Build 3 Network Apps With Python And Machine Learning

Learn Python from SCRATCH! Build 3 Network Apps with Python, Pandas, Nmap and Machine Learning. Full Python Apps inside.

What you'll learn

You are going to learn Python from scratch, in an organized, easy-to-understand, hands-on fashion.

You are going to learn to read, write and manipulate dataframes and files using the Pandas library.

You will learn how to scan network devices and services using Nmap and the Nmap Scripting Engine (NSE).

You are going to build, test and download a Network Vulnerability detection application with Python.

You are going to build, test and download a Machine Learning app to analyze network vulnerabilities.

You are going to build, test and download a Machine Learning app to analyze network traffic captures.

Requirements

No prior Python knowledge is required! This course teaches you everything, from scratch.

You should already be familiar with networking concepts such as TCP, UDP, SSH, LAN, etc.

You are going to use only free software throughout the course: Python, VirtualBox, Linux.

All you need is a decent PC or laptop with at least 16GB RAM and an Internet connection.

Description

Python and Machine Learning course designed for Network Engineers10+ hours of video content​ and over 20 downloadable resourcesLearn Python from scratch and download additional PDF resourcesLearn how to use the Pandas library for data and file manipulationUse Nmap and the Nmap Scripting Engine for scanning the networkBuild and download a Network Vulnerability detection app with PythonBuild and download a Machine Learning app to analyze vulnerabilitiesBuild and download a Machine Learning app to analyze traffic capturesYou have my full support. I am answering questions in less than 24hrsUdemy Bestselling Instructor with thousands of amazing 5* reviewsOver 100,000+ satisfied students across several e-learning platformsCERTIFICATE of COMPLETION is also included at the end of the courseWhat others have to say about my Python courses?Before you read what I have to say, see what my students are saying about my courses:"What an incredible value and learning experience!" by Sean A."Excellent material. Kudos to a great instructor with a vast level of creativity." by Anthony W."This man is going on smoothly and perfectly, explaining in the most empirical way." by Kolapo A.What's this course all about?Python programming and Machine Learning course aimed at Network Professionals - Network Engineers, Network Admins and Security Engineers, having little or no experience in Python coding, network automation and ML, but a great desire to start learning Python from scratch and implement new technologies in their network environments.This 100% hands-on training takes you from "Hello World!" to building 3 network applications with Python, Pandas, Matplotlib, Nmap and Machine Learning in no time. You will learn Python, Pandas and Machine Learning concepts which are relevant to the networking industry, and then you're going to build 3 network applications.Here are some highlights from this course:Python - Variables. Data Types. Keywords Python - Strings. String OperationsPython - Numbers. Numeric OperationsPython - Booleans. Boolean OperationsPython - Lists. List OperationsPython - Sets. Set OperationsPython - Tuples. Tuple OperationsPython - Ranges. Range OperationsPython - Dictionaries. Dictionary OperationsPython - If-Elif-Else ConditionalsPython - For-Else LoopsPython - While-Else LoopsPython - List-Set-Dictionary ComprehensionsPython - Break. Continue. PassPython - Try. Except. Else. FinallyPython - Functions. Parameters. ArgumentsPython - Namespaces. ModulesPython - File OperationsPython - Regular ExpressionsPython - Special FunctionsDOWNLOAD - Regular Expressions NotebookDOWNLOAD - Python Primer NotebookPandas - Introduction. Basic OperationsPandas - Intermediate to AdvancedPandas - Handling CSV Files with PandasPandas - Data Visualization with MatplotlibDOWNLOAD - Pandas NotebookNMAP - Introduction to NMAP ScanningNMAP - The World of NMAP NSE ScriptsDOWNLOAD - NMAP NotebookUNSUPERVISED ML - Introduction to Machine LearningUNSUPERVISED ML - K-Means Clustering BasicsAPPLICATION 1 - Network Vulnerability Map with Python & NmapAPPLICATION 2 - Vulnerability Data Analysis with Machine LearningAPPLICATION 3 - Network Traffic Analysis with Machine LearningSUPERVISED ML - Decision Trees and Random ForestSUPERVISED ML - Extra APPLICATION and Examples"The instructor seems to touch all the basis in a logical and methodical way while providing examples and explanations. I can only conclude the instructor is a professional educator who spent considerable time structuring and organizing the course. The result is evident. THIS IS A GREAT WAY TO LEARN PYTHON!" by Larry L.Important information before you enroll!You are covered by a 30-days money back guarantee, full refund, no questions asked.Once enrolled, you have unlimited, 24/7, lifetime access to the course from any device.You will benefit from my full support regarding any question related to the course content.I strongly encourage you to boost your Network Engineering skills using the amazing power of Python and Machine Learning, and take your job and career one step further!Enroll now and I'll see you in the first lecture!

Overview

Section 1: SECTION 1: Getting Started

Lecture 1 What's This Course All About?

Lecture 2 Installing VirtualBox and Kali Linux

Lecture 3 Troubleshooting Kali Linux in VirtualBox

Lecture 4 Installing Python and Jupyter Notebook

Lecture 5 Best Way of Going Forward

Section 2: SECTION 2: Learning Python From Scratch

Lecture 6 Introduction to Jupyter Notebook

Lecture 7 Quick Note on the Concepts in this Section

Lecture 8 Variables. Data Types. Keywords

Lecture 9 Strings. String Operations

Lecture 10 Strings. Additional Tip 1

Lecture 11 Strings. Additional Tip 2

Lecture 12 Strings. Additional Tip 3

Lecture 13 Strings. Additional Tip 4

Lecture 14 Numbers. Numeric Operations

Lecture 15 Numbers. Additional Tip

Lecture 16 Booleans. Boolean Operations

Lecture 17 Lists. List Operations

Lecture 18 Sets. Set Operations

Lecture 19 Tuples. Tuple Operations

Lecture 20 Ranges. Range Operations

Lecture 21 Dictionaries. Dictionary Operations

Lecture 22 Dictionaries. Additional Tip 1

Lecture 23 Dictionaries. Additional Tip 2

Lecture 24 If-Elif-Else Conditionals

Lecture 25 For-Else Loops

Lecture 26 While-Else Loops

Lecture 27 List-Set-Dictionary Comprehensions

Lecture 28 Break. Continue. Pass

Lecture 29 Try. Except. Else. Finally

Lecture 30 Exceptions. Additional Tip 1

Lecture 31 Exceptions. Additional Tip 2

Lecture 32 Functions. Parameters. Arguments

Lecture 33 Functions. Additional Tip 1

Lecture 34 Functions. Additional Tip 2

Lecture 35 Namespaces. Modules

Lecture 36 File Operations

Lecture 37 Regular Expressions

Lecture 38 Regular Expressions. Additional Tip

Lecture 39 Additional Useful Python Concepts

Lecture 40 Special Functions: Map. Filter. Reduce. Lambda

Lecture 41 Special Functions: Zipping and Unzipping Lists

Lecture 42 DOWNLOAD: Regular Expressions Notebook

Lecture 43 DOWNLOAD: Python Primer Notebook

Lecture 44 PRACTICE: Multiple-Choice Quizzes

Lecture 45 PRACTICE: Coding Exercises

Section 3: SECTION 3: Basic-to-Intermediate Pandas

Lecture 46 Introduction to Pandas. Basic Operations

Lecture 47 Intermediate to Advanced Pandas Operations

Lecture 48 Handling CSV Files with Pandas

Lecture 49 SAMPLE: CSV File for Visualization

Lecture 50 Pandas and Data Visualization

Lecture 51 [EXTRA] Pandas Series

Lecture 52 DOWNLOAD: Pandas Notebook

Section 4: SECTION 4: Building a Network Vulnerability Map

Lecture 53 Application Development Plan

Lecture 54 Installing the Necessary Libraries

Lecture 55 Installing the Ubuntu VMs in VirtualBox

Lecture 56 Preparing the Network Hosts: Host 1

Lecture 57 Preparing the Network Hosts: Host 2

Lecture 58 Preparing the Network Hosts: Host 3

Lecture 59 VirtualBox Guest Additions for Ubuntu

Lecture 60 Re-Adding All Devices to the LAN

Lecture 61 Important Note on Network Scanning

Lecture 62 Introduction to NMAP Scanning

Lecture 63 The World of NMAP NSE Scripts

Lecture 64 DOWNLOAD: NMAP Notebook

Lecture 65 Defining the Initial Variables and Actions

Lecture 66 Parsing the Scan Results per Host

Lecture 67 Extracting the Number of Exploits Available

Lecture 68 Writing Code for Brute-Force Attacks

Lecture 69 SAMPLE: Username/Password File

Lecture 70 Organizing the Scan and Attack Data

Lecture 71 Plotting the Hosts and Data in the Final Graph

Lecture 72 Sending the Vulnerability Data via Email

Lecture 73 Recap of Full Application Code

Lecture 74 Testing the Application on the Network

Lecture 75 Testing the Application on an Extended Network

Lecture 76 Automating the Scan on a Daily Basis

Lecture 77 DOWNLOAD APPLICATION 1: Full Code

Section 5: SECTION 5: Unsupervised ML in Networking

Lecture 78 Application Development Plan

Lecture 79 Introduction to Machine Learning

Lecture 80 K-Means Clustering Basics

Lecture 81 Goals. Preparing the Data Set

Lecture 82 DOWNLOAD: Sample Data Set

Lecture 83 Analyzing the ML Python Code

Lecture 84 Note on the Core ML Functionality

Lecture 85 Testing the ML Model on the Data Set

Lecture 86 Adding Correlation Functionality

Lecture 87 Testing the Correlation Feature

Lecture 88 DOWNLOAD APPLICATION 2: Full Code

Lecture 89 Brute-Force Attacks in Traffic Captures

Lecture 90 Using Machine Learning on PCAP Files

Lecture 91 Analyzing the ML Python Code

Lecture 92 Note on the Core ML Functionality

Lecture 93 DOWNLOAD: Sample PCAP File

Lecture 94 DOWNLOAD APPLICATION 3: Full Code

Lecture 95 [EXTRA] Using the DBSCAN ML Algorithm

Lecture 96 [EXTRA] DOWNLOAD: DBSCAN Code

Lecture 97 [EXTRA] Using the Hierarchical Clustering ML Algorithm

Lecture 98 [EXTRA] DOWNLOAD: Hierarchical Clustering Code

Section 6: SECTION 6: Supervised ML in Networking

Lecture 99 Application Development Plan

Lecture 100 What are Decision Trees in ML

Lecture 101 The Random Forest Algorithm

Lecture 102 Generating Data for Training and Predictions

Lecture 103 DOWNLOAD: Code for Data Generation

Lecture 104 Analyzing the ML Python Code: Splitting & Training

Lecture 105 Summary of Splitting the Train-Test Data

Lecture 106 Analyzing the ML Python Code: Loading & Executing

Lecture 107 [EXTRA] Analyzing Feature Importance

Lecture 108 Testing the Application on the New Data

Lecture 109 DOWNLOAD: ML with Feature Importance Code

Lecture 110 Generating nmap -sV Traffic for Training & Predicting

Lecture 111 DOWNLOAD: Training Data CSV & Prediction PCAP

Lecture 112 Analyzing the ML Python Code: Extracting Data

Lecture 113 DOWNLOAD: Code for Converting PCAP Files to CSV

Lecture 114 Analyzing the ML Python Code: Training the Model

Lecture 115 Analyzing the ML Python Code: Making Predictions

Lecture 116 Testing the Application on a New Traffic Capture

Lecture 117 DOWNLOAD APPLICATION: Full Code

Lecture 118 Network Engineering: Supervised vs. Unsupervised ML

Lecture 119 Final Notes on ML Applications

Lecture 120 Closing Thoughts

Lecture 121 Bonus Lecture

Network Administrators, Network Engineers, Network Managers, Systems Engineers.,Network Quality Assurance Engineers, Network Analysts, Network Security Engineers.,Python Developers Who Want to Apply Their Knowledge in Network Automation.,Any other kind of IT or Networking geeks are welcome, as well.