Topic: SQL for Data Science ,Oracle , MySQL, R and Python [2020]

Section 1: Introduction
1. Why SQL for Data Science?
2. Course Outline
3. Database Concept
4. SQL Concept
5. Summary for Learning

Section 3: Database Installation - Oracle and MySQL
7. Oracle vs MySQL
8. Understand Company Schema
9. Installation of MySQL Database
10. Play with MySQL Workbench
11. Installation of Oracle Database (optional)
12. Understanding Select and Where Clause
13. Create Company DB ( MySQL)
14. Create Company DB in Oracle (optional)

Section 4: Oracle vs MySQL Resources
15. Data Types Oracle and MySQL
16. Built-in Functions

Section 5: Basics of SQL - Write your 1st SQL
17. Write your 1st SQL
18. IN,NOT IN,BETWEEN Clause
19. Null ,Not Null , Order by Clause

Section 6: Advance SQL
20. Advance SQL - Aggregators
21. Basic Aggregators
22. Group by Clause 1
23. Group by Clause 2
24. Having Clause

Section 7: Nested SQL and Joins
25. Introduction to Nested SQL
26. Nested SQL 1
27. Nested SQL 2
28. Introduction to Joins
29. Inner Join
30. Outer Join

Section 9: SQL in Python and Data Visualizations

32. Introduction to SQL with Python and R
33. Installation of Python Developer Environment -Anaconda & Jupyter
34. SQL Python-Upload Market Data
35. Write SQL in Python
36. Visualizations in Python- Seaborn and Pandas
37. MySQL Database Connection with Python

Section 10: SQL in R and Data Visualizations
38. Introduction to R
39. Installation of R
40. Upload CSV files in R and SQL Queries
41. Data Visualizations in R using ggplot
42. Connecting MySQL with R and DML
43. Connecting MySQL with R and DDL operations

Section 11: Import/Export Files into Database - Oracle and MySQL
44. Import CSV file in Oracle Database - 1
45. Import CSV file in Oracle Database - 2
46. Import CSV file in MySQL - 1

Section 12: Case Study - Market Data
47. DML and DDL commands for Market Data
48. Select Clauses for Market Data in MySQL
49. Export Files - MySQL
50. Select Clauses for Market Data in Oracle
51. Complex SQL - Assignment
52. Assignment Solutions-1
53. Assignment Solutions-2

Section 13: Bonus Lectures on Machine Learning
54. What is machine learning ?
55. Concept of Linear Regression : The 1st machine learning Technique

Link: https://youtu.be/ZYPeaXr4tS0

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Topic: Android Malware Analysis - From Zero to Hero

Section 1: Introduction
1. Introduction
2. Why Android

Section 2: Android Security Architecture
3. Android Platform
4. Android Security Architecture
5. Android Security Features
6. Google Security Features

Section 3: Mobile App Anatomy
7. Android Application Anatomy
8. APK File Structure
9. DEX File

Section 4: Mobile Malware Types
10. Mobile Malware Types

Section 5: Setup Your Lab
11. Attack Tools Explained
12. Setup Your Kali Lab

Section 6: Acquire Malicious Apps
13. Malware Distribution Mechanism
14. Acquire Malicious Mobile Apps
15. Create a Malicious Mobile App
16. Extract a Mobile App From a Phone

Section 7: Malware Analysis Types
17. Static vs Dynamic Malware Analysis

Section 8: Reverse Engineer a Mobile App
18. Unzip vs Decode
19. Decode and Decompile Android Apps

Section 9: Perform Static Malware Analysis
20. Keyword Search Techniques
21. Dangerous Permissions
22. Analysis of a Spyware APK
23. Analysis of a Stalkware APK
24. Analysis of a Trojan APK

Section 10: Perform Malware Injection
25. Inject Whatsapp Application with Malware
26. Inject a Legit APK and Analyze It

Section 11: Perform Dynamic Malware Analysis
27. Download and Setup an Emulator
28. Dynamic Analysis
29. SSL Interception

Link: https://youtu.be/2vop2MgrBgo

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Topic: Bash Shell scripting and automation

Section 1: Bash Shell script and Command line

1. Introduction To Bash Shell
2. Introduction to Shell Scripts
3. iterm, Terminal, hostname, uname
4. File System and Directory Hierarchy
5. Multi Tasking and Multi User
6. Shell and environment Path
7. Some common commands like ls,cd,man,exit
8. Some more common Linux commands
9. cp, mv, clear, inode
10. who, whoami,tty,which, locate,pwd
11. calendar, date, time
12. vi editor part 1
13. vi editor part2
14. chmod and user permissions
15. chown, chmod, getent, chgrp
16. Background Jobs and no hangup
17. Sort and Uniq Command
18. Top and ps command
19. Pipes and redirection Part 1
20. Pipes and redirection Part 2
21. Wild Cards
22. Find Part 1
23. Find Part 2
24. Find Part 3
25. Find Part 4
26. grep command and common usage part-1
27. grep Part 2
28. Grep part 3
29. Grep part4
30. Shell script's different component
31. Functions and command line processing
32. While Loop
33. Until loop
34. For loop and its various syntax
35. If conditional statement
36. Test Conditions for file type and strings
37. Read user input and processing
38. Case statement AKA switch
39. File Handling and processing
40. Exit Status of Shell
41. Random number and its use case
42. Arrays and Iterating over elements in different ways
43. Here Documents and Multi line comments, FTP scripts
44. Trap and signals part-1
45. Trap and signals part-2
46. Trap and signals part-3
47. Trap and signals part-4
48. Trap and signals part-5
49. Ubuntu Installation in virtual Machine
50. dd and od command for hex representation and using dd to copy file
51. df, du, lsof netstat
52. dmidecode, lcpu, lspci, lsusb and SMBIOS
53. Partition, format and mounting a fresh disk
54. nmap and port scanning
55. ssh, scp, sshpass
56. tcpdump and how to create pcap file for network packet capture.

Link: https://www.youtube.com/watch?v=XFDsVGETUPA

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Topic: Introduction to AI, Machine Learning and Data Science 2020

Course content

Section 1: Introduction
1. Introduction

Section 2: Lets get families with terminologies
2. What is Artificial Intelligence
3. What is Data Science and Machine Learning
4. What is Deep Learning
5. What is Natural Language Processing?
6. What is Computer Vision?
7. What is Supervised Learning?
8. What is Unsupervised Learning?

Section 3: Variety of business problem statements which Machine Learning can solve
9. Lets get started
10. APS data to reduce maintenance cost
11. Identifying legitimate high-end need of teachers/students for donation
12. Automatic Music Generation
13. Car steering angle prediction using on road images
14. Lets conclude with examples

Section 4: Miscellaneous
15. Why to use Python for Machine Learning

Link: https://youtu.be/sQmWqoC8oAU

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Topic: Computer Networks Security from Scratch to Advanced

Course content:

Section 1: Introduction to Computer Networks
1. Introduction
2. What is a Computer Network?
3. Computer Networks Topologies
4. Computer Networks Categories
5. Computer Networks Devices and Services
6. Computer Networks Transmission Media

Section 2: ISO/OSI Model (7 Layers)
7. Why ISO/OSI Model?
8. Application, Presentation, and Session Layers
9. Transport and Network Layers
10. Data Link and Physical Layers
11. ISO/OSI Model in Action

Section 3: TCP/IP Protocol Suite
12. Introduction to Computer Networks Protocols
13. IP Protocol
14. TCP and UDP Protocols
15. Application Protocols
16. TCP/IP Characteristics and Tools

Section 4: Wireless Networks
17. Wireless Networks Benefits
18. Wireless Networks Types
19. Wireless Networks Protocol (Wi-Fi)
20. Wireless Networks Devices
21. Wireless Networks Drawbacks

Section 5: Computer Networks Security
22. Security Goals
23. Securing the Network Design
24. TCP/IP Security and Tools
25. Port Scanning and Tools
26. Sniffing and Tools

Section 6: Firewalls and Honeypots
27. Why Using a Firewall?
28. Firewalls Rules
29. Firewalls Filtering
30. Honeypots
31. Bypassing Firewalls

Section 7: Intrusion Detection and Prevention Systems (IDS/IPS)
32. What is Intrusion Detection Systems (IDS)?
33. Network IDS (NIDS)
34. NIDS Challenges
35. Snort as NIDS
36. Intrusion Prevention Systems (IPS)

Section 8: Wireless Networks Security
37. Wired Equivalent Privacy WEP Attacking
38. WPA and AES Protocols
39. Wireless Security Misconceptions
40. Wireless Attacks and Mitigation
41. Secure Network Design with Wireless

Section 9: Physical Security and Incident Handling
42. Physical Security Objectives
43. Physical Threats and Mitigation
44. Defense in Depth (DiD)
45. What is an Incident?
46. Incident Handling

Section 10: Computer Networks Security Conclusion
47. Confidentiality, Integrity, and Availability (CIA)
48. Assets, Threats, and Vulnerabilities
49. Risks and Network Intrusion
50. Common Attacks
51. Security Recommendations

Link: https://youtu.be/9E-jDeGbKRE

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Topic: Google Hacks For Businesses Introduction to Google Tools

Course content

Section 1: Introduction
1. Course Overview

Section 2: Google Search Tools: Research
2. Suggested Search Phrases
3. 'AllInTitle:' Tag
4. Site Search
5. Google Cache Search
6. 'NoIndex' and 'NoFollow' Meta Tags
7. 'Related' Tag
8. 'Links' Tag
9. 'Define' Tag

Section 3: Google Alerts & Trends: Micro Niching
10. Google Alerts
11. Google Trends - Introduction
12. Google Trends - Part 2
13. Google Trends - Part 3
14. Google Trends - Part 4
15. Google Trends - Part 5

Section 4: Productivity Tools: Consistency Is Key
16. Google Timer
17. Google Calendar - Introduction
18. Google Calendar - Setup
19. Google Calendar - Promo Calendar
20. Google Calendar - Search & Sort
21. Google Keep

Section 5: Groups & Hangouts: Working With Clients
22. Google Groups: Introduction
23. Google Groups: Setup
24. Google Groups: Think Outside The Box!
25. Google Hangouts: Introduction
26. Google Hangouts - Demo Hangout
27. Google Hangouts - Important Features

Section 6: G-Suite: Google's Toolbox
28. Google Drive - Intro
29. Google Drive - PC Sync
30. Google Docs
31. Google Slides
32. Google Sheets
33. Google Forms
34. Google Voice
35. Google Photos - Intro
36. Google Photos - Assistant
37. Google Plus
38. Connect External Emails to Gmail

Section 7: 'Under-The-Radar' Business Tools: Boost Your Business
39. Google My Business
40. Google Newsstand Publisher
41. Global Market Finder

Section 8: Advertising: Introduction to Adsense & Adwords
42. Introduction to Adsense
43. Your First Ad Unit
44. Allow & Block Ads
45. Performance Reports
46. Ad Placements
47. Introduction to AdWords

Assignment 4: Building A Campaign Worksheet
48. Setup Your Campaign
49. AdWords Keyword Planner
50. Google AdWords Resources

link: https://youtu.be/97U6vQMQrzo

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Topic: Practical Database Course for Beginners

Course content
Section 1: Introduction to MySQL and Installation
1. Introduction to Course + What you will learn?
2. XAMPP Installation
3. MySQL Workbench Installation

Section 2: MySQL Basics
4. Data Types in MySQL - Part 1
5. Data Types in MySQL - Part 2
6. Creating Database & Table through PHPMyAdmin
7. Creating Database & Table through MySQL Workbench
8. Understanding Primary Key, Database Users & their Permissions
9. Importing & Exporting Databases
10. Introduction to MySQL Queries and SELECT Clause
11. Practical session on Insert + Update + Delete
Section 3: MySQL Advanced
12. Mastering Table Storage Engines
13. Mastering Table Joining: Part 1
14. Mastering Table Joining: Part 2
15. Working with Math and Strings: Part 1
16. Working with Math and Strings: Part 2
17. Working with Group By: Part 1
18. Working with Group By: Part 2
19. Mastering Sub-Queries: Part 1
20. Mastering Sub-Queries: Part 2
21. Transactions and sequence!

Section 4: MongoDB Introduction + Installation
22. Introduction to MogoDB course
23. MongoDB Installation

Section 5: MongoDB Basic Queries
24. Basic Queries: Part 1
25. Basic Queries: Part 2
Section 6: MongoDB Advanced Queries
26. Advanced Queries: Part 1
27. Advanced Queries: Part 2

Section 7: MongoDB Project withPHP
28. PHP + MongoDB Basic Project # Part 1
29. PHP + MongoDB Basic Project # Part 2

Section 8: MongoDB ADVANCED project with PHP
30. PHP + MongoDB Advanced Project #Part 1
31. PHP + MongoDB Advanced Project #Part 2
32. PHP + MongoDB Advanced Project #Part 3
33. PHP + MongoDB Advanced Project #Part 4
34. PHP + MongoDB Advanced Project #Part 5
35. PHP + MongoDB Advanced Project #Part 6
36. PHP + MongoDB Advanced Project #Part 7
37. PHP + MongoDB Advanced Project #Part 8
Section 9: Redis Introduction + Installation
38. Redis Intro
39. What is NoSQL?
40. What is Key-Value Store/Database ?
41. Redis Installation in Mac: From Source File
42. Redis installtion in Mac: Using Homebrew

Section 10: Redis Commands
43. Redis Connection Commands
44. Redis string relevant commands

Section 11: Redis Datatypes
45. Introduction to Redis Datatype
46. Redis Lists
47. Redis Hash
48. Redis Sets
49. Redis Sorted/Ordered Set

Link: https://youtu.be/V2jeVSDRZ7c

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Topic: Hacking Fundamentals: The Complete Nmap No-Nonsense Course

Course content

Section 1: Introduction
1. Introduction

Section 2: Nmap Installation and Setup
2. Install Kali Linux in VirtualBox
3. Install Nmap on MAC OS
4. Install Nmap on Microsfot Windows
5. Install Nmap on Linux (Debian\Ubuntu)

Section 3: Fundamentals
6. Scanning remote hosts and listing open ports
7. Identifying services of a remote hosts
8. Identifying live hosts in local networks
9. Scanning using specific port ranges
10. Specifying network interface for scanning
11. NSE scripts

Section 4: Gathering Information
12. Brute forcing DNS records
13. OS identification of remote hosts
14. UDP Services
15. Identifying protocols on remote hosts
16. Discovering and Identifying Firewalls
17. Identifying services with vulnerabilities
18. Using zombie hosts to spoof origin of ports scans

Section 5: Network Exploration
19. TCP SYN ping scans and host discovery
20. TCP ACK ping scans and host discovery
21. UDP ping scans and host discovery
22. ICMP ping scans and host discovery
23. IP protocol ping scans and host discovery
24. ARP ping scans and host discovery
25. Broadcast ping and host discovery
26. Traffic hiding with random data
3min

27. Information gathering using forced DNS resolution
28. Host exclusion from scanning
29. Broadcast scripts usage for information gathering

Section 6: Web Servers
30. Supported HTTP methods enumeration
31. HTTP proxy check
32. Discovering directories in web servers
33. User account enumeration
34. Detecting XST vulnerabilities
35. Detecting XSS vulnerabilities
36. Check if crawling utilities are allowed on the host

Section 7: Mail Servers
37. Open relay identification
38. SMTP passwords attacks
39. SMTP servers user enumeration
40. Detecting backdoor SMTP servers
41. IMAP servers
42. POP3 servers

Section 8: Large Networks
43. Scanning a range of IP addresses
44. Random target scanning
45. Speeding up time consuming scans
46. Setting nmap timing templates

Section 9: Using Nmap with Python
47. Environment Setup
48. Basic Scan Techniques

Link: https://youtu.be/I2Gt6HzyWDw

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Hello. I hope you all guys are safe and stay healthy.
You guys are bored.??
I have a solution.
Today I am gonna share some few list of course.

Content:
1. Topic: Ethical Hacking From Zero To Hero
Link: https://youtu.be/-49BW-94SB0

2. Topic: Top 5 Tools & Techniques for Ethical Hacking/Pentesting 2020
Link: https://youtu.be/IumsNiMeaSQ

3. Topic: Fundamentals of Machine Learning [Hindi] [Python]
link: https://youtu.be/LBrUaMhIcgg

4. Topic: The Complete Android Ethical Hacking Practical Course CAEHP
Link: https://youtu.be/wLu8kKcAvq4

5. Topic: The Complete SQL Course 2020: Become a MYSQL Master
Link: https://youtu.be/eheErxQnUpE

6. Topic: Data Science & Business Analytics Course
Link : https://youtu.be/ePSCXa-i0rA

7. Topic: IP Addressing - Zero to Hero
Link: https://youtu.be/1pFpqwqBoNg

8. Topic: SEO 2020 Training with SEO Expert for Beginners
Link: https://youtu.be/BKQNAkvptTM

9. Topic: SQL for Data Science ,Oracle , MySQL, R and Python [2020]
Link: https://youtu.be/ZYPeaXr4tS0

10. Topic: Android Malware Analysis - From Zero to Hero
Link: https://youtu.be/2vop2MgrBgo

11. Topic: Bash Shell scripting and automation
Link: https://youtu.be/XFDsVGETUPA

12. Topic: Introduction to AI, Machine Learning and Data Science 2020
Link: https://youtu.be/sQmWqoC8oAU

13. Topic: Computer Networks Security from Scratch to Advanced
Link: https://youtu.be/9E-jDeGbKRE

14. Topic: Google Hacks For Businesses Introduction to Google Tools
link: https://youtu.be/97U6vQMQrzo

15. Topic: Practical Database Course for Beginners
Link: https://youtu.be/V2jeVSDRZ7c

Direct YouTube Channel link: https://bit.ly/39YRQBK (Subscriber Goal: 1K)

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Topic: Mobile Security Reverse Engineer Android Apps From Scratch

Course content

Section 1: Course prerequisites
1. Course overview
2. Course prerequisites

Section 2: Intro to Android
3. Section 2
4. Intro to Android

Section 3: Course/Lab setup
5. Section 3
6. Android Studio
7. SDK Manager
8. Emulator
9. ADB
10. Apktool
11. JD-GUI/Enjarify
12. Bytecodeviewer
13. Androguard
14. Objection
15. Tamer
16. Recap

Section 4: Developing a basic Android App
17. Section 4
18. Android App Structure and components
19. Simple UI i
20. Simple UI ii
21. Simple UI iii
22. App Components: Activity i
23. App Components: Activity ii
24. App Components: Content Provider
25. App Components: Broadcast Receiver i
26. App Components: Broadcast Receiver ii
27. App Components: Service i
28. App Components: Service ii
29. Recap

Section 5: Analyzing Android Apps
30. Section 5
31. Static vs Dynamic vs Automated Analysis
32. ADB
33. Static Analysis: APKtool
34. Static Analysis: Bytecodeviewer
35. Static Analysis: Androguard
36. Dynamic Analysis: Objection
37. Automated Analysis: Malware Sandbox i
38. Automated Analysis: Malware Sandbox ii
39. Recap

Section 6: Case study: Analyzing real ransomware and developing a decr…
40. Section 6
41. Simplocker: Automated Analysis
42. Simplocker: Running on Emulator
43. Simplocker: Static Analysis
44. Simplocker: Decryption Tool
45. Recap
46. Thank you

Link: https://youtu.be/BNoB4ZKqwhs

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Topic: The Art of Doing Learn the Linux Command Line

Course content

Section 1: Virtual Box
1. Installing Virtual Box
2. Installing Kali Linux Virtual Machine

Section 2: Entering Terminal Commands: Getting to Know Your Computer
3. whoami, uname
4. ifconfig, ip, and ping
5. df, free, ps, top, kill

Section 3: Navigating the Terminal
6. Changing Directories
7. Important Directories and Looking at What They Contain
8. Editing Files Within a Directory

Section 4: Making and Manipulating Files
9. Making and Manipulating Files
10. Making and Manipulating Directories
11. Knowledge Check Challenge 1: Terminal Navigation and Manipulating Files

Section 5: Searching Through Files
12. Piping and Redirection
13. Searching With Grep
14. Awk: Grabbing Only What You Want
15. Knowledge Check Challenge 2: Piping, Grep, Awk, and Redirecting Output

Section 6: Changing File Permissions and Executing Files
16. What Are Permissions
17. Owner, Group, All Users
18. Changing Permissions and Executing Files
19. Adding to the Sudo Group
20. Knowledge Check Challenge 3: File Permissions, Users, and Groups

Section 7: Installing Software
21. The Advanced Packaging Tool APT
22. Updating Our System and Installing Software
23. Knowledge Check Challenge 4: Installing New Software and Learning How to use it.

Section 8: Compressing and Extracting Files
24. Compressing Files With Gzip
25. Creating Archives with Tar
26. Knowledge Check Challenge 5: Compressing and Extracting Files

Section 9: Basics of Bash Scripting
27. Writing Your First Script With Echo
28. An Introduction to Expansion
29. Parameter and Command Expansion
30. Arithmetic Expansion and Getting User Input
31. Tests and If Statements
32. Looping With For
33. Looping with While and Until
34. Knowledge Check Challenge 6: Bash Scripting

Link: https://youtu.be/T-UwrZPmahc

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Topic: The Basics of Cyber Security 2020

Course content

Section 1: Introduction to Cyber Security
1. Introduction
2. What is Cyber Security?
3. Information Assets

Section 2: Cyber Threats and Protection
4. Introduction
5. Personal Information and Protection
6. Top Cyber Threats Faced by Organizations
7. Most Important Risks and Cyber Threats

Section 3: Cyber Risk Management
8. Risk Management (Definition and Function)
9. Risk Management Methodology
10. Responding to Risk
11. Risk Reporting

Section 4: Cryptography
12. What is Cryptography
13. Encryption Elements and Types

Section 5: Business Continuity
14. What is Business Continuity?
15. Business Continuity Components
16. Business Continuity Planning

link: https://youtu.be/inFIS9G_Afo

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Topic: Build Undetectable Malware Using C Language Ethical Hacking

Course content

Section 1: Introduction & Welcome To Hacking With C!
1. Ethical Hacking Advanced - Learn How To build Undetectable Malware Using C
2. What Are We Going To Learn In This Course & What You Need To Know!
3. Our Malware In Action & Hacking Fully Secured And Updated Windows 10 Machine
4. How To Make An .EXE Transform Into Any Other File Type (.jpg, .pdf, .mp4 ... )

Section 2: Hiding Our Program & Defining Connection Points
5. Explaining Malware Structure & Including Needed Libraries
6. Hiding Our Program Console Window
7. Defining Connection Points To Our Backdoor
8. Note for next lecture ("goto instruction" in C programming)
9. Attempting Connection Every 10 Seconds With Our Target

Section 3: Building Shell Function & Executing Commands
10. Creating Our Shell Function
11. Executing Commands On Target Machine
12. Server Socket Initiation
13. Making Server Compatible With Our Backdoor
14. Testing Our Malware For Command Execution

Section 4: Switching Directories Inside Of A Program
15. Changing Our Program Directory

Section 5: Creating Persistance & Nesting Our Program In Windows Registry
16. Taking A Look At Windows Registry
17. Interacting With Registry In Order To Start Our Program Automaticly

Section 6: Adding Keylogger To Our Malware
18. Understanding Keylogger Code
19. Adding Keylogger Function To Our Backdoor

Section 7: Hacking Windows 10
20. Updating Our Server Code
21. Hacking Windows 10 Target With Our Program
22. "You Have Been Hacked"
23. Is It a Browser Or Backdoor ?

Link: https://youtu.be/6Dc8i1NQhCM

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Topic: Deep Web The complete Introduction to the hidden web

Course content

Section 1: Introduction
1. Introduction
2. What is the deep web
3. Difference between the deep web and the dark web
4. How the deep web could be used for legal and good purposes
5. Deep web/dark web myths

Section 2: Basic things to know
6. Introduction to TOR
7. TOR security
8. The concept of anonymity

Section 3: Let's begin our journey
9. Downloading TOR
10. list of safe Websites to visit
11. Introduction to TAILS

Section 4: Making purchases on the deep web and cryptocurrencies
12. Introduction to cryptocurrencies
13. How to buy cryptocurrencies
14. Is it safe to buy products on the deep web
15. Market places

Section 5: Dangers
16. The dangers of the dark net
17. Staying safe
18. Course summary

Link: https://youtu.be/aQMYsCmkTzQ

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Topic: Complete Machine Learning with R Studio - ML for 2020

Course content

Section 1: Welcome to the course
1. Introduction
2. Course resources: Notes and Datasets (Part 1)

Section 2: Setting up R Studio and R crash course
3. Installing R and R studio
4. Basics of R and R studio
5. Packages in R
6. Inputting data part 1: Inbuilt datasets of R
7. Inputting data part 2: Manual data entry
8. Inputting data part 3: Importing from CSV or Text files
9. Creating Barplots in R
10. Creating Histograms in R

Section 3: Basics of Statistics
11. Types of Data
12. Types of Statistics
13. Describing the data graphically
14. Measures of Centers
15. Measures of Dispersion

Section 4: Intorduction to Machine Learning
16. Introduction to Machine Learning
17. Building a Machine Learning Model

Section 5: Data Preprocessing for Regression Analysis
18. Gathering Business Knowledge
19. Data Exploration
20. The Data and the Data Dictionary
21. Importing the dataset into R
22. Univariate Analysis and EDD
23. EDD in R
24. Outlier Treatment
25. Outlier Treatment in R
26. Missing Value imputation
27. Missing Value imputation in R
28. Seasonality in Data
29. Bi-variate Analysis and Variable Transformation
30. Variable transformation in R
31. Non Usable Variables
32. Dummy variable creation: Handling qualitative data
33. Dummy variable creation in R
34. Correlation Matrix and cause-effect relationship
35. Correlation Matrix in R

Section 6: Linear Regression Model
36. The problem statement
37. Basic equations and Ordinary Least Squared (OLS) method
38. Assessing Accuracy of predicted coefficients
39. Assessing Model Accuracy - RSE and R squared
40. Simple Linear Regression in R
41. Multiple Linear Regression
42. The F - statistic
43. Interpreting result for categorical Variable
44. Multiple Linear Regression in R
45. Test-Train split
46. Bias Variance trade-off
47. Test-Train Split in R

Section 7: Regression models other than OLS
48. Linear models other than OLS
49. Subset Selection techniques
50. Subset selection in R
51. Shrinkage methods - Ridge Regression and The Lasso
52. Ridge regression and Lasso in R

Section 8: Classification Models: Data Preparation
53. The Data and the Data Dictionary
54. Course resources: Notes and Datasets
55. Importing the dataset into R
56. EDD in R
57. Outlier Treatment in R
58. Missing Value imputation in R
59. Variable transformation in R
60. Dummy variable creation in R

Section 9: The Three classification models
61. Three Classifiers and the problem statement
62. Why can't we use Linear Regression?

Section 10: Logistic Regression
63. Logistic Regression
64. Training a Simple Logistic model in R
65. Results of Simple Logistic Regression
66. Logistic with multiple predictors
67. Training multiple predictor Logistic model in R
68. Confusion Matrix
69. Evaluating Model performance
70. Predicting probabilities, assigning classes and making Confusion Matrix in R

Section 11: Linear Discriminant Analysis
71. Linear Discriminant Analysis
72. Linear Discriminant Analysis in R

Section 12: K-Nearest Neighbors
73. Test-Train Split
74. Test-Train Split in R
75. K-Nearest Neighbors classifier
76. K-Nearest Neighbors in R

Section 13: Comparing results from 3 models
77. Understanding the results of classification models
78. Summary of the three models

Section 14: Simple Decision Trees
79. Basics of Decision Trees
80. Understanding a Regression Tree
81. The stopping criteria for controlling tree growth
82. The Data set for this part
83. Course resources: Notes and Datasets
84. Importing the Data set into R
85. Splitting Data into Test and Train Set in R
86. Building a Regression Tree in R
87. Pruning a tree
88. Pruning a Tree in R

Section 15: Simple Classification Tree
89. Classification Trees
90. The Data set for Classification problem
91. Building a classification Tree in R
92. Advantages and Disadvantages of Decision Trees

Section 16: Ensemble technique 1 - Bagging
93. Bagging
94. Bagging in R

Section 17: Ensemble technique 2 - Random Forest
95. Random Forest technique
96. Random Forest in R

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Section 18: Ensemble technique 3 - GBM, AdaBoost and XGBoost
97. Boosting techniques
98. Gradient Boosting in R
99. AdaBoosting in R
100. XGBoosting in R

Section 19: Maximum Margin Classifier
101. Content flow
102. The Concept of a Hyperplane
103. Maximum Margin Classifier
104. Limitations of Maximum Margin Classifier

Section 20: Support Vector Classifier
105. Support Vector classifiers
106. Limitations of Support Vector Classifiers

Section 21: Support Vector Machines
107. Kernel Based Support Vector

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Topic: Practical Machine Learning by Example in Python

Course content

Section 1: Course Structure and Development Environment
1. Course Structure and Development Environment
2. Course Quick Tips
3. Introduction to Jupyter Notebook
4. Jupyter notebook: Text Cells
5. Jupyter notebook: Code Cells
6. Jupyter notebook: Math Markup and Magic Commands
7. Sharing Colab Notebooks
8. Artificial Intelligence, Machine Learning, and Deep Learning
9. What you learned in this section

Section 2: Python Quick Start
10. About this section
11. Basic Syntax
12. String formatting
13. Literal string interpolation
14. Type conversion
15. Flow control
16. Lists

Assignment 3: Dot product
17. Dictionaries
18. Defining functions
19. Classes
20. File I/O and Modules
21. Prompting for passwords
22. What you learned in this section

Section 3: Example: Logistic Regression
23. The problem
24. Machine Learning Development Process
25. Data analysis
26. The model
27. The forward function
28. Loss and cost functions
29. Gradient descent
30. Backpropagation
31. Model training
32. Making predictions
33. Test vs. train accuracy
34. Speeding up training
35. Improving the model
36. What you learned in this section

Section 4: Foundations: NumPy
37. What is NumPy and why it is needed?
38. Creating data with NumPy
39. Basic operations

Assignment 9: Experiment with NumPy
40. Introduction to Linear Regression
41. Linear Regression Example
42. More Complex Models
43. Statistics and linear algebra
44. Visualizing data
45. Images
46. Reshaping data
47. What you learned in this section

Section 5: Foundations: Tensorflow
48. About this section
49. Model example
50. Model layers
51. Activation functions
52. Training example
53. Loss functions
54. Optimizers
55. Prediction example
56. Saving and restoring models
57. The Three Body Problem
58. What you learned in this section

Section 6: Example: Image recognition
59. The problem
60. Data analysis
61. Model selection
62. Data preparation
63. CNN Model Layers
64. Model definition
65. Model training
66. Making predictions
67. Error analysis
68. Hyperparameter tuning
69. Hyperparameter tuning example
70. Common questions
71. What you learned in this section

Section 7: Foundations: Pandas
72. What is Pandas and why is it useful?
73. Loading and inspecting data example
74. Indexing and selecting data example

Assignment 20: Experiment with Pandas
75. Sorting and transforming data example
76. Aggregations example
77. Visualizing data
78. What you learned in this section

Section 8: Example: Recommendations
79. The problem
80. Data analysis
81. Model selection
82. Data preparation
83. Embedding layers
84. Model definition
85. Model training
86. Predictions
87. Making predictions
88. Error analysis
89. Common questions
90. What you learned in this section

Section 9: Example: Sentiment Analysis
91. The Problem
92. Data Analysis
93. Supervised Learning
94. Data Preparation
95. Model Definition
96. Model Training
97. Transfer Learning with BERT
98. Transfer Learning Example
99. Fine Tuning and Prediction
100. What you learned in this section

Section 10: Example: Fraud detection
101. The problem
102. Data analysis
103. Unsupervised learning
104. Data preparation
105. Model definition
106. Model training
107. Making predictions
108. Common questions
109. What you learned in this section

Section 11: Next steps
110. Next steps
111. Thank you

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