Free Udemy Course – 16 April 2021

Today’s Free Udemy Courses are; Advance Bug Bounty Hunting & Penetration Testing, Machine Learning & Deep Learning in Python & R, Python And Flask Framework Complete Course For Beginners and Adobe Premiere Pro CC Essential Video Editing Zero To Hero

Coupons are limited.  So It may run out early

Free Udemy Course – 16 April 2021

Daily Free Udemy Course

Advance Bug Bounty Hunting & Penetration Testing Course 2021

Learn Advance skills for finding bugs in websites, penetration testing on Windows and Linux machine. Setting up free Labs on Amazon EC2 (Elastic Compute Cloud) Instance. At the end of this course you will get links to download tools which we have used while making this course. You will learn below skills from this course.

  • Setup and Install Kali Linux VM on VMWare Workstation.
  • Setup your first Amazon EC2 Instance (Elastic Compute Cloud).
  • Basic Linux Networking, Files & Folders and Extra Commands.
  • Learn to Setup and Use Burpsuite.
  • Hunt Host Header Attack Bugs.
  • Create Custom Wordlists, Bruteforce Username and Password, Bypass Anti CSRF Protection.
  • Automation using burpsuite to find Sensitive/Critical Files.
  • Use Google Dork to find Sensitive Files.
  • Find your first XSS Bug (Cross Site Scripting) both manual and automation methods.
  • Exploiting XSS (Cross Site Scripting) using Beef Framework and Injecting Malicious Commands.
  • Basic and Advance SQL Injection Attacks.
  • Command Injection Attacks.
  • Finding File Upload Vulnerabilities.
  • Local File Inclusion (LFI) and Remote File Inclusion (RFI) Vulnerabilities.
  • Detailed Guide to Find Bug Bounty Programs and How to Submit your first Bug.
  • Recent Proof of Concept (POC) videos of live Websites.
  • Introduction to HacktheBox and Steps to Register your account on HacktheBox.
  • Penetration Testing: Capturing User & Root flag on HacktheBox for both Windows and Linux Machines.
  • Download link for Free Tools which are used in this Course.

Disclaimer : All video’s and tutorials are for informational and educational purposes only. We believe that ethical hacking, information security and cyber security should be familiar subjects to anyone using digital information and computers.

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Machine Learning & Deep Learning in Python & R

Download Practice files, take Quizzes, and complete Assignments

With each lecture, there are class notes attached for you to follow along. You can also take quizzes to check your understanding of concepts. Each section contains a practice assignment for you to practically implement your learning.

Table of Contents

  • Section 1 – Python basic
  • This section gets you started with Python.
  • This section will help you set up the python and Jupyter environment on your system and it’ll teach you how to perform some basic operations in Python. We will understand the importance of different libraries such as Numpy, Pandas & Seaborn.
  • Section 2 – R basic
  • This section will help you set up the R and R studio on your system and it’ll teach you how to perform some basic operations in R.
  • Section 3 – Basics of Statistics
  • This section is divided into five different lectures starting from types of data then types of statistics then graphical representations to describe the data and then a lecture on measures of center like mean median and mode and lastly measures of dispersion like range and standard deviation
  • Section 4 – Introduction to Machine Learning
  • In this section we will learn – What does Machine Learning mean. What are the meanings or different terms associated with machine learning? You will see some examples so that you understand what machine learning actually is. It also contains steps involved in building a machine learning model, not just linear models, any machine learning model.
  • Section 5 – Data Preprocessing
  • In this section you will learn what actions you need to take step by step to get the data and then prepare it for the analysis these steps are very important. We start with understanding the importance of business knowledge then we will see how to do data exploration. We learn how to do uni-variate analysis and bivariate analysis then we cover topics like outlier treatment, missing value imputation, variable transformation and correlation.
  • Section 6 – Regression Model
  • This section starts with simple linear regression and then covers multiple linear regression.
  • We have covered the basic theory behind each concept without getting too mathematical about it so that you understand where the concept is coming from and how it is important. But even if you don’t understand it,  it will be okay as long as you learn how to run and interpret the result as taught in the practical lectures.
  • We also look at how to quantify models accuracy, what is the meaning of F statistic, how categorical variables in the independent variables dataset are interpreted in the results, what are other variations to the ordinary least squared method and how do we finally interpret the result to find out the answer to a business problem.
  • Section 7 – Classification Models
  • This section starts with Logistic regression and then covers Linear Discriminant Analysis and K-Nearest Neighbors.
  • We have covered the basic theory behind each concept without getting too mathematical about it so that you
  • understand where the concept is coming from and how it is important. But even if you don’t understand
  • it,  it will be okay as long as you learn how to run and interpret the result as taught in the practical lectures.
  • We also look at how to quantify models performance using confusion matrix, how categorical variables in the independent variables dataset are interpreted in the results, test-train split and how do we finally interpret the result to find out the answer to a business problem.
  • Section 8 – Decision trees
  • In this section, we will start with the basic theory of decision tree then we will create and plot a simple Regression decision tree. Then we will expand our knowledge of regression Decision tree to classification trees, we will also learn how to create a classification tree in Python and R
  • Section 9 – Ensemble technique
    In this section, we will start our discussion about advanced ensemble techniques for Decision trees. Ensembles techniques are used to improve the stability and accuracy of machine learning algorithms. We will discuss Random Forest, Bagging, Gradient Boosting, AdaBoost and XGBoost.
  • Section 10 – Support Vector Machines
    SVM’s are unique models and stand out in terms of their conceptIn this section, we will discussion about support vector classifiers and support vector machines.
  • Section 11 – ANN Theoretical Concepts
  • This part will give you a solid understanding of concepts involved in Neural Networks.
  • In this section you will learn about the single cells or Perceptrons and how Perceptrons are stacked to create a network architecture. Once architecture is set, we understand the Gradient descent algorithm to find the minima of a function and learn how this is used to optimize our network model.
  • Section 12 – Creating ANN model in Python and R
  • In this part you will learn how to create ANN models in Python and R.
  • We will start this section by creating an ANN model using Sequential API to solve a classification problem. We learn how to define network architecture, configure the model and train the model. Then we evaluate the performance of our trained model and use it to predict on new data. Lastly we learn how to save and restore models.
  • We also understand the importance of libraries such as Keras and TensorFlow in this part.
  • Section 13 – CNN Theoretical Concepts
  • In this part you will learn about convolutional and pooling layers which are the building blocks of CNN models.
  • In this section, we will start with the basic theory of convolutional layer, stride, filters and feature maps. We also explain how gray-scale images are different from colored images. Lastly we discuss pooling layer which bring computational efficiency in our model.
  • Section 14 – Creating CNN model in Python and R
    In this part you will learn how to create CNN models in Python and R.
  • We will take the same problem of recognizing fashion objects and apply CNN model to it. We will compare the performance of our CNN model with our ANN model and notice that the accuracy increases by 9-10% when we use CNN. However, this is not the end of it. We can further improve accuracy by using certain techniques which we explore in the next part.
  • Section 15 – End-to-End Image Recognition project in Python and R
    In this section we build a complete image recognition project on colored images.
  • We take a Kaggle image recognition competition and build CNN model to solve it. With a simple model we achieve nearly 70% accuracy on test set. Then we learn concepts like Data Augmentation and Transfer Learning which help us improve accuracy level from 70% to nearly 97% (as good as the winners of that competition).
  • Section 16 – Pre-processing Time Series Data
  • In this section, you will learn how to visualize time series, perform feature engineering, do re-sampling of data, and various other tools to analyze and prepare the data for models
  • Section 17 – Time Series Forecasting

In this section, you will learn common time series models such as Auto-regression (AR), Moving Average (MA), ARMA, ARIMA, SARIMA and SARIMAX.

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Python And Flask Framework Complete Course For Beginners

Created thorough, extensive, but easy to follow content which you’ll easily understand and absorb.

The course starts with the basics, including Python fundamentals, programming, and user interaction.

The curriculum is going to be very hands-on as we walk you from start to finish becoming a professional Python developer. We will start from the very beginning by teaching you Python basics and programming fundamentals, and then going into advanced topics and different career fields in Python so you can get real-life practice and be ready for the real world.

The topics covered in this course are:

  • Array implementation
  • File methods
  • Keywords and Identifiers
  • Python Tuples
  • Python Basics
  • Python Fundamentals
  • Data Structures
  • Object-Oriented Programming with Python
  • Functional Programming with Python
  • Lambdas
  • Decorators
  • Generators
  • Testing in Python
  • Debugging
  • Error Handling
  • Regular Expressions
  • Comprehensions
  • Modules

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Adobe Premiere Pro CC Essential Video Editing Zero To Hero

This is a beginner level class so so together me you we will learn Adobe Premiere pro from basic to advance. This is a project base class so you will be able to apply your learned skill in real time class project. You will learn from this class about every basic lesson of video editing in Adobe Premiere Pro CC:

You’ll learn:

  • Setting Your Project and Import Video Clips
  • Multiple Video Placement
  • Color Adjustment
  • Transitions
  • Noise Reduction
  • Audio Levelling
  • Add Text
  • Unlink And Nest
  • Speed Of Video
  • End Credit
  • Text Animation
  • Remove Green Screen
  • Cinematic Effect
  • Video Blur
  • Video Inside Text
  • Multiple Video Same Time

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