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"Welcome to our comprehensive course on social media account hacking, where you'll delve deep into various techniques and methods utilized by hackers to gain unauthorized access. With a focus on practical learning, this course offers a hands-on approach, dedicating a minimal 10% to theory and maximizing practical exercises.

Throughout the curriculum, we've curated a selection of super topics, including Facebook hacking, Instagram hacking, Snapchat hacking, Freefire hacking, BGMI hacking, and more. With over 30 methods and techniques covered, you'll gain invaluable insights into the tools and strategies employed by cyber attackers.

Embark on your journey today and equip yourself with the knowledge and skills to understand, prevent, and combat social media account hacking. Join us as we empower you to stay ahead in the ever-evolving landscape of cybersecurity."

30 + SOCIAL MEDIA HACKING METHOD

Course Description:
The Ethical Hacking Fundamentals and Techniques course is designed to provide students with a comprehensive introduction to the world of ethical hacking, cybersecurity, and penetration testing. This hands-on course equips participants with the knowledge and skills necessary to identify vulnerabilities, assess security measures, and protect systems and networks from cyber threats.

You will get full life time access to course curriculum and personal Slack Channel for all your queries you can directly contact on our Slack Channel regarding any issues.

BOOTCAMP CONTENTS

1) Introduction
2) Setting Up Our Hacking Lab
3) Linux Operating System
4) Reconnaissance & Information Gathering
5) Scanning
6) Python Coding Project #1 - Portscanner
7) Vulnerability Analysis
8) Exploitation & Gaining Access
9) SMBGhost CVE 2020-0796 - Windows 10 Manual Exploitation
10) Gaining Access (Viruses, Trojans, Payloads ...)
11) Post Exploitation - Elevating Privileges, Extracting Data, Running Keyloggers
12) Coding Project #2 - Backdoor
13) Website Application Penetration Testing
14) Python Coding Project #3 - Login Bruteforce, Directory Discovery
15) Man In The Middle - MITM
16) Extra Wireless Access Point Cracking
17) Extra Gaining Access To An Android Device
18) Extra Introduction To Anonymity
19) Extra Hacking Active Directory
20) Learn Python Basics
21) Learn Python Intermediate
22) Learn Python 3 Error Handling
23) Learn Python 4 File IO

ETHICAL HACKING BOOTCAMP

Course Description:
The Data Science Fundamentals and Techniques course is designed to provide participants with a comprehensive introduction to the field of data science, covering the entire data analysis pipeline from data collection and preprocessing to modeling and visualization. Through hands-on exercises and real-world projects, participants will gain the skills needed to extract valuable insights and make informed decisions using data.

You will get full life time access to course curriculum and personal Slack Channel for all your queries you can directly contact on our Slack Channel regarding any issues.

BOOTCAMP CONTENTS

1) Part 1 Introduction to the Bootcamp
2) The Field of Data Science - The Various Data Science Disciplines
3) The Field of Data Science - Connecting the Data Science Disciplines
4) The Field of Data Science - The Benefits of Each Disciplines
5) The Field of Data Science - Popular Data Science Techniques
6) The Field of Data Science - Popular Data Science Tools
7) The Field of Data Science - Careers in Data Science
8) The Field of Data Science - Debunking Common Misconceptions
9) Part 2 Probability
10) Probability - Combinatorics
11) Probability - Bayesian Inference
12) Probability - Distribution
13) Probability - Probability in Other Fields
14) Part 3 - Statistics
15) Statistics - Descriptive Statistics
16) Statistics - Practical Example of Descriptive Statistics
17) Statistics - Inferential Statistics Fundamentals
18) Statistics - Inferential Statistics Confidence Intervals
19) Statistics - Practical Example of Inferential Statistics
20) Statistics - Hypothesis Testing
21) Statistics - Practical Example of Hypothesis Testing 
22) Part 4 Introduction to Python
23) Python - Variables and Data Types
24) Python - Basic Python Syntax
25) Python - Other Python Operators
26) Python  - Conditional Statements
27) Python - Python Function
28) Python - Sequences
29) Python - Iterations
30) Python - Advanced Python Tools
31) Part 5 Advanced Statistical Method in Python
32) Advanced  Statistical Methods - Linear Regression with StatsModels
33) Advanced Statistical Methods - Multiple Linear Regression with StatsModels
34) Advanced Statistical Methods -  Linear Regression with sklearn
35) Advanced Statistical Methods - Practical Example Linear Regression
36) Advanced Statistical Methods - Logistic Regression
37) Advance Statistical Methods - Cluster Analysis
38) Advanced Statistical Methods - K-means Clustering
39) Advanced  Statistical Methods - Other Types Of Clustering
40) 40 Part 6 Mathematics
41) Part 7 Deep Learning
42) Deep Learning - Introduction to Neural Networks
43) Deep Learning - How to build a Neural Network from Scratch with Numpy
44)  Deep Learning - Tensorflow 2.0 Introduction
45) Deep Learning - Digging Deeper into NN's Introducing Deep Neural Networks
46) Deep Learning - Overfitting
47) Deep Learning  - Initilization
48) Deep Learning - Digging into Gradient Descent and Learning Rate Schedules
49) Deep Learning - Preprocessing
50) Deep Learning - Classifying on the MNIST Dataset
51) Deep Learning - Business Case Example
52) Deep Learning - Conclusion
53) Appendix Deep Learning - TensorFlow 1 Introduction
54) Appendix Deep Learning - TensorFlow 1 Classifying on the MNIST Dataset
55) Appendix Deep Learning - TensorFlow 1 Business Case
56) Software Integration
57) Case Study - What's Next in the Bootcamp
58) Preprocessing the Absenteeism_data
59) Case Study - Applying Machine Learning to Create the absenteeism_module
60) Case Study - Loading the absenteeism_module
61) Case Study - Analyzing the Predicted Outputs in Tableau

Data Science Bootcamp

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