Full Stack Artificial Intelligence

What you'll learn

This comprehensive Web Development with AI & Machine Learning course is designed to transform you into a modern full-stack developer capable of building intelligent, data-driven web applications. From creating responsive user interfaces to developing powerful backend systems and integrating Artificial Intelligence, Machine Learning, and Data Science capabilities, this program covers the complete development lifecycle.

You will gain hands-on experience building real-world projects using HTML, CSS, JavaScript, Bootstrap, Django, Machine Learning models, and AI-powered features. Throughout the course, you will learn how to develop scalable web applications, create RESTful APIs, implement intelligent automation, analyze data, and deploy production-ready solutions on cloud platforms.

The curriculum emphasizes practical learning through industry-relevant projects such as AI-powered web applications, predictive analytics dashboards, recommendation systems, intelligent chatbots, data visualization platforms, and machine learning-enabled business solutions.

By the end of this course, you will be confident in designing, developing, integrating, and deploying modern web applications enhanced with Artificial Intelligence and Data Science capabilities, preparing you for careers as a Full Stack Developer, AI Web Developer, Machine Learning Engineer, Python Developer, Data Science Developer, or Intelligent Application Engineer.

  • Fundamentals of Modern Web Development and Full Stack Architecture.
  • Building responsive user interfaces using HTML5, CSS3, JavaScript, and Bootstrap.
  • Developing secure and scalable backend applications using Django and Django REST Framework.
  • Designing and managing databases with PostgreSQL, MySQL, MongoDB, and Redis.
  • Creating RESTful APIs and integrating third-party services.
  • Applying Machine Learning models within web applications for intelligent decision-making.
  • Working with Data Science workflows, data preprocessing, visualization, and analytics.
  • Integrating Artificial Intelligence, Generative AI, and Large Language Models (LLMs) into web solutions.
  • Building AI-powered chatbots, recommendation systems, and predictive applications.
  • Using Git, GitHub, Docker and cloud deployment workflows.
  • Deploying scalable web, AI, and machine learning applications on production environments.
  • Developing end-to-end intelligent web solutions from concept to deployment.

Course Content

  • HTML Basics : Introduction to HTML, HTML Document Structure, Elements & Tags, Attributes, Head & Body Tags
  • Text & Content Elements : Headings, Paragraphs, Line Breaks, Formatting Tags, Lists, Blockquote, Code Elements
  • Links & Navigation : Internal Links, External Links, Anchor Links, Navigation Menus, Breadcrumb Navigation
  • Images & Multimedia : Images, SVG, Audio, Video, Iframes
  • Tables : Table Structure, Table Styling, Responsive Tables
  • Forms : Form Elements, Input Types, Labels, Validation, Checkboxes, Radio Buttons, Select Dropdowns, Textarea, File Upload
  • Semantic HTML : Header, Footer, Main, Section, Article, Aside, Nav
  • CSS Introduction : Inline CSS, Internal CSS, External CSS, CSS Selectors, CSS Specificity
  • Typography & Styling : Fonts, Colors, Text Styling, Google Fonts, Icons
  • Box Model : Width, Height, Padding, Margin, Border
  • Backgrounds : Colors, Images, Gradients, Overlays
  • Positioning : Static, Relative, Absolute, Fixed, Sticky
  • Display Properties : Block, Inline, Inline-Block, Flexbox, Grid
  • Responsive Design Fundamentals : Mobile First Design, Breakpoints, Responsive Units
  • Flexbox Layout : Flex Container, Flex Items, Alignment, Wrapping
  • CSS Grid : Grid Layout, Grid Columns, Grid Rows, Grid Areas
  • vMedia Queries : Mobile Devices, Tablets, Desktop Screens
  • Modern UI Components : Buttons, Cards, Pricing Tables, Testimonials, Team Sections
  • Animations : CSS Transitions, Transformations, Keyframes, Hover Effects
  • Advanced Effects : Glassmorphism, Neumorphism, Shadows, Gradients
  • Bootstrap Fundamentals : Bootstrap Setup, Grid System, Containers, Utilities
  • Layout Components : Navbar, Hero Sections, Responsive Grids, Cards
  • Forms & UI Components : Forms, Buttons, Alerts, Badges, Progress Bars
  • Interactive Components : Modal Windows, Accordion, Carousel Slider, Tabs, Offcanvas Menu
  • Responsive Utilities : Spacing, Display Classes, Responsive Visibility
  • JavaScript Basics : Variables, Data Types, Operators, Functions
  • Control Flow : Conditions, Loops, Switch Statements
  • Arrays & Objects : Array Methods, Object Manipulation, JSON Basics
  • Functions : Arrow Functions, Callback Functions, Scope
  • DOM Fundamentals : Selecting Elements, Modifying Elements, Event Handling
  • User Interaction : Click Events, Form Events, Keyboard Events
  • Dynamic UI : Dynamic Content, Show/Hide Elements, Creating Elements
  • Modern JavaScript ES6+ : Let & Const, Template Literals, Destructuring, Spread Operator
  • Asynchronous JavaScript : Promises, Async/Await, Fetch API
  • API Integration : Working with REST APIs, JSON Data Handling
Intro Course content
  • What is Artificial Intelligence?
  • AI vs Machine Learning vs Deep Learning
  • IDE Setup (VS Code, Jupyter)
  • Real-world applications of AI
  • Tools & technologies overview
  • About Python
  • How to Python Work
  • Python setup & environment
  • Python Virtual Machine
  • Installing Python (Anaconda / Standard)
  • Running Python scripts
  • Python Output/print function
  • Syntax, Comments
  • Variables, Data Types, Built in Data Types
  • Type Casting & Type Checking
  • Python Literal, Operators, Keywords and Identifiers, User Input
  • If, Elif, Else
  • Nested Conditions
  • Loops (For, While)
  • Break, Continue, Pass
  • Comprehensions
  • String : String indexing, String slicing, Edit and delete a string, Operations on String, Common String functions
  • List : Create and access a add items, Edit items in a list, Deleting items from a list, Arithmetic, membership and loop operations on a List, Various List functions, List comprehension, Zip() function
  • Tuple : Create and access a tuple, Can we edit and add items to a tuple?, Deletion, Operations on tuple, Tuple functions, List vs tuple, Tuple unpacking, Zip () on tuple
  • Set : Create and access a set, Can we edit and add items to a set?, Deletion, Operations on set, set functions, Frozen set (immutable set), Set comprehension
  • Dictionary : Create dictionary, Accessing items, Add, remove, edit key-value pairs, Operations on dictionary, Dictionary functions, Nested comprehension
  • Defining & Calling Functions
  • Parameters & Arguments
  • Return Values
  • Default & Keyword Arguments
  • *args and **kwargs
  • Lambda Functions
  • Scope (local, global)
  • Nested functions
  • Introduction to OOP
  • Classes & Objects
  • Attributes & Methods
  • Constructor (__init__), self Keyword
  • Encapsulation, Abstraction, Inheritance, Polymorphism
  • Special Methods (__str__, __add__, etc.)
  • How objects access attributes
  • Iterators and Iterable Objects in Python
  • Decorators for Code Reusability and Abstraction
  • Generators for Efficient Data Processing
  • Text Processing Using Regular Expressions (re Module)
  • Package Installation (pip)
  • Importing Modules
  • Standard Library (os, sys, math, random, datetime)
  • Virtual Environments (venv, pipenv, conda)
  • Reading & Writing Files (txt, json)
  • File modes (r, w, a, rb, wb)
  • Exception Handling with Files
  • JSON Handling (json module)
  • Pickle
  • Try, Except, Finally
  • Raise Exceptions
  • Introduction to Arrays
  • List vs Arrays
  • Array Operations
  • What is Time Complexity?
  • Big-O Notation
  • Common Complexity Examples
  • Introduction to Django Framework
  • Setting Up Virtual Environment
  • Creating Your First Django Project
  • Understanding Django Project Structure
  • Running Development Server
  • Creating Django Applications
  • URL Routing Basics
  • Django Templates Introduction
  • Template Syntax & Variables
  • Template Tags & Filters
  • Static Files Management
  • Creating Reusable Layouts
  • Template Inheritance
  • Navigation & Dynamic Content
  • Form Validation
  • File Uploads & Image Uploads
  • Search Functionality
  • Contact Forms Development
  • User Registration System
  • Login & Logout Functionality
  • Password Hashing & Security
  • Authentication System
  • Profile Management
  • Password Reset System
  • Sending Emails in Django
  • Payment Gateway Integration
  • Google Maps Integration
  • Password Reset System
  • Introduction to Numpy
  • Numpy Fundamentals
  • Numpy Basics
  • Array Indexing & Slicing
  • Array Operations
  • Mathematical Functions
  • Array Shape & Reshaping
  • Stacking & Splitting
  • Random Module
  • Advanced Numpy
  • Introduction to Pandas
  • Pandas Data Structures
  • Data Inspection
  • Data Selection & Indexing
  • Data Cleaning
  • Data Transformation
  • Sorting & Ranking
  • Grouping & Aggregation
  • Combining & Merging Data
  • Reshaping & Pivoting
  • Advanced Pandas
  • Matplotlib : 2D Plot / 3D Plot (Line plot, Scatter plot, Bar plot, Stem plot, Step plot, Histogram plot, Box plot, Pie chart, Subplots, Violin plot, Stacked bar chart, Contour plot, Contourf plot, Heatmap)
  • Seaborn : Line plot, Scatter plot, Dis plot, Cat plot, Rel plot, Hist plot, Kde plot, Rug plot, Bar plot, Violin plot, Heatmap, Cluster map, Strip plot, Joint plot
  • Plotly : Plotly Go, Plotly Express, Dash, Line plots, Scatter plots, Bar charts, Pie charts, Donut charts, Bubble charts, Histogram, Box plot, Violin plot, Strip plot, Waterfall charts
  • Dashboards with Plotly : Basic Dash app structure, Linking Plotly figures to Dash, Advance Dash app
  • All functions of plots
  • All changing styles of plots
  • Project using Dashboard with Plotly
  • Requests
  • BeautifulSoup
  • Selenium
  • Introduction to Statistics
  • Types of Statistics
  • Basics of Data : Population vs. Sample, Raw data vs. Grouped data, Types of data, Frequency distribution
  • Measures of Central Tendency : Mean, Median, Mode, Weighted Mean, Trimmed Mean (robust measure)
  • Measures of Dispersion (Spread of Data) : Range, Standard Deviation, Variance, Coefficient of Variation, Interquartile Range (IQR)
  • Measures of Position (Relative Standing) : Percentiles, Quartiles (Q1, Q2, Q3), Z-scores (Standard scores)
  • Measures of Shape : Skewness
  • Data Visualization in Descriptive Statistics : Categorical – Categorical, Numerical – Numerical, Categorical – Numerical
  • Hypothesis Testing
  • Null and alternate hypothesis
  • Type I & Type II errors
  • p-value interpretation
  • One-tailed vs. Two-tailed tests
  • Z-test, T-test, Chi-square test
  • ANOVA
  • 2D density plots
  • Normal Distribution
  • Standard Normal Variate
  • Properties of Normal Distribution
  • Skewness
  • CDF of Normal Distribution
  • QQ plot
  • Uniform Distribution
  • Log-normal distribution
  • Power Transformer
  • Log Transform
  • Box-Cox Transform
  • Yeo-Johnson Transformation
  • Data Understanding
  • Data Collection & Import
  • Data Cleaning
  • Feature Engineering
  • Statistical Testing
  • Visualization & Reporting
  • Feature Transformation : Standardization, Normalization, Log transformation, Power transformations, K-Nearest Neighbors
  • Feature Selection : Filter methods, Wrapper methods, Embedded methods
  • Feature Extraction : PCA, t-SNE
  • Handling Missing Values : Mean, Median, Mode, KNN imputer, Handling duplicates
  • Encoding Categorical Variables : One-Hot Encoding, Label Encoding, Target Encoding
  • Outlier detection & treatment : IQR, Z-Score, Percentile
  • Project
  • Introduction to Machine Learning
  • Types of ML
  • ML vs AI vs DL vs Data Science
  • Regression Algorithms : Linear Regression, Multiple Linear Regression, Polynomial Regression, Lasso Regression (L1 Regularization), Ridge Regression (L2 Regularization), Support Vector Regression (SVR), Decision Tree Regression, Random Forest Regression, Gradient Boosting Regression, XGBoost, CatBoost
  • Classification Algorithms : Logistic Regression, K-Nearest Neighbors (KNN), Naïve Bayes Classifier, Decision Tree Classifier, Random Forest Classifier, Support Vector Machines (SVM), Gradient Boosting Classifier, XGBoost, CatBoost, AdaBoost, Bagging
  • Clustering : K-Means Clustering, Hierarchical Clustering, DBSCAN, Gaussian Mixture Models (GMM)
  • Dimensionality Reduction : Principal Component Analysis (PCA), t-SNE (t-distributed Stochastic Neighbor Embedding)
  • Self-Training
  • Label Propagation
  • Introduction to RL
  • Markov Decision Process (MDP)
  • Value-Based Methods
  • Regression: MSE, RMSE, MAE, R²
  • Classification: Accuracy, Precision, Recall, F1-Score
  • Confusion Matrix & Cross-Validation
  • Data collection
  • Overfitting and Underfitting
  • What is Git?
  • What is VCS/SCM?
  • How Git works?
  • Installing git
  • Creating and Cloning repository
  • add, commit, add, gitignore
  • seeing commits
  • Nonlinear Development (Branching)
  • Merging branches
  • Working with a remote repository
  • Introduction to Deep Learning : What is Deep Learning?, Difference between ML, DL, AI, Perceptron & Biological Neuron Analogy
  • Neural Network Fundamentals : Perceptron Model, Activation Functions, Loss Functions, Forward Propagation, Backpropagation Algorithm, Gradient Descent
  • Training Deep Neural Networks : Weight Initialization, Optimization Algorithms, Learning Rate Scheduling, Dropout (Regularization)
  • Deep Learning Frameworks : TensorFlow, Keras
  • ANN : Biological neuron vs Artificial neuron, Perceptron model, Inputs, Weights, Bias, Weighted sum, Activation function, Single-layer perceptron, Multi-layer perceptron (MLP), Input layer, Hidden layers, Output layer, Fully connected layers, Loss / Cost Functions, Backpropagation, Improving ANN Performance, Mathematical Foundations, Training ANN Models
  • CNN : Convolution layers, Convolution Operation, Pooling layers, Activation & Loss Functions,Training CNN Models, Regularization Techniques, Model Evaluation, Fully connected layers
  • RNN : RNN cell, Types of RNN, Mathematical Foundations, Backpropagation Through Time, Training RNN Models
  • LSTM : LSTM Architecture Components, Gates in LSTM, LSTM Cell Equations, Types of LSTM Architectures, Training LSTM Models, Data Preparation for LSTM, LSTM for Speech & Audio, Advanced LSTM Concepts, Model Evaluation
  • GRU : Introduction to GRU, GRU Architecture, GRU Mathematical Formulation, Forward Pass in GRU, Training GRU Models
  • Introduction to Databases & SQL : What is a Database?, CRUD operations, Types of Database, DBMS
  • SQL : Database, Table, Row, Column concepts, SQL Data Types, Creating & Dropping Databases, Creating & Dropping Tables, Altering Tables
  • CRUD Operations : INSERT INTO, SELECT, UPDATE, DELETE
  • Filtering & Sorting : WHERE, Logical Operators, Comparison Operators, Sorting Data, Limiting Results
  • SQL Functions : Aggregate Functions, String Functions, Date & Time Functions, Mathematical Functions
  • Grouping Data, GROUP BY, HAVING
  • SQL Joins : INNER JOIN, LEFT JOIN, RIGHT JOIN, FULL OUTER JOIN, CROSS JOIN, SELF JOIN
  • SQL Subqueries : Subquery in SELECT, Subquery in WHERE, Nested Queries
  • SQL Set Operations : UNION vs. UNION ALL, EXCEPT, INTERSECT
  • Keys : Primary Key, Foreign Key, Unique Key, Candidate Key, Super Key, Composite Key, Surrogate Key
  • Triggers : What are Triggers?, BEFORE & AFTER Triggers
  • Advanced Functions : Window Functions, Pivot, Case Statements
  • SQL Projects
  • Introduction to NLP
  • Text Preprocessing : Tokenization, Stopword Removal, Lowercasing, normalization, Stemming, Lemmatization, Handling Punctuation, Numbers, Case Normalization, Handling Emojis & Special Characters, Remove URLs, Spelling correction, Keyword extraction
  • Text Representation (Feature Engineering) : Bag of Words, One Hot Encoding, N-grams/Bi-grams/Tri-grams, TF-IDF, Word2Vec
  • Classical NLP Algorithms : Text Classification, Language Modeling, Latent Semantic Analysis (LSA), Latent Dirichlet Allocation (LDA)
  • Sequence Modeling : Recurrent Neural Networks (RNNs), LSTMs, GRUs, Seq2Seq Models
  • Model evaluation : Accuracy, Precision, Recall, F1-score, Confusion Matrix
  • NLTK
  • Advanced Architectures : Encoder-Decoder models, Sequence-to-sequence learning, Speech-to-text, Text-to-speech
  • NLP Project
  • Introduction to Power BI
  • Data Sources & Connectivity
  • Data Cleaning with Power Query
  • Data Modeling
  • DAX (Data Analysis Expressions)
  • Data Visualization
  • Dashboard Design
  • Advanced Analytics
  • Power BI Project
  • Introduction to Data Visualization & Tableau
  • Data Connections
  • Data Preparation
  • Data Modeling
  • Calculations in Tableau
  • Data Visualization
  • Dashboard Design
  • Advanced Analytics
  • Tableau Project
  • Introduction to FastAPI
  • Background Processing
  • Creating your first FastAPI app
  • Path parameters
  • Query parameters
  • Request body
  • Response models
  • GET, POST, PUT, DELETE
  • Flow Diagram
  • Databases
  • Request Handling
  • Testing FastAPI
  • Introduction to MongoDB
  • MongoDB Basics : Database, collection, document structure, CRUD operations
  • Querying Documents
  • Data Modeling in MongoDB
  • Aggregation
  • Advanced MongoDB
  • Foundations of Generative AI
  • Large Language Models (LLMs)
  • LangChain
  • LangGraph
  • Hugging Face
  • Ollama
  • Retrieval-Augmented Generation (RAG)
  • AI Agents
  • LLMOps
  • Text Generation Systems
  • Image Generation
  • All Topics Interview Questions & Answers
  • All Topics Coding Practice
  • Real-world Problem Solving
  • Total Duration : 550+ hours
  • Lectures : 270+
  • Skill Level : Expert
  • Language : Hindi
  • Industry Based Course Curriculum
  • Weekdays & Weekend Classes
  • Resume & LinkedIn Profile Building
  • Regular Assessments
  • Quiz
  • Hands-on Practice & Projects
  • Globally Recognized Certification
  • Interview Preparation & Mock Interview Session
  • 100% Job Assistance
  • 24/7 Student Support
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