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☄️ The first channel on Telegram that offers exciting questions, answers, and tests in data science, artificial intelligence, machine learning, and programming languages. ⚙️ We provide daily interview and exam questions ✅ https://t.me/DataScienceQhttps://t.me/DataScienceQ
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30 Days Roadmap to learn Ethical Hacking 👇👇 Day 1-3: Introduction to Ethical Hacking - Understand the basics of ethical hacking and its importance - Learn about different types of hackers and their motivations - Explore the legal and ethical considerations of ethical hacking Day 4-7: Networking Fundamentals - Learn about networking protocols, IP addresses, and subnets - Understand how data is transmitted over networks - Explore common network vulnerabilities and how to secure them Day 8-10: Information Gathering and Footprinting - Learn how to gather information about a target system or network - Explore techniques such as passive information gathering and footprinting - Understand the importance of reconnaissance in ethical hacking Day 11-14: Scanning and Enumeration - Learn how to scan for open ports and services on a target system - Understand the concept of enumeration and its role in ethical hacking - Explore tools such as Nmap for scanning and enumeration Day 15-17: Vulnerability Assessment and Exploitation - Learn how to identify and assess vulnerabilities in a target system - Understand common exploitation techniques and tools used in ethical hacking - Explore how to exploit vulnerabilities responsibly and ethically Day 18-21: Web Application Security - Learn about common web application vulnerabilities (e.g., SQL injection, XSS) - Understand how to secure web applications against attacks - Explore tools such as Burp Suite for web application testing Day 22-24: Wireless Network Security - Learn about common wireless network vulnerabilities and attacks - Understand how to secure wireless networks against intruders - Explore tools such as Aircrack-ng for wireless network penetration testing Day 25-27: Social Engineering and Physical Security - Learn about social engineering techniques used in ethical hacking - Understand the importance of physical security in cybersecurity - Explore ways to protect against social engineering attacks Day 28-30: Penetration Testing and Reporting - Learn how to conduct penetration tests on systems and networks - Understand the methodology of penetration testing (e.g., reconnaissance, scanning, exploitation, reporting) - Practice conducting penetration tests on virtual environments and create detailed reports on findings Remember to practice your skills in a controlled environment and always seek permission before performing any ethical hacking activities. Additionally, consider obtaining relevant certifications such as Certified Ethical Hacker (CEH) to validate your skills in ethical hacking. Some good resources to learn Ethical Hacking 1. Tutorials & Courses    - Informarion Security Free Course    - Ethical Hacking Bootcamp    - Network Hacking Course 2. Telegram Channels    - Cyber Security and Ethical Hacking    - Ethical Hacking Books 3. Books    - Ultimate Linux Free Book    - Python for Ethical Hacking 4. Ethical Hacking Forums Join @free4unow_backup for more free resources ENJOY LEARNING 👨‍💻🔒
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30-day roadmap to learn HTML, CSS, and JavaScript: Day 1-5: HTML Basics - Day 1-2: Introduction to HTML, tags, elements, and structure - Day 3-4: Working with text, links, images, lists, and tables - Day 5: Forms and input elements Day 6-15: CSS Fundamentals - Day 6-8: Introduction to CSS, selectors, properties, and values - Day 9-11: Box model, margins, padding, borders, and positioning - Day 12-15: CSS layout techniques, responsive design, and media queries Day 16-25: JavaScript Essentials - Day 16-18: Introduction to JavaScript, variables, data types, operators - Day 19-21: Functions, control flow (if statements, loops), and arrays - Day 22-25: DOM manipulation, events, and forms validation Day 26-30: Project-Based Learning - Day 26-27: Build a simple website using HTML and CSS - Day 28-29: Enhance the website with interactivity using JavaScript - Day 30: Finalize your project, test it thoroughly, and showcase it to others Throughout the 30 days: - Practice regularly by working on small projects and challenges - Review your progress and reinforce your learning by revisiting key concepts - Seek help from online resources, forums, and communities when you encounter difficulties - Stay motivated and track your progress to see how far you've come Here are some free resources to help you in the journey 👇 Into to HTML & CSS Learn JavaScript Learn HTML Learn CSS HTML Book Full Stack Web Development Course Object Oriented Javascript Javascript Courses CSS Roadmap Please give us credits while sharing: -> https://t.me/free4unow_backup ENJOY LEARNING 👍👍
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If I were to start Computer Science in 2023, - Harvard - Stanford - MIT - IBM - Telegram - Microsoft - Google ❯ CS50 from Harvard http://cs50.harvard.edu/x/2023/certificate/ ❯ C/C++ http://ocw.mit.edu/courses/6-s096-effective-programming-in-c-and-c-january-iap-2014/ ❯ Python http://cs50.harvard.edu/python/2022/ https://t.me/dsabooks ❯ SQL http://online.stanford.edu/courses/soe-ydatabases0005-databases-relational-databases-and-sql https://t.me/sqlanalyst ❯ DSA http://techdevguide.withgoogle.com/paths/data-structures-and-algorithms/ https://t.me/crackingthecodinginterview/290 ❯ Java http://learn.microsoft.com/shows/java-for-beginners/ https://t.me/programming_guide/573 ❯ JavaScript http://learn.microsoft.com/training/paths/web-development-101/ https://t.me/javascript_courses ❯ TypeScript http://learn.microsoft.com/training/paths/build-javascript-applications-typescript/ ❯ C# http://learn.microsoft.com/users/dotnet/collections/yz26f8y64n7k07 ❯ Mathematics (incl. Statistics) ocw.mit.edu/search/?d=Mathematics&s=department_course_numbers.sort_coursenum ❯ Data Science cognitiveclass.ai/courses/data-science-101 https://t.me/datasciencefun/1141 ❯ Machine Learning http://developers.google.com/machine-learning/crash-course ❯ Deep Learning introtodeeplearning.com t.me/machinelearning_deeplearning/ ❯ Full Stack Web (HTML/CSS) pll.harvard.edu/course/cs50s-web-programming-python-and-javascript/2023-05 t.me/webdevcoursefree/594 ❯ OS, Networking ocw.mit.edu/courses/6-033-computer-system-engineering-spring-2018/ ❯ Compiler Design online.stanford.edu/courses/soe-ycscs1-compilers Please give us credits while sharing: -> https://t.me/free4unow_backup ENJOY LEARNING 👍👍
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Complete Roadmap to become a data scientist in 5 months Free Resources to learn Data Science: https://t.me/datasciencefun Week 1-2: Fundamentals - Day 1-3: Introduction to Data Science, its applications, and roles. - Day 4-7: Brush up on Python programming. - Day 8-10: Learn basic statistics and probability. Week 3-4: Data Manipulation and Visualization - Day 11-15: Pandas for data manipulation. - Day 16-20: Data visualization with Matplotlib and Seaborn. Week 5-6: Machine Learning Foundations - Day 21-25: Introduction to scikit-learn. - Day 26-30: Linear regression and logistic regression. Work on Data Science Projects: https://t.me/pythonspecialist/29 Week 7-8: Advanced Machine Learning - Day 31-35: Decision trees and random forests. - Day 36-40: Clustering (K-Means, DBSCAN) and dimensionality reduction. Week 9-10: Deep Learning - Day 41-45: Basics of Neural Networks and TensorFlow/Keras. - Day 46-50: Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs). Week 11-12: Data Engineering - Day 51-55: Learn about SQL and databases. - Day 56-60: Data preprocessing and cleaning. Week 13-14: Model Evaluation and Optimization - Day 61-65: Cross-validation, hyperparameter tuning. - Day 66-70: Evaluation metrics (accuracy, precision, recall, F1-score). Week 15-16: Big Data and Tools - Day 71-75: Introduction to big data technologies (Hadoop, Spark). - Day 76-80: Basics of cloud computing (AWS, GCP, Azure). Week 17-18: Deployment and Production - Day 81-85: Model deployment with Flask or FastAPI. - Day 86-90: Containerization with Docker, cloud deployment (AWS, Heroku). Week 19-20: Specialization - Day 91-95: NLP or Computer Vision, based on your interests. Week 21-22: Projects and Portfolios - Day 96-100: Work on personal data science projects. Week 23-24: Soft Skills and Networking - Day 101-105: Improve communication and presentation skills. - Day 106-110: Attend online data science meetups or forums. Week 25-26: Interview Preparation - Day 111-115: Practice coding interviews on platforms like LeetCode. - Day 116-120: Review your projects and be ready to discuss them. Week 27-28: Apply for Jobs - Day 121-125: Start applying for entry-level data scientist positions. Week 29-30: Interviews - Day 126-130: Attend interviews, practice whiteboard problems. Week 31-32: Continuous Learning - Day 131-135: Stay updated with the latest trends in data science. Week 33-34: Accepting Offers - Day 136-140: Evaluate job offers and negotiate if necessary. Week 35-36: Settling In - Day 141-150: Start your new data science job, adapt to the team, and continue learning on the job. Best Resources to learn Data Science Intro to Data Analytics by Udacity Machine Learning course by Google Machine Learning with Python Data Science Interview Questions Data Science Project ideas Data Science: Linear Regression Course by Harvard Machine Learning Interview Questions Free Datasets for Projects Please give us credits while sharing: -> https://t.me/free4unow_backup ENJOY LEARNING 👍👍
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Artificial Intelligence && Deep Learning Channel for who have a passion for - * Artificial Intelligence * Machine Learning * Deep Learning * Data Science * Computer vision * Image Processing * Research Papers https://t.me/DeepLearning_ai
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Complete Roadmap to learn Machine Learning and Artificial Intelligence 👇👇 Week 1-2: Introduction to Machine Learning - Learn the basics of Python programming language (if you are not already familiar with it) - Understand the fundamentals of Machine Learning concepts such as supervised learning, unsupervised learning, and reinforcement learning - Study linear algebra and calculus basics - Complete online courses like Andrew Ng's Machine Learning course on Coursera Week 3-4: Deep Learning Fundamentals - Dive into neural networks and deep learning - Learn about different types of neural networks like Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) - Implement deep learning models using frameworks like TensorFlow or PyTorch - Complete online courses like Deep Learning Specialization on Coursera Week 5-6: Natural Language Processing (NLP) and Computer Vision - Explore NLP techniques such as tokenization, word embeddings, and sentiment analysis - Dive into computer vision concepts like image classification, object detection, and image segmentation - Work on projects involving NLP and Computer Vision applications Week 7-8: Reinforcement Learning and AI Applications - Learn about Reinforcement Learning algorithms like Q-learning and Deep Q Networks - Explore AI applications in fields like healthcare, finance, and autonomous vehicles - Work on a final project that combines different aspects of Machine Learning and AI Additional Tips: - Practice coding regularly to strengthen your programming skills - Join online communities like Kaggle or GitHub to collaborate with other learners - Read research papers and articles to stay updated on the latest advancements in the field Pro Tip: Roadmap won't help unless you start working on it consistently. Start working on projects as early as possible. 2 months are good as a starting point to get grasp the basics of Generative AI but mastering it is very difficult as AI keeps evolving every day. Best Resources to learn Generative AI 👇👇 Learn Python for Free Prompt Engineering Course Prompt Engineering Guide Data Science Course Google Cloud Generative AI Path Unlock the power of Generative AI Models Machine Learning with Python Free Course Machine Learning Free Book Deep Learning Nanodegree Program with Real-world Projects AI, Machine Learning and Deep Learning Join @free4unow_backup for more free courses ENJOY LEARNING👍👍
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