Artificial Intelligence

🔰 Machine Learning A-Z [2026]: AI, AWS, Python & R + LLM Prize 🌟 4.5 - 203785 votes 💰 Original Price: $64.99 📖 Learn to create Machine Learning Algorithms in Python, R and AWS from two Data Science experts. Code templates included. 🔊 Taught By: Kirill Eremenko, Hadelin de Ponteves 📤 Download Full Course 📤 Download All Courses

Time Complexity of 10 Most Popular ML Algorithms

🖥 Mindmap to learn machine learning

🔅 Applied Machine Learning: Value Estimation 📝 Learn to build, evaluate, and deploy value estimation models using machine learning with Python. 🌐 Author: Matt Harrison 🔰 Level: Intermediate ⏰ Duration: 1h 52m 📋 Topics: Model Training, Machine Learning, Artificial Intelligence 🔗 Join Machine Learning for more courses

🖥 Plexe 🛠 Plexe is a system that simplifies the creation of machine learning models by allowing users to describe their intent in natural language. 🔰 The system automatically generates functional models and is available as a Python library and cloud service. 🔰 Custom models are defined through a description including intent and input-output patterns, and can be created by a single team with support for distributed training using Ray. 🔰 Additionally, Plexe offers synthetic data generation and automatic schema inference features, supporting various LLM providers such as OpenAI and Anthropic. 🔗Links: https://github.com/plexe-ai/plexe

🖥 Top MLOps Truths Every Engineer Should Know (2026) 👇 • Python + ML libraries = your core skillset • Git is essential for model + experiment tracking • Cloud gives you the scale ML truly needs • Containers make ML environments reproducible • Kubernetes powers real ML production workloads • Clean data beats complex models every time • Monitor models continuously to catch drift early

🔅 Machine Learning in Telecommunication: From Basics to Real-World Cases 📝 Explore how machine learning optimizes telecom networks through predictive analytics, automation, and real-world applications like fault detection and traffic forecasting. 🌐 Author: Itelcotech 🔰 Level: Intermediate ⏰ Duration: 2h 11m 📋 Topics: Machine Learning, Telecommunications, Reinforcement Learning 🔗 Join Machine Learning for more courses

Learning n8n from scratch

Learning n8n from scratch

Learning n8n from scratch

Read this once. There won't be a second message. Brainlancer just launched today. Investor-backed marketplace for ALL AI freelancers. Designers, builders, copywriters, marketers, video creators, automation experts, consultants. If you build, design, write, or sell anything with AI, this is your moment. How it works: • Register free at brainlancer.com • Stripe verification, 5 minutes, instant approval • List up to 5 services from $49 to $4,999 • Add monthly subscriptions on top if you want • We bring the clients. You keep 80%. The deal: No subscription. No bidding. No chasing. We pay all marketing. Real talk: no services live yet. We just launched. Whoever joins first gets seen first. The first 100 Brainlancers are onboarding right now. In 6 months others will have founding status, recurring income, featured services on the homepage. You'll scroll past and remember this post. Don't. → brainlancer.com

Machine Learning Algorithm: 1. Linear Regression:    - Imagine drawing a straight line on a graph to show the relationship between two things, like how the height of a plant might relate to the amount of sunlight it gets. 2. Decision Trees:    - Think of a game where you have to answer yes or no questions to find an object. It's like a flowchart helping you decide what the object is based on your answers. 3. Random Forest:    - Picture a group of friends making decisions together. Random Forest is like combining the opinions of many friends to make a more reliable decision. 4. Support Vector Machines (SVM):    - Imagine drawing a line to separate different types of things, like putting all red balls on one side and blue balls on the other, with the line in between them. 5. k-Nearest Neighbors (kNN):    - Pretend you have a collection of toys, and you want to find out which toys are similar to a new one. kNN is like asking your friends which toys are closest in looks to the new one. 6. Naive Bayes:    - Think of a detective trying to solve a mystery. Naive Bayes is like the detective making guesses based on the probability of certain clues leading to the culprit. 7. K-Means Clustering:    - Imagine sorting your toys into different groups based on their similarities, like putting all the cars in one group and all the dolls in another. 8. Hierarchical Clustering:    - Picture organizing your toys into groups, and then those groups into bigger groups. It's like creating a family tree for your toys based on their similarities. 9. Principal Component Analysis (PCA):    - Suppose you have many different measurements for your toys, and PCA helps you find the most important ones to understand and compare them easily. 10. Neural Networks (Deep Learning):     - Think of a robot brain with lots of interconnected parts. Each part helps the robot understand different aspects of things, like recognizing shapes or colors. 11. Gradient Boosting algorithms:     - Imagine you are trying to reach the top of a hill, and each time you take a step, you learn from the mistakes of the previous step to get closer to the summit. XGBoost and LightGBM are like smart ways of learning from those steps.

Resonant is a mini-app that connects your decision patterns to your AI Agents. Generate your personal Agentic Memory Card now! https://t.me/ResonantAlphaBot/resonant?startapp

🔅 Machine Learning Foundations: Prototyping on the Edge 📝 Learn how to prototype concepts and products using machine learning on microcontrollers. 🌐 Author: Robert Gallup 🔰 Level: Beginner ⏰ Duration: 1h 21m 📋 Topics: Prototyping, Machine Learning 🔗 Join Machine Learning for more courses