AI Skills

📂 Full description Explore the ins and outs of hacking machine learning with the cybersecurity training experts at Infosec Institute. Deep dive into topics such as hacking a CAPTCHA system, fuzzing a target, evading malware detection, and attacking machine learning systems. Plus, learn about deepfakes and how to perform backdoor attacks on machine learning. This course was created by Infosec Institute. We are pleased to host this training in our library.

🔅 Machine Learning for Red Team Hackers by Infosec 🌐 Author: Infosec Institute 🔰 Level: Intermediate ⏰ Duration: 3h 39m

🌀 Learn the various techniques used in hacking machine learning.

📗 Topics: Ethical Hacking, Machine Learning, Red Teaming 📤 Join Artificial Intelligence and Machine Learning for more courses

🔗 What is an AI Agent?

An AI agent is a software program that can interact with its environment, gather data, and use that data to achieve predetermined goals. AI agents can choose the best actions to perform to meet those goals.

Key characteristics of AI agents are as follows: An agent can perform autonomous actions without constant human intervention. Also, they can have a human in the loop to maintain control. - Agents have a memory to store individual preferences and allow for personalization. It can also store knowledge. An LLM can undertake information processing and decision-making functions. - Agents must be able to perceive and process the information available from their environment. - Agents can also use tools such as accessing the internet, using code interpreters and making API calls. - Agents can also collaborate with other agents or humans.

Multiple types of AI agents are available such as learning agents, simple reflex agents, model-based reflex agents, goal-based agents, and utility-based agents.

A system with AI agents can be built with different architectural approaches. 1 - Single Agent: Agents can serve as personal assistants. 2 - Multi-Agent: Agents can interact with each other in collaborative or competitive ways. 3 - Human Machine: Agents can interact with humans to execute tasks more efficiently.

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🔗 Most ML roadmaps

If you’re tired of bloated diagrams and endless theory, this one’s for you.

This is the 10-step roadmap I wish someone gave me earlier focused on real-world impact, not just flashy model builds. Swipe through to see: ✅ The core skills you actually need ✅ What separates you from junior talent ✅ What most self-taught engineers skip Whether you’re transitioning from data analyst, coming from software dev, or just trying to stop tutorial-hopping...

🔥 Google has introduced InstructPipe , an AI editor for ML pipelines that works via text queries. ❔ What is InstructPipe? InstructPipe is an AI assistant that transforms text commands into visual flowcharts representing machine learning pipelines. The system uses two large language model (LLM) modules and a code interpreter to generate pseudocode and visualize it in a graph editor. This is a low-code approach: you simply connect ready-made components (nodes) without writing code. 🌟 How does this work? 1️⃣ The user enters a text instruction describing the desired pipeline. 2️⃣ LLM modules process the instruction and generate the corresponding pseudocode. 3️⃣ The code interpreter converts pseudocode into a visual flowchart that you can edit and customize. ✔️ Benefits of InstructPipe 🟡 Accessibility: Allows newcomers to programming to create complex ML pipelines without having to write code. 🟡 Flexibility: Accepts text description in any form, no strict format. 🟡 Lower barrier to entry: Simplifies the process of learning and prototyping ml projects. 🔜 Read more

🔗 Top 5 machine learning projects: 1. Predicting House Prices: Build a machine learning model that predicts house prices based on features such as location, size, number of bedrooms, etc. This project will help you understand regression techniques and feature engineering. 2. Image Classification: Create a model that can classify images into different categories such as cats vs. dogs, fruits, or handwritten digits. This project will introduce you to convolutional neural networks (CNNs) and image processing. 3. Sentiment Analysis: Develop a sentiment analysis model that can classify text data as positive, negative, or neutral. This project will help you learn natural language processing techniques and text classification algorithms. 4. Credit Card Fraud Detection: Build a model that can detect fraudulent credit card transactions based on transaction data. This project will help you understand anomaly detection techniques and imbalanced classification problems. 5. Recommendation System: Create a recommendation system that suggests products or movies to users based on their preferences and behavior. This project will introduce you to collaborative filtering and recommendation algorithms.

👨🏻‍💻 One of the most popular GitHub repositories for "learning and using algorithms in Python" is The Algorithms - Python repo with 196K stars. ✏️ It has a lot of organized and categorized code that you can use to find, read, and run different algorithms. Everything you can think of is here; from simple algorithms like sorting to advanced algorithms for machine learning, artificial intelligence, neural networks, and more. ✅ Why should we use it? 🔢 For learning: If you're looking to learn algorithms in action, this is great. 🔢 For practice: You can take the codes, run them, and modify them to better understand. 🔢 For projects : You can even use the codes here in real-life or academic projects. 🔢 For interviews: If you're preparing for data science interviews, this is full of practical algorithms. ┌ 🏳️‍🌈 The Algorithms - Python └ 🐱 GitHub-Repos

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🔗 Types of Machine Learning

🔗 Instill Core is a universal tool for working with unstructured data. This open-source project offers a comprehensive solution for ETL processing, data preparation for AI, and deployment of LLM models. The platform combines document, image, and video processing into a single workflow, which is especially valuable for RAG scenarios and building AI pipelines. Instill Core can be easily integrated into existing systems via the Python/TypeScript SDK or CLI. Local execution is possible via Docker, and ready-made recipes allow you to quickly deploy PDF parsing, web scraping, or image segmentation. 🔗 GitHub

🔗 Instill Core is a universal tool for working with unstructured data. This open-source project offers a comprehensive solution for ETL processing, data preparation for AI, and deployment of LLM models. The platform combines document, image, and video processing into a single workflow, which is especially valuable for RAG scenarios and building AI pipelines. Instill Core can be easily integrated into existing systems via the Python/TypeScript SDK or CLI. Local execution is possible via Docker, and ready-made recipes allow you to quickly deploy PDF parsing, web scraping, or image segmentation. 🔗 GitHub

🔗 Instill Core is a universal tool for working with unstructured data. This open-source project offers a comprehensive solution for ETL processing, data preparation for AI, and deployment of LLM models. The platform combines document, image, and video processing into a single workflow, which is especially valuable for RAG scenarios and building AI pipelines. Instill Core can be easily integrated into existing systems via the Python/TypeScript SDK or CLI. Local execution is possible via Docker, and ready-made recipes allow you to quickly deploy PDF parsing, web scraping, or image segmentation. 🔗 GitHub

Machine Learning Algorithms every data scientist should know: 📌 Supervised Learning: 🔹 Regression ∟ Linear Regression ∟ Ridge & Lasso Regression ∟ Polynomial Regression 🔹 Classification ∟ Logistic Regression ∟ K-Nearest Neighbors (KNN) ∟ Decision Tree ∟ Random Forest ∟ Support Vector Machine (SVM) ∟ Naive Bayes ∟ Gradient Boosting (XGBoost, LightGBM, CatBoost) 📌 Unsupervised Learning: 🔹 Clustering ∟ K-Means ∟ Hierarchical Clustering ∟ DBSCAN 🔹 Dimensionality Reduction ∟ PCA (Principal Component Analysis) ∟ t-SNE ∟ LDA (Linear Discriminant Analysis) 📌 Reinforcement Learning (Basics): ∟ Q-Learning ∟ Deep Q Network (DQN) 📌 Ensemble Techniques: ∟ Bagging (Random Forest) ∟ Boosting (XGBoost, AdaBoost, Gradient Boosting) ∟ Stacking Don’t forget to learn model evaluation metrics: accuracy, precision, recall, F1-score, AUC-ROC, confusion matrix, etc.

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📖 Postman API testing for manual and automated tests. Automate with Newman, GitLab, Jenkins or any other CI tool.

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