Full Stack AI Web Course (Comprehensive, MVT)

The Python Philosophy
Introduction to Python
Installing Python on Windows
Installing Python on Linux, Mac

Using Mathematical Operators with Numbers
Using quotes and escape character
String Concatenation and Repeater Operators

Creating and Using Lists
Deleting List Element
len() with Lists
in Operator with Lists
Common list and operations

Understanding Dictionaries
Accessing Dictionary values
Adding, Replacing and Deleting key-value pairs
Functions: get(), keys(), values() and items()

Understanding for Loops
Using Sequence Operators and Functions with Strings
Using the in Operator
Indexing and Slicing Strings

User Defined Functions
Using Parameters and Return Values
Using Arguments and Defaults Parameters
Using Global Variables and Constants

Understanding Object-Oriented Programming
Creating Classes, Methods and Objects
Using Constructor and Attributes
Using Class Attributes and Static Methods
Understanding Object Encapsulation
Private Attributes and Methods
Controlling Attribute Access
Inheritance
Polymorphism
Best Practices

The open Function
Input from Text Files
Output to Text Files
Accessing Network Files
Handling Exceptions

Using Modules in Programs
Writing Modules
Importing Modules

MySQL / MariaDB Installation
MySQL access through command prompt
DDL and DML Queries
Creating, Altering and Dropping Tables
CRUD Queries
Python & MySQLi Usage

Extracting Data from a Webpage
Manipulating Live Data from a Webpage
Creating a Weather Reporting Project
Creating an intelligent Dictionary App

OpenCV − Image processing
Numpy and Scipy libraries − For image manipuation and processing.
Scikit − for image classification
Python Imaging Library (PIL) − operations on images like create thumnails, resize, rotation etc.

The vision of Open Source & how it works
Introduction to Full Stack Development
Networks, the Internet and Client Server architecture
HTML
Fundamentals of Programming
PHP: Basics, Introduction and Installation
PHP: Variables, Conditions, Loops, Arrays
PHP: Functions, Dynamic Variables, Classes & Objects

Introduction to DBMS & RDBMS Concepts
MySQL / MariaDB Installation and Usage
SQL Queries: DDL & DML (CRUD) Queries
MyISAM, InnoDB, Merge & Other types
Datatypes, Defaults & limits in MySQL
MySQL Functions & Advanced operations
MySQL Joining & Subqueries
PHP Libraries for MySQL programming
DB Optimisation & Security

Javascript basics
JS Variables, Loops, Conditions, Arrays
JS functions, Class & objects
JS Events and DOM Model
ES6 Advanced JS Concepts
JS AJAX
JS Animation & Gaming
JS Common Website components
JS Full complete Animated Game example
JQuery
LightBox, Highslide

Advanced OOPs in PHP
Sessions & Security
Login Logout System
PHP CURL & Web Components
File Uploading & Downloading
File Handling & Web Scraping
Thumbnails & Gallery
Pagination & Search
Captcha & Validations
Cookies & Local Storage

Advanced HTML
CSS 3
Bootstrap
Templates Creation
Responsive Websites
Favicons

CURL, Socket Programming
Internationalisation
Coding Standards
Advanced Classes & Objects
Exception Handling
Exception Handling
Interfaces, Traits
Closures & Lambda Functions
Namespaces
ODBC

MVC
Email Processing
SMTP
RESTFul APi,
Smarty Template Engine
XML, RSS, JSON
CSV & Data Sharing
SMS Notifications
OTP Generation
API Creation & Integration
Security and Hacking Safeguards

Custom Project 1
ECommerce Project 2
Project Architecture & Planning
Database Design
Template Creation and Separation
Shopping Cart – User Frontend.
Shopping Cart – Admin Backend.
Shopping Cart - Custom CMS using CK
Shopping Cart - Payment Gateway Integration
FTP & Live Project Setup

Composer Setup
Laravel - Vision & Installation
Laravel - Artisan & Configuration
Laravel - Project Structure & Usage
Laravel - Routes & Controllers
Laravel - Middleware & Templates
Laravel - Forms, Requests & Databases
Laravel - Sessions, Cookies & Validations
Laravel - Full Project From Scratch

Composer Setup
Yii/CodeIgniter - Vision & Installation
Yii/CodeIgniter - Project Structure & Usage
Yii/CodeIgniter - Routes & Controllers
Yii/CodeIgniter - Forms, Requests & Databases
Yii/CodeIgniter - Sessions, Cookies & Validations
Yii/CodeIgniter - Full Project From Scratch

WordPress - Overview & Installation
Admin Usage - Pages, Posts, Media
Admin Usage - Users, Themes, Settings
Customizing and Developing WP Themes
Customizing and Developing WP Plugins
WP Filter & Action Hooks
WP API & Common Code Library
Creating advanced WP Plugins

Self Improvement - How to become a Good Programmer
Self Improvement - Improving IQ & Solving Puzzles
Self Improvement - Improving Communication & Interpersonal skills
Self Improvement - Giving Presentations & Public Speaking
Self Improvement - Meditation, Emotional Control and Happiness
Self Improvement - Entrepreneurship & Freelancing
Making an Impressive CV
Complete Machine Test Preparation
Comprehensive Interview Preparation
Team Building Activities & Speeches
Placements & More.

How to Learn
The Right Mindset
Developing a deep Understanding
Memorizing new concepts fast
Improving Focus & Time Management

What Is Machine Learning?
Why Use Machine Learning?
Types of Machine Learning Systems
Supervised/Unsupervised Learning
Batch and Online Learning
Instance-Based Versus Model-Based Learning
Main Challenges of Machine Learning
Insufficient Quantity of Training Data
Nonrepresentative Training Data
Poor-Quality Data
Irrelevant Features
Overfitting the Training Data
Underfitting the Training Data
Stepping Back
Testing and Validating
Hyperparameter Tuning and Model Selection
Data Mismatch

Working with Real Data
Look at the Big Picture
Frame the Problem
Select a Performance Measure
Check the Assumptions
Get the Data
Create the Workspace
Download the Data
Take a Quick Look at the Data Structure
Create a Test Set
Discover and Visualize the Data to Gain Insights
Visualizing Geographical Data
Looking for Correlations
Experimenting with Attribute Combinations
Prepare the Data for Machine Learning Algorithms
Data Cleaning
Handling Text and Categorical Attributes
Custom Transformers
Feature Scaling
Transformation Pipelines
Select and Train a Model
Training and Evaluating on the Training Set
Better Evaluation Using Cross-Validation
Fine-Tune Your Model
Grid Search
Randomized Search
Ensemble Methods
Analyze the Best Models and Their Errors
Evaluate Your System on the Test Set
Launch, Monitor, and Maintain Your System

MNIST
Training a Binary Classifier
Performance Measures
Measuring Accuracy Using Cross-Validation
Confusion Matrix
Precision and Recall
Precision/Recall Tradeoff
The ROC Curve
Multiclass Classification
Error Analysis
Multilabel Classification
Multioutput Classification

Linear Regression
The Normal Equation
Computational Complexity
Gradient Descent
Batch Gradient Descent
Stochastic Gradient Descent
Mini-batch Gradient Descent
Polynomial Regression
Learning Curves
Regularized Linear Models
Ridge Regression
Lasso Regression
Elastic Net
Early Stopping
Logistic Regression
Estimating Probabilities
Training and Cost Function
Decision Boundaries
Softmax Regression

Linear SVM Classification
Soft Margin Classification
Nonlinear SVM Classification
Polynomial Kernel
Adding Similarity Features
Gaussian RBF Kernel
Computational Complexity
SVM Regression
Under the Hood
Decision Function and Predictions
Training Objective
Quadratic Programming
The Dual Problem
Kernelized SVM
Online SVMs

Training and Visualizing a Decision Tree
Making Predictions
Estimating Class Probabilities
The CART Training Algorithm
Computational Complexity
Gini Impurity or Entropy?
Regularization Hyperparameters
Regression
Instability

Voting Classifiers
Bagging and Pasting
Bagging and Pasting in Scikit-Learn
Out-of-Bag Evaluation
Random Patches and Random Subspaces
Random Forests
Extra-Trees
Feature Importance
Boosting
AdaBoost
Gradient Boosting
Stacking

The Curse of Dimensionality
Main Approaches for Dimensionality Reduction
Projection
Manifold Learning
PCA
Preserving the Variance
Principal Components
Projecting Down to d Dimensions
Using Scikit-Learn
Explained Variance Ratio
Choosing the Right Number of Dimensions
PCA for Compression
Randomized PCA
Incremental PCA
Kernel PCA
Selecting a Kernel and Tuning Hyperparameters
LLE
Other Dimensionality Reduction Techniques

Clustering
K-Means
Limits of K-Means
Using clustering for image segmentation
Using Clustering for Preprocessing
Using Clustering for Semi-Supervised Learning
DBSCAN
Other Clustering Algorithms
Gaussian Mixtures
Anomaly Detection using Gaussian Mixtures
Selecting the Number of Clusters
Bayesian Gaussian Mixture Models
Other Anomaly Detection and Novelty Detection Algorithms

From Biological to Artificial Neurons
Biological Neurons
Logical Computations with Neurons
The Perceptron
Multi-Layer Perceptron and Backpropagation
Regression MLPs
Classification MLPs
Implementing MLPs with Keras
Installing TensorFlow 2
Building an Image Classifier Using the Sequential API
Building a Regression MLP Using the Sequential API
Building Complex Models Using the Functional API
Building Dynamic Models Using the Subclassing API
Saving and Restoring a Model
Using Callbacks
Visualization Using TensorBoard
Fine-Tuning Neural Network Hyperparameters
Number of Hidden Layers
Number of Neurons per Hidden Layer
Learning Rate, Batch Size and Other Hyperparameters

Vanishing/Exploding Gradients Problems
Glorot and He Initialization
Nonsaturating Activation Functions
Batch Normalization
Gradient Clipping
Reusing Pretrained Layers
Transfer Learning With Keras
Unsupervised Pretraining
Pretraining on an Auxiliary Task
Faster Optimizers
Momentum Optimization
Nesterov Accelerated Gradient
AdaGrad
RMSProp
Adam and Nadam Optimization
Learning Rate Scheduling
Avoiding Overfitting Through Regularization
ℓ1 and ℓ2 Regularization
Dropout
Monte-Carlo (MC) Dropout
Max-Norm Regularization
Summary and Practical Guidelines

A Quick Tour of TensorFlow
Using TensorFlow like NumPy
Tensors and Operations
Tensors and NumPy
Type Conversions
Variables
Other Data Structures
Customizing Models and Training Algorithms
Custom Loss Functions
Saving and Loading Models That Contain Custom Components
Custom Activation Functions, Initializers, Regularizers, and Constraints
Custom Metrics
Custom Layers
Custom Models
Losses and Metrics Based on Model Internals
Computing Gradients Using Autodiff
Custom Training Loops
TensorFlow Functions and Graphs
Autograph and Tracing
TF Function Rules

The Data API
Chaining Transformations
Shuffling the Data
Preprocessing the Data
Putting Everything Together
Prefetching
Using the Dataset With tf.keras
The TFRecord Format
Compressed TFRecord Files
A Brief Introduction to Protocol Buffers
TensorFlow Protobufs
Loading and Parsing Examples
Handling Lists of Lists Using the SequenceExample Protobuf
The Features API
Categorical Features
Crossed Categorical Features
Encoding Categorical Features Using One-Hot Vectors
Encoding Categorical Features Using Embeddings
Using Feature Columns for Parsing
Using Feature Columns in Your Models
TF Transform
The TensorFlow Datasets (TFDS) Project

The Architecture of the Visual Cortex
Convolutional Layer
Filters
Stacking Multiple Feature Maps
TensorFlow Implementation
Memory Requirements
Pooling Layer
TensorFlow Implementation
CNN Architectures
LeNet-5
AlexNet
GoogLeNet
VGGNet
ResNet
Xception
SENet
Implementing a ResNet-34 CNN Using Keras
Using Pretrained Models From Keras
Pretrained Models for Transfer Learning
Classification and Localization
Object Detection
Fully Convolutional Networks (FCNs)
You Only Look Once (YOLO)
Semantic Segmentation

Predict Housing Price
Stock Price Prediction
Online Assignment Plagiarism Checker
Personality Prediction System via CV Analysis
Breast Cancer Detection
Undersea Sonar Bomb Detection
Handwriting Recognition
Face recognition
Chatbot
YouTube Comment Spam Detection
11. Face Filter

Secrets of Success
SUpercharged CVs
Technical & HR Interview Preperation
Speaking & giving Presentations on AI
Freelancing / Entrepreneurship

The birth of MEAN Stack
Pros and Cons
HTML & CSS
Introduction to MEAN
Components and Usage
JavaScript and ECMAScript.
Vanilla JavaScript Basics,
Variables, Scope, Conditions,
Loops, Arrays, Functions,
Objects, JSON.
Animation, Cursor control,
Media, Interactive Animated Game

ECMAScript 2015 or ES6,
Console, Strict Mode,
Global Vs Local,
Scope, Variable and Function Hoisting,
Let, immutable const,
Exponentiation operator,
Class, Objects,
Constructor, Static,
Super, Inheritance,
Functions, this keyword,
OOPs Usage.
For IN, Switch,
Parameterized functions & Default function parameters,
Rest Parameters,
Anonymous Function,
Lambda Expression,
Immediately Invoked Function Expressions (IIFEs),
Generator Functions,
iterator, Object() Constructor,
Cloning an Object,
Merging Objects,
Object De-structuring,
Array De-structuring,
Maps and Sets,
WeakMaps, The instanceof operator,
Callback, AsyncCallback,
Promises, Prototype,
Call, Apply, Bind etc.

Installation and setup,
Data Directory,
Running background service,
Client, Creating a new database,
creating a new user,
Creating a new Collection,
Inserting, Showing,
Inserting Multiple,
Pretty, Updating, Find,
Finding using ID, Removing a field,
UPSERT, Renaming a field,
Removing a Document,
Like and Regular Expression,
Sorting, Counting, Limit, Loop - Foreach etc.
backup and restore databases in MongoDB
MongoDB relations and joins.

Node JS,
VSCode Setup,
NodeJS Installation, Package.json,
Node Modules. NodeJS Hello World,
NodeJS Module, NodeJS Processing Query String,
File Reading, File Writing,
Append a File, Delete a File,
Rename a File, NodeJS POST Form data,
The Formidable Module, File Uploading.
JS with MYSQL, MySQL Connection, CRUD.
Node With MySQL using Promises,
MongoDB usage with JS etc.

Express, Express Overview,
Installation, Basic App,
Routing, Request & Response,
Body Parser, Processing GET and POST Data,
RESTFul APIs, POSTMan,
Dynamic Routing - Passing Parameters through routes,
Middleware Functions,
Middleware order of execution,
Templating Engine, PUG,
Push Values to Template,
Template IF, Render, Include,
Static Files in Express,
Multiple Static Directories,
Template Forms, EJS Template Engine,
Mongoose to work with Express and MongoDB,
Cookies on Express,
Sessions with Express,
Login Logout System etc.

Testing, Deployment,
Payment Integration and SEO,
Using good project development methodologies,
testing and debugging, integrating different payment techniques using gateways,
optimization for Google search and Different Devices/Browsers.
Coding Standards and Improving coding quality.
Code optimization for improved performance,
decreased latency, increased capacity and higher ROI etc

At the End of the course, our students will be assigned customized Live projects. They will get full project guidance as well as motivation to complete the same.
Industry standard projects will be allotted to ensure highest quality individual learning. Projects will be made live so they can be accessed from anywhere. They will be certified by us as MEAN Stack Developers etc.
We have a very good placement system in place, we will prepare you for interviews, give you special interview preparation materials, prepare your CV in a way that will give you very good interview calls etc.