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
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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
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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
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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
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The Art of Doing Learn the Linux Command Line
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…
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…
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
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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
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The Basics of Cyber Security 2020
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…
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…
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 ?
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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 ?
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Build Undetectable Malware Using C Language Ethical Hacking
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…
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…
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
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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
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Deep Web The complete Introduction to the hidden web
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…
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…
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
👇👇👇👇👇👇👇
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
👇👇👇👇👇👇👇
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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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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Complete Machine Learning with R Studio - ML for 2020
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…
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…
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
Link: https://youtu.be/X8Gqi9IPo4g
Share to help us.
Channel Link: https://bit.ly/39YRQBK
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
Link: https://youtu.be/X8Gqi9IPo4g
Share to help us.
Channel Link: https://bit.ly/39YRQBK
YouTube
Practical Machine Learning by Example in Python
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…
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…
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Here is a guide from myself . I can create alot of VPS (RDPs) with this method. Hope you can have VPS to use
1. First of all, prepare an educational email account (You can get it easily). Login your education email account
2. Login or create github account
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4. After a few minutes , you will receive email. Follow the instruction : Choose Student purpose and reason to use for develop application
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The beginners guide to cyber security 2019
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Machine Learning and AI Support Vector Machines in Python
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Anatomy of a Cyber Attack Beginner Hacking with Metasploit
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Start using wireshark to hack like a pro
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Ethical hacking the most advanced level nmap course
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Ethical password hacking and protecting
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Html, JavaScript and bootstrap certification course
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Master in wifi ethical hacking
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The beginners guide to cyber security 2019
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The complete ethical hacking Masterclass beginning to expert
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Front End web development for beginners A practice guide.
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Machine Learning and AI Support Vector Machines in Python
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Anatomy of a Cyber Attack Beginner Hacking with Metasploit
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Topic : Learn operating System
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Introduction
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1.Introduction About Operating System:
Introduction
Important Tip
2.Introduction to Operating Systems
Introduction to Computer Systems
How hardware devices work together
Need for Operating Systems…
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Bug Bounty: Android Hacking
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Learn Network Hacking From Scratch (WiFi & Wired)
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How Hackers Create Undetectable Malware and Viruses
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Ethical Hacking using Linux & Termux From Scratch
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Bug Bounty: Android Hacking
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Learn Linux in 5 Days and Level Up Your Career
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Easy Adsense Method Using Whatsapp Free Traffic 2019
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Complete Cyber Security Course Go from zero to hero!
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Most Advanced Level Ethical Hacking Using Kali Linux
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Cyber Security and Information Security Fundamentals 2019
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A Hacker's Guide to Internet Safety and Cybersecurity
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Learn Network Hacking From Scratch (WiFi & Wired)
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How Hackers Create Undetectable Malware and Viruses
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Ethical Hacking using Linux & Termux From Scratch
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The Ultimate 2019 Cyber Security Awareness Training Course
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Front End web development for beginners A practice guide.
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Machine Learning and AI Support Vector Machines in Python
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Cyber Security and Information Security Fundamentals 2019
https://www.youtube.com/watch?v=ZqSdEMkMl7k
A Hacker's Guide to Internet Safety and Cybersecurity
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Learn Network Hacking From Scratch (WiFi & Wired)
https://www.youtube.com/watch?v=3u2rRM4LnsY
How Hackers Create Undetectable Malware and Viruses
https://www.youtube.com/watch?v=2jhbJyWn8W8
Ethical Hacking using Linux & Termux From Scratch
https://www.youtube.com/watch?v=NH9tEw8KiQc
REAL World Website Hacking and Penetration (101% Practical) 2019
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Share to help us.
Because Free Education for everyone.
YouTube
Full Stack Web Developer Bootcamp
Course content
Introduction to HTML and CSS
Introduction to Javascript(ES6)
Introduction to Node
Introduction to Python
Introduction to TypeScript
Introduction to TypeScript and Rect
Building Static Websites withGatsbyJS,React and GraphQL
Introduction to HTML and CSS
Introduction to Javascript(ES6)
Introduction to Node
Introduction to Python
Introduction to TypeScript
Introduction to TypeScript and Rect
Building Static Websites withGatsbyJS,React and GraphQL
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