Agentic AI Coding

🔰 Artificial Intelligence A-Z 2025: Build 7 AI + LLM & ChatGPT 🌟 4.4 - 43970 votes 💰 Original Price: $89.99

📖 Combine the power of Data Science, Machine Learning and Deep Learning to create powerful AI for Real-World applications!

🔊 Taught By: Hadelin de Ponteves, Kirill Eremenko 📤 Download Full Course 📤 Download All Courses

Open Source LLMs Part-1

AI tools for online business

🔗 Basics of Machine Learning 👇👇

Machine learning is a branch of artificial intelligence where computers learn from data to make decisions without explicit programming. There are three main types:

1. Supervised Learning: The algorithm is trained on a labeled datasets, learning to map input to output. For example, it can predict housing prices based on features like size and location. 2. Unsupervised Learning: The algorithm explores data patterns without explicit labels. Clustering is a common task, grouping similar data points. An example is customer segmentation for targeted marketing. 3. Reinforcement Learning: The algorithm learns by interacting with an environment. It receives feedback in the form of rewards or penalties, improving its actions over time. Gaming AI and robotic control are applications. 📖 Key concepts include: - Features and Labels: Features are input variables, and labels are the desired output. The model learns to map features to labels during training. - Training and Testing: The model is trained on a subset of data and then tested on unseen data to evaluate its performance. - Overfitting and Underfitting: Overfitting occurs when a model is too complex and fits the training data too closely, performing poorly on new data. Underfitting happens when the model is too simple and fails to capture the underlying patterns. - Algorithms: Different algorithms suit various tasks. Common ones include linear regression for predicting numerical values, and decision trees for classification tasks. In summary, machine learning involves training models on data to make predictions or decisions. Supervised learning uses labeled data, unsupervised learning finds patterns in unlabeled data, and reinforcement learning learns through interaction with an environment. Key considerations include features, labels, overfitting, underfitting, and choosing the right algorithm for the task.

🔗 30 Useful AI Apps That Can Help You in 2025

AI apps are taking over the world. There’s an AI app for every conceivable use case. Here are some AI apps for different categories:

1 - General Purpose: Perplexity, Anthropic Claude, Grok, ChatGPT, and Gemini 2 - Writing Code: Cursor, Replit, Windsurf AI, Github Copilot, and Tabnine 3 - Productivity: Adobe (PDF Chat), Gemini for Gmail, Gamma (AI slide deck), WisprFlow (AI voice dictation), and Granola (AI notetaker) 4 - Audience Building: Delphi (AI text, voice), HeyGen (video translation), Persona (AI agent builder), Captions (AI video editing), and OpusClips (Video repurposing) 5 - Creativity: ElevenLabs (realistic AI voices), Midjourney, Suno AI (music generation), Krea (enhance images), and Photoroom (AI image editing) 6 - Learning and Growth: Particle News App, Rosebud (AI journal app), NotebookLM, GoodInside (parenting co-pilot), and Ash (AI counselor).