Ultimate Learning Pack

HTML 5

• Basic overview, document structure, semantic tags
• Basic tags, lists, DIV & SPAN, attributes
• HTML Forms, Labels, Inputs and validation

CSS 3

• Introduction, selectors, basic tag styling
• Backgrounds & borders, classes & IDs
• Inspecting elements, fonts, display, margins & paddings
• Animations & transitions

Bootstrap 4

• Bootstrap overview, grid system
• Buttons, forms, navbars, typography

JavaScript (ES6) & Core

• Intro to JavaScript: var/let/const, data types, strings & arrays
• Control flow: conditionals, switch, loops
• Functions, window object, basic DOM, selectors
• Create/remove/replace elements, events & handlers
• Local & session storage, Math & Date objects
• OOP in JS: prototypes, prototype inheritance, ES6 classes
• Asynchronous programming concepts

AJAX & Advanced JS

• Callback functions, Promises, Arrow functions
• Fetch API, Async/Await, error handling
• Regular expressions, metacharacters, character sets
• Iterators, generators, Maps, Sets, Symbols, destructuring
• jQuery essentials

React JS

• Introduction, create-react-app, JSX fundamentals
• Props & PropTypes, state & events, class & function components
• React Router, fetch API in React, pagination, infinite scroll
• Hooks, converting class components to function components
• Dark mode, favicon & meta management, spinners & loaders

Python Basics

• Setup (VS Code), virtual environments
• Syntax, variables, data types, operators
• Control flow: if/elif/else, loops
• Data structures: lists (comprehensions), tuples, sets, dictionaries

Functions & Modules

• Functions, lambda, map, filter
• Modules & packages, standard library

File Handling & Exceptions

• File operations, paths, exception handling (try/except/else/finally)
• Custom exceptions

OOP & Advanced

• Classes & objects, inheritance, polymorphism, encapsulation, abstraction
• Magic methods, operator overloading
• Iterators, generators, closures, decorators

Data Analysis

• NumPy basics, Pandas DataFrame & Series
• Data manipulation & I/O, visualization with Matplotlib & Seaborn
• Reading data from CSV/Excel/DBs

SQLite & Concurrency

• sqlite3 CRUD with Python
• Logging (multiple loggers), multithreading & multiprocessing concepts & practice

• Flask introduction and app skeleton
• Integrating HTML with Flask, Jinja2 templating
• HTTP verbs (GET, POST), dynamic URLs and variable rules
• REST APIs with PUT & DELETE
• Streamlit apps for quick data dashboards

Statistics & Probability

• Population vs sample, measures of central tendency & dispersion
• Variance, standard deviation, histograms, percentiles & quartiles
• Correlation, covariance, probability rules, PDF/PMF/CDF
• Common distributions: Bernoulli, Binomial, Poisson, Normal, LogNormal, Uniform, Pareto
• Central Limit Theorem, estimates

Inferential Statistics

• Hypothesis testing, p-value, Z-test, t-test, chi-square, ANOVA
• Type I & II errors, Bayes theorem, confidence intervals

Feature Engineering & EDA

• Missing values, outliers, imbalanced datasets
• Encoding: One-Hot, Label, Ordinal, Target guided
• Practical EDA (example: Wine dataset) — cleaning, visualization, feature selection

Machine Learning

• Intro: ML types, linear algebra intuition
• Linear regression (simple, multiple), cost function, convergence, metrics (MSE, MAE, RMSE)
• Regularization: Ridge, Lasso, ElasticNet; cross-validation; hyperparameter tuning
• Classification: Logistic Regression, SVM, Naive Bayes, KNN
• Tree-based: Decision Trees, Random Forest, AdaBoost, Gradient Boosting
• Unsupervised: PCA, K-Means, Hierarchical, DBSCAN
• NLP basics, Deep Learning intro & Transformers overview

• Real-time portfolio projects (web apps, dashboards, ML models)
• Code reviews, GitHub best practices, resume & interview prep
• Internship project delivery and certificate issuance (4 certificates)