Matrices

Order of Metrix

Numericals on Matrix

Types of Matrix

Quality of Matrix

Operations of Matrices

Properties of Matrix

Transpose of Matrix

Symmetric & Skew Symmetric Matrix

Inverse & Elementary Transformation in Matrix

IIT JEE Questions

Introduction

Review on Precalculus

Limits and Continuity

Differentiation

Derivatives of Transcendental Functions

Limits - Indeterminate Forms

Applications: Rates of Change

Applications: Differential Equations

Applications: Mean Value Theorem

Applications: Linear Approximation

Applications: Curve Sketching

Applications: Optimization Problems

Conclusion

1- Overview of Computers and Programming

2- Overview of C

3 - Control Flow

4 - Loops

5- Functions

6 - Arrays

Course Preview & Syllabus

Basics of Engineering Drawing

Orthographic Projections

Projection Of Solids

Section Of Solids

Development Of Surfaces

Isometric Views

Geometrical Constructions

Bonus Setcion

Introduction

The Electricity

Electromotive Force - Voltage -Current

The Electrical Resistance And Impedance

The Electricity And Electricity Production Technologies- Part- 1

The Electricity And Electricity Production Technologies- Part- 2

The Electrical Loads

The Alternating Current

The Electromagnetism And The Electromagnetic Induction

The Voltages Classification

The Electrical Power And The Power Triangle

Power Factor - Power Calculation - Energy Calculation

Impedance Calculation - Power Factor Correction - Capacitors Bank- Part 1

Impedance Calculation - Power Factor Correction - Capacitors Bank- Part 2

Impedance Calculation - Power Factor Correction - Capacitors Bank- Part 3

The Voltage Drop

Load Balance - Phases Sequence

The Earthing - Part 1

The Earthing - Part 2

The Earthing - Part 3

The Electrical Insulation

The Harmonics

Electrical Cables - Part 1

Electrical Cables - Part 2

The Electrical Isolators

Circuit Breakers - Part 1

Circuit Breakers - Part 2

Solid State Circuit Breakers

The Earth Leakage Circuit Breaker

The Transformers - Part 1

The Transformers - Part 2

The Transformers - Part 3

The Protective Relays

Surge Protection Devices - Lighting Protection System

Nature and scope of Physics

Matter and its characteristics

Gravitation

Heat

Waves

Light

Differential Equations

Order & Degree of Differential Equations

Formation of Differential Equations

Solution of Differential Equations

First Order First Degree Differential Equation

Homogeneous Differential Equations

Linear Differential Equations

Miscellaneous Problems

Introduction To Number Systems

Conversions

Sign Bit Representation

Binary Arithmetic

Complement Subtractions

15'S & 16'S Complement Subtractions

BCD Code & Arithmetic

Excess-3 Arithmetic

Gray Code & ASCII Code

List of Gates

Alternate Gates & NAND, NOR Realization

Fundamentals & Theorem's Of Boolean Algebra

Boolean Equation Representation

Propagation Delay

Karnaugh Map (K-Map)

Combinational Circuits

Designing Of Adders

Designing Of Comparator

Code Converters

Parity Generators

Decoders

Multiplexers

Hamming Code

Basic Latch & Flip-Flop

Excitation Tables

Flip-Flop Conversions

Shift Registers

Counters

Mod Counter

Bipolar Logic Families

Unipolar Logic Families

Semi Conductor Memories

Digital To Analog Converters DACs

Analog To Digital Converters DACs

Introduction

Basic Concepts

Control Structures

Arrays and Strings

Pointers

Functions

Object Oriented Programming (OOPS)

Inheritance

Polymorphism

Exception Handling and File Handling

Templates and Generic Programming

STL

Course Overview

Introduction

Persuasive Communication

Business Negotiation Techniques

Effective Business Writing

writing business reports and mails

Making Business Presentations

Developing a Career in the Business Sector

Introduction

Logic: Discrete Math

Sets, Relations, Functions

Combinatorics

Graphs

DBMS Basics

Database Design With E-R Model

Formal Relational Query Languages

SQL Basics

Functional Dependencies and Normalization

Physical Data Organization

Query Processing and Optimization

Transaction Processing & Concurrency Control

Recovery and Reliability

System Architecture

Introduction to the course

Introduction to Python Programming

Variables, Comments, Type Casting, Strings, Taking Input from User

Python Operators, Data Types and Control Structures

Arrays, Command Line Arguments, Functions

Classes and objects, Inheritance and Overriding, Abstract Classes and Interfaces

Working with Date and Time and File Handling

Working with Exception Handling and Regular Expressions

Software Engineering

Software Engineering Basics

Software Development Life Cycle

Software Project Management

Software Requirements

Software Design

Analysis and Design Tools

Software Design Strategies

Software User Interface

Software Design Complexity

Software Implementation

Software Testing

Software Maintenance

CASE Tools

Computer Organization

Performance Measures

Instructions Set Architecture & Design

Computer Arithmetic

Processor Unit Design

Memory System Design

Input Output Design & Organization

Pipeline Design Technique

Reduced Instruction Set Computers (RISC)

Introduction

Definitions

Network fundamentals

Network models

IP addressing

Known Protocols

Networking

Practical Part

Introduction

Letter and Number Codes

Letter and Number Sequences

Understanding Word Meanings

Section 5: Constructing Words

Analogies

Calculations

Logical Skills

Test Yourself

Basics

Operators

Conditional Statements

Loops

Functions

Arrays

Strings

OOPS

Algorithm Analysis

Arrays and Techniques Based On Arrays

sorting

Hash Table

Stacks

Binary Search

Sliding Window

Linked Lists

Queue

Trees

Heaps

binary search tree

Dynamic Programming

Graphs

Greedy Algorithms

Backtracking

Introduction and Background

Process Management

CPU Scheduling

Introduction & Basics Of Algorithm

Divide And Conqure Concept And Applications

Sets And Their Related Concepts

Concept And Applications Of Greedy Method Approach

Introduction to Artificial Intelligence

Machine Learning Fundamentals

Deep learning

Natural Language Processing (NLP)

Computer Vision

Reinforcement Learning

Capstone Project

Course Overview - Preview

Probability - Essential Concepts for Statistics and Data Science

Random Variables and probability distributions

Binomial & Geometric distributions

Normal & Standard normal distributions

Introduction

Time and Work

Pipes and Cisterns

Time and Distance

Problems on Trains

Boats and Streams

Percentages, Theory of Sets

Profit and Loss

Mixture and Alligation

Permutations and Combinations

Finite Automata

The Theory of Automata

Formal Languages

Regular Sets & Regular Grammars

Context Free Languages

Push Down Automata (PDA)

LL(K) And LR(K) Grammars

Turing Machines

Introduction to Cyber Security Fundamentals

Malware Types

What is a Penetration Tester and Expert?

What are Hackers and Hacker Groups?

Information Security - Vulnerability and GDPR

Types of Cyber Attacks

Anonymous

Cyber Attack Methodology

Cyber Attacks and Security

Defense Mechanisms

Computer Graphics

Graphics Systems

Output Primitives

Curves

2D Transformation

3D Transformation and Projection

Hidden Lines and Surfaces

Graphics Procedures

Illumination and Shading

IoT INTRODUCTION

IoT ARCHITECTURE

IoT TECHNOLOGIES

IoT PROTOCOLS

IoT SECURITY & FEATURES

Compiler Design

Introduction to Compiling

Finite Automata and Formal Languages

Lexical Analyzer

Syntax Analysis

Parsing Techniques

Automatic Construction Of Efficient Parsers

Syntax Directed Translation

Storage Allocation

Intermediate Code Generation

Code Optimization

Code Generation

Introduction

Basics and Principles

Computing Concepts

Networking Concepts

Storage Concepts

Governance, Security, and Compliance Concepts

Next Steps

Introduction to the Course

Why Machine Learning

Process of Learning from Data

Machine Learning Methods

Data Preparation and Preprocessing

Machine Learning Models and Optimization

Building Machine Learning Model from Scratch

Overfitting, Underfitting and Generalization

Machine Learning Model Performance Metrics

Dimensionality Reduction

Deep Learning Overview

Hands-on Machine Learning Project Using Scikit-Learn

OPTIONAL Section- Mathematics Wrap-up

Introduction to Business and Data

Python Basics and Jupyter Notebooks

Operators and Conditionals

Loops and function

Object-Oriented Programming (OOP) and NumPy

Pandas Library and Data Manipulation

Working with Files and Data Importing

Data Cleaning and Preprocessing

Exploratory Data Analysis (EDA)

Advanced Topics

Capstone Project

Introduction to the Embedded Systems Engineer Diploma

Introduction to Embedded C

Multisim PCB Design and Fabrication for Everyone

C# Basics for Beginners: Learn C# Fundamentals by Coding

SOLIDWORKS Course: Turn 2D Sketches into 3D Models

Design 3D Printed Enclosure for Arduino Boards Fusion 360

Introduction to STM32 - 32-bit ARM-Based Microcontroller

Set Up an Electronics Home Lab Tools and Equipment

How to Solder Electronic Components Like A Professional

Introduction

Introduction(Regular Expressions)

Meta Characters(Regular Expressions)

Pattern Objects(Regular Expressions)

More Meta Characters(Regular Expressions)

String Modification(Regular Expressions)

Words and Tokens(Text Preprocessing)

Sentiment Classification(Text Preprocessing)

Language Independent Tokenization(Text Preprocessing)

Text Nomalization(Text Preprocessing)

String Matching and Spelling Correction(Text Preprocessing)

Language Modeling

Topic Modelling with Word and Document Representations

Word Embeddings LSI

Word Semantics

Word2vec(Optional)

Need of Deep Learning for NLP(NLP with Deep Learning DNN)

Introduction(NLP with Deep Learning DNN)

Training(NLP with Deep Learning DNN)

Hyper parameters(NLP with Deep Learning DNN)

Introduction(NLP with Deep Learning RNN)

Mini-project Language Modelling(NLP with Deep Learning RNN)

Mini-project Sentiment Classification(NLP with Deep Learning RNN)

RNN in PyTorch(NLP with Deep Learning RNN)

Advanced RNN models(NLP with Deep Learning RNN)

Neural Machine Translation

Introduction

Introduction to Full Stack Development

Introduction to ChatGPT Open AI

==== Fundamentals of HTML Cheat Sheet ====

Frontend Development – HTML-CSS

Frontend Development – Basic

Frontend Development – Intermediate

Frontend Development – Advanced

Frontend Scripting – JavaScript

Frontend Scripting – JavaScript – Basic

Frontend Scripting – JavaScript – Intermediate

Frontend Scripting – JavaScript - Advanced

Backend Development-Python

Backend Development – Python Basic

Backend Development- Python Intermediate

Backend Development-Python Advanced-OOPs

Databases and Data Management – MySQL

Databases and Data Management-MySQL-Basics

Databases and Data Management – MySQL-Intermediate

Databases and Data Management- MySQL-Advanced

Server-side Frameworks-Django-PostgreSQL

Server-side Frameworks-Django-PostgreSQL-Basic

Server-side Framework-Django-PostgreSQL Intermediate

Server-side Frameworks-Django-PostgreSQL-Advanced

Server-side REST API-Web Services

Server-side REST API-Basic

Server-side REST API-Intermediate

Server-side REST API-Advanced

Welcome

Introduction

Built in Data Types

Conditional Statements

Iterative Statements

Functions

Exception Handling

Object Oriented Programming

Functional Programming

Dart Collection

Welcome to the Flutter

Flutter First Application

Flutter Widgets

Building Interest Calculator Application

Building User Management Application

Introduction

The fundamentals of testing

Testing throughout the lifecycle

Static Testing

Test Design Techniques

Test Management

Tool support for testing

Course roundup

Introduction to virtual reality course

Unity Game Engine

Distributed virtual reality , VR applications

How to create Avatar using Unity and SDK

Introduction and Getting Started

Temperature Sensors

Mechanical PRESSURE TRANSDUCERS

ELECTRICAL PRESSURE TRANSDUCERS

SPEED TRANSDUCERS

FLOW METERS

Force Sensors

Position Sensors

Depth Gauges

STRAIN GAUGES

Bonus Lecture: Gifts, Coupons, and More..

Introduction to the Course

Setup the Environment

Introduction to ROS

Digital Twin

Control

Kinematics

Applications

Alexa Integration

Build the Robot

Conclusions

Introduction

Modelling Fundamentals

Java For AnyLogic

Modelling in AnyLogic

Resources and Support

Introduction To Design and its Essence

Overcoming Blank Canvas Fear

The Elements of Design → with UI and Web Design Examples

Principles of Design you must know

Case Study → Designing a Business Card by using Design Principles

Advanced Visual Design Topics

introduction

openCV - image operations

openCV - image segmentation

openCV - haar cascade classifiers

openCV - image analysis and transformation

openCV - motion and object tracking

openCV - facial landmark detection & face swaps

openCV projects

openCV - working with video

Open CV Codes Resource Files

deep learning in computer vision introduction

building CNNs in pytorch

building CNNs in tensorflow with keras

assessing model performance

improving models and advanced CNN design

visualizing what CNN 's learn

advanced convolutional neural networks

building and loading advanced CNN archiectures and rank-N Accuracy

using callbacks in keras and pytorch

pyTorch lightning

transfer learning and fine tuning

google deepstream and neural style transfer

autoencoders

generative adversarial networks(GANs)

siamese network

Face Recognition (Age, Gender, Emotion and Ethnicity) with Deep Learning

object detection

Modern Object Detectors - YOLO, EfficientDet, Detectron2

Gun Detector - Scaled-YoloV4

Mask Detector TFODAPI MobileNetV2_SSD

Sign Language Detector TFODAPI EfficentDet

Pothole Detector - TinyYOLOv4

Mushroom Detector Detectron2

Website Region Detector YOLOv4 Darknet

Drone Maritime Detector R-CNN

Chess Piece YOLOv3

Bloodcell Detector YOLOv5

Hard Hat Detector EfficentDet

Plant Doctor Detector YOLOv5

Deep Segmentation - U-Net, SegNet, DeeplabV3 and Mask R-CNN

Body Pose Estimation

tracking with deepSORT

Deep Fake

vision transformers - Vits

BiT BigTransfer Classifier Keras

Depth Estimation Project

Image Similarity using Metric Learning

Image Captioning with Keras

Video Classification usign CNN+RNN

Video Classification with Transformers

Point Cloud Classification PointNet

Point Cloud Segmentation Using PointNet

medical project - X-Ray Pneumonia Prediction

medical project - 3D CT Scan Classification

Low Light Image Enhancement MIRNet

Deploy your CV App using Flask RestFUL API & Web App

OCR Captcha Cracker

Deep Learning Codes Resource Files

Course Introduction and Table of Contents

Introduction to AI and Machine Learning

Introduction to Deep learning and Neural Networks

Setting up Computer - Installing Anaconda

Python Basics - Flow Control

Python Basics - List and Tuples

Python Basics - Dictionary and Functions

Numpy Basics

Matplotlib Basics

Pandas Basics

Basic Structure of Artificial Neuron and Neural Network

Activation Functions Introduction

Popular Types of Activation Functions

Popular Types of Loss Functions

Popular Optimizers

Popular Neural Network Types

King County House Sales Regression Model - Step 1 Fetch and Load Dataset

Step 2 and 3 EDA and Data Prepration

Step 4 Defining the Keras Model

Step 5 and 6 Compile and Fit Model

Step 7 Visualize Training and Metrics

Step 8 Prediction Using the Model

Heart Disease Binary Classification Model - Introduction

Step 1 - Fetch and Load Data

Step 2 and 3 - EDA and Data Preparation

Step 4 - Defining the model

Step 5 - Compile Fit and Plot the Model

Step 5 - Predicting Heart Disease using Model

Step 6 - Testing and Evaluating Heart Disease Model

Redwine Quality MultiClass Classification Model - Introduction

Step 2 - EDA and Data Visualization

Step 3 - Defining the Model

Step 4 - Compile Fit and Plot the Model

Step 5 - Predicting Wine Quality using Model

Serialize and Save Trained Model for Later Usage

Digital Image Basics

Basic Image Processing using Keras Functions

Keras Single Image Augmentation

Keras Directory Image Augmentation

Keras Data Frame Augmentation

CNN Basics

Stride Padding and Flattening Concepts of CNN

Flowers CNN Image Classification Model - Fetch Load and Prepare Data

Flowers Classification CNN - Create Test and Train Folders

Flowers Classification CNN - Defining the Model

Flowers Classification CNN - Training and Visualization

Flowers Classification CNN - Save Model for Later Use

Flowers Classification CNN - Load Saved Model and Predict

Flowers Classification CNN - Optimization Techniques - Introduction

Flowers Classification CNN - Dropout Regularization

Flowers Classification CNN - Padding and Filter Optimization

Flowers Classification CNN - Augmentation Optimization

Hyper Parameter Tuning

Transfer Learning using Pretrained Models - VGG Introduction

VGG16 and VGG19 prediction

ResNet50 Prediction

VGG16 Transfer Learning Training Flowers Dataset

VGG16 Transfer Learning Flower Prediction

VGG16 Transfer Learning using Google Colab GPU - Preparing and Uploading Dataset

VGG16 Transfer Learning using Google Colab GPU - Training and Prediction

VGG19 Transfer Learning using Google Colab GPU - Training and Prediction

ResNet50 Transfer Learning using Google Colab GPU - Training and Prediction

Popular Neural Network Type

Generative Adversarial Networks GAN Introduction

Simple Transpose Convolution using a grayscale image

Generator and Discriminator Mechanism Explained

A fully Connected Simple GAN using MNIST DataSet - Introduction

Fully Connected GAN - Loading the Dataset

Fully Connected GAN - Defining the Generator Function

Fully Connected GAN - Defining the Discriminator Function

Fully Connected GAN - Combining Generator and Discriminator Models

Fully Connected GAN - Compiling Discriminator and Combined GAN Models

Fully Connected GAN - Discriminator Training

Fully Connected GAN - Generator Training

Fully Connected GAN - Saving Log at Each Interval

Fully Connected GAN - Plot the log at intervals

Fully Connected GAN - Display Generated Images

Saving the Trained Generator for Later Use

Generating fake images using the saved GAN Model

Fully Connected GAN vs Deep Convoluted GAN

Deep Convolutional GAN - Loading the MNIST Hand Written Digits Dataset

Deep Convolutional GAN - Defining the Generator Function

Deep Convolutional GAN - Defining the Discriminator Function

Deep Convolutional GAN - Combining and Compiling the Model

Deep Convolutional GAN - Training the Model

Deep Convolutional GAN - Training the Model using Google Colab GPU

Deep Convolutional GAN - Loading the Fashion MNIST Dataset

Deep Convolutional GAN - Training the MNIST Fashion Model using Google Colab GPU

Deep Convolutional GAN - Loading the CIFAR-10 Dataset and Defining the Generator

Deep Convolutional GAN - Defining the Discriminator

Deep Convolutional GAN CIFAR 10 - Training the Model

Deep Convolutional GAN - Training the CIFAR10 Model using Google Colab GPU

Vanilla GAN vs Conditional GAN

Conditional GAN - Defining the Basic Generator Function

Conditional GAN - Label Embedding for Generator

Conditional GAN - Defining the Basic Discriminator Function

Conditional GAN - Label Embedding for Discriminator

Conditional GAN - Combining and Compiling the Model

Conditional GAN - Training the Model

Conditional GAN - Display Generated Images

Conditional GAN - Training the MNIST Model using Google Colab GPU

Conditional GAN - Training the Fashion MNIST Model using Google Colab GPU

Other Popular GANs - Further Refrence and Source Code Link

Download Complete Source Code

Course Overview

Building Your Forensics Lab Environment Using VirtualBox

Using Kali Forensic Mode and Autopsy

Digital Forensics Case Management

Open-source intelligence (OSINT)

Computer Forensics

Using Shodan to Search for Vulnerable devices

Stenography

Network forensics Using Wireshark

Practice What You earned