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🔰 Artificial Intelligence & ChatGPT for Cyber Security 2024 🌟 4.6 - 1597 votes 💰 Original Price: $49.99

📖 Master Cyber Security/Ethical Hacking With Artificial Intelligence - Implement, Uncover Risks and Navigate The AI Era

🔊 Taught By: Luka Anicin, Aleksa Tamburkovski 📤 Download Full Course 📤 Download All Courses

💸 Understanding Popular ML Algorithms: 1️⃣ Linear Regression: Think of it as drawing a straight line through data points to predict future outcomes. 2️⃣ Logistic Regression: Like a yes/no machine - it predicts the likelihood of something happening or not. 3️⃣ Decision Trees: Imagine making decisions by answering yes/no questions, leading to a conclusion. 4️⃣ Random Forest: It's like a group of decision trees working together, making more accurate predictions. 5️⃣ Support Vector Machines (SVM): Visualize drawing lines to separate different types of things, like cats and dogs. 6️⃣ K-Nearest Neighbors (KNN): Friends sticking together - if most of your friends like something, chances are you'll like it too! 7️⃣ Neural Networks: Inspired by the brain, they learn patterns from examples - perfect for recognizing faces or understanding speech. 8️⃣ K-Means Clustering: Imagine sorting your socks by color without knowing how many colors there are - it groups similar things. 9️⃣ Principal Component Analysis (PCA): Simplifies complex data by focusing on what's important, like summarizing a long story with just a few key points.

For those of you who are new to Neural Networks, let me try to give you a brief overview.

Neural networks are computational models inspired by the human brain's structure and function. They consist of interconnected layers of nodes (or neurons) that process data and learn patterns. Here's a brief overview:

1. Structure: Neural networks have three main types of layers: - Input layer: Receives the initial data. - Hidden layers: Intermediate layers that process the input data through weighted connections. - Output layer: Produces the final output or prediction. 2. Neurons and Connections: Each neuron receives input from several other neurons, processes this input through a weighted sum, and applies an activation function to determine the output. This output is then passed to the neurons in the next layer. 3. Training: Neural networks learn by adjusting the weights of the connections between neurons using a process called backpropagation, which involves: - Forward pass: Calculating the output based on current weights. - Loss calculation: Comparing the output to the actual result using a loss function. - Backward pass: Adjusting the weights to minimize the loss using optimization algorithms like gradient descent. 4. Activation Functions: Functions like ReLU, Sigmoid, or Tanh are used to introduce non-linearity into the network, enabling it to learn complex patterns. 5. Applications: Neural networks are used in various fields, including image and speech recognition, natural language processing, and game playing, among others. Overall, neural networks are powerful tools for modeling and solving complex problems by learning from data. ENJOY LEARNING 👍👍

💸 Use of Machine Learning in Data Analysis

🔅 Ethical Hacking: Hacking Web Servers and Web Applications 🌐 Author: Malcolm Shore 🔰 Level: Intermediate ⏰ Duration: 1h 39m

🌀 Find out about the protocols used to access websites, and how to test websites and web applications to prevent exploitation through cyberattacks.

📗 Topics: Ethical Hacking 📤 Join Ethical Hacking and Cybersecurity for more courses

For those of you who are new to Neural Networks, let me try to give you a brief overview.

Neural networks are computational models inspired by the human brain's structure and function. They consist of interconnected layers of nodes (or neurons) that process data and learn patterns. Here's a brief overview:

1. Structure: Neural networks have three main types of layers: - Input layer: Receives the initial data. - Hidden layers: Intermediate layers that process the input data through weighted connections. - Output layer: Produces the final output or prediction. 2. Neurons and Connections: Each neuron receives input from several other neurons, processes this input through a weighted sum, and applies an activation function to determine the output. This output is then passed to the neurons in the next layer. 3. Training: Neural networks learn by adjusting the weights of the connections between neurons using a process called backpropagation, which involves: - Forward pass: Calculating the output based on current weights. - Loss calculation: Comparing the output to the actual result using a loss function. - Backward pass: Adjusting the weights to minimize the loss using optimization algorithms like gradient descent. 4. Activation Functions: Functions like ReLU, Sigmoid, or Tanh are used to introduce non-linearity into the network, enabling it to learn complex patterns. 5. Applications: Neural networks are used in various fields, including image and speech recognition, natural language processing, and game playing, among others. Overall, neural networks are powerful tools for modeling and solving complex problems by learning from data. ENJOY LEARNING 👍👍

💸 Use of Machine Learning in Data Analysis

🔅 Building a Video Transcriber with Node.js and Google AI Speech-To-Text API 🌐 Author: Fikayo Adepoju 🔰 Level: Intermediate ⏰ Duration: 1h 9m

🌀 Learn how to transcribe audio from video by integrating Node.js applications with the Google AI Speech-to-Text API.

📗 Topics: Machine Transcription, Artificial Intelligence, Node.js 📤 Join Machine Learning and Artificial intelligence for more courses

Javascript for Everything: JS + React = Web Development JS + Three.js = 3D Visualization JS + Angular = Web Applications JS + Phaser = Game Development JS + Vue.js = Progressive Web Apps JS + TensorFlow.js = Machine Learning JS + Node.js = Server-Side Development JS + Electron = DesktopApp Development JS + React Native = MobileApp Development #javascript

🐱 Top 10 GitHub repos ✅ To succeed in data science interviews 1️⃣ ML Interviews repo ⏪ Including machine learning interview questions from basic topics to complex topics such as neural networks and reinforcement learning. 🔗 Link: GitHub-Repos ✂️✂️✂️✂️✂️✂️ 2️⃣ Repo 500AI Projects with Code ⏪ 500 artificial intelligence, machine learning, deep learning, CV, NLP projects with code. 🔗 Link: GitHub-Repos ✂️✂️✂️✂️✂️✂️ 3️⃣ 100 Days of ML Code repo ⏪ A hundred-day program for learning and practicing machine learning coding. 🔗 Link: GitHub-Repos ✂️✂️✂️✂️✂️✂️ 4️⃣ Awesome Data Science repo ⏪ This repository is a curated list of great data science resources including books, software, and tools. 🔗 Link: GitHub-Repos ✂️✂️✂️✂️✂️✂️ 5️⃣ Data Science for Beginners repo ⏪ This repository from Microsoft has a 10-week course with 20 lessons for beginners. Each lesson includes videos, quizzes, challenges and more. 🔗 Link: GitHub-Repos ✂️✂️✂️✂️✂️✂️ 6️⃣ Data Science Masters repo ⏪ This repository provides a comprehensive, open-source curriculum to prepare students for entry-level roles in data science, at no cost! 🔗 Link: GitHub-Repos ✂️✂️✂️✂️✂️✂️ 7️⃣ Awesome AI repo ⏪ A comprehensive collection of artificial intelligence learning resources including articles, books, courses and related tools. 🔗 Link: GitHub-Repos ✂️✂️✂️✂️✂️✂️ 8️⃣ Awesome Public Datasets repo ⏪ Multiple examples of machine learning algorithms in Python with interactive Jupyter demos and mathematical explanation. 🔗 Link: GitHub-Repos ✂️✂️✂️✂️✂️✂️ 9️⃣ Awesome Public Datasets repo ⏪ This repository provides a collection of data science job interview questions and answers that are great for preparation. 🔗 Link: GitHub-Repos ✂️✂️✂️✂️✂️✂️ 1️⃣ Data Science Collected Resources repository ⏪ A list of the best data science resources including books, articles, and practical tools for learning and developing data science skills. 🔗 Link: GitHub-Repos 📂 Tags: #DataScience #Python #ML #AI ⭐️ The opportunity is not too late, register before it is too late 😮

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🔅 Python: Working with Predictive Analytics 🌐 Author: Isil Berkun 🔰 Level: Advanced ⏰ Duration: 1h 17m

🌀 Find out how to use prebuilt Python libraries for predictive analytics and discover insights about the future.

📗 Topics: Predictive Analytics, Python 📤 Join Learn Python for more courses