eye](https://www.amolshukla.online/experience/hitbull-eye-trainer): India, 2024–2024. Delivered specialized training in Big Data technologies and data science workflows.

## Practice datasets

- [MySQL retail practice database](https://www.amolshukla.online/downloads/mysql-retail-lab.sql): Learning Shop: 6 customers, 6 products, 8 orders, 14 order lines (schema portfolio_retail_lab). MySQL 8.0.16+. No DROP statements; safe to re-run.
- [MySQL practice database reset](https://www.amolshukla.online/downloads/mysql-retail-lab-reset.sql): Drops and recreates portfolio_retail_lab with the original rows. Practice servers only.
- [MySQL large practice dataset](https://www.amolshukla.online/downloads/mysql-retail-large.sql): 300 customers, 40 products, 4,000 orders (2025-01 to 2026-06), schema portfolio_retail_large; for window functions, indexes and EXPLAIN.
- [Data-science sales CSV](https://www.amolshukla.online/downloads/data-science-sales.csv): Twelve fictional orders for NumPy, pandas and visualisation workshops; includes returned orders.

## Optional

- [RSS feed](https://www.amolshukla.online/feed.xml): Newest articles first, updated on publish.
- [Sitemap](https://www.amolshukla.online/sitemap.xml): Every indexable URL on the site.
- [llms.txt](https://www.amolshukla.online/llms.txt): This file.