AI Skills

🔰 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).

🔗 Roadmap to become NLP Expert in 2025

🔅 Deep Learning and Generative AI: Data Prep, Analysis, and Visualization with Python 🌐 Author: Gwendolyn Stripling 🔰 Level: Intermediate ⏰ Duration: 1h 56m

🌀 Learn the knowledge and practical skills needed to effectively utilize deep learning techniques using the Python programming language.

📗 Topics: Generative AI, Deep Learning, Python 📤 Join Artificial intelligence for more courses

Machine Learning Algorithms Cheatsheet ✅

🔅 Machine Learning and AI Foundations: Advanced Decision Trees with KNIME 🌐 Author: Keith McCormick 🔰 Level: Advanced ⏰ Duration: 1h 33m

🌀 Learn to go beyond the basic decision tree algorithms in KNIME by accessing WEKA, R, and Python-based decision tree and rule induction algorithms from within the KNIME platform.

📗 Topics: Decision Trees, Knime, Machine Learning 📤 Join Artificial intelligence for more courses

🔅 Trump calls China's DeepSeek AI a "wake-up call"

The sudden rise of a Chinese startup called DeepSeek sent U.S. tech stocks tumbling Monday. DeepSeek says it created an artificial intelligence model in much less time and for much less money than U.S. companies. President Trump called it a "wake-up call."

Generative AI in Data Analytics ✅

🔗 Master AI (Artificial Intelligence) in 10 days 👇👇 Day 1: Introduction to AI - Start with an overview of what AI is and its various applications. - Read articles or watch videos explaining the basics of AI. Day 2-3: Machine Learning Fundamentals - Learn the basics of machine learning, including supervised and unsupervised learning. - Study concepts like data, features, labels, and algorithms. Day 4-5: Deep Learning - Dive into deep learning, understanding neural networks and their architecture. - Learn about popular deep learning frameworks like TensorFlow or PyTorch. Day 6: Natural Language Processing (NLP) - Explore the basics of NLP, including tokenization, sentiment analysis, and named entity recognition. Day 7: Computer Vision - Study computer vision, including image recognition, object detection, and convolutional neural networks. Day 8: AI Ethics and Bias - Explore the ethical considerations in AI and the issue of bias in AI algorithms. Day 9: AI Tools and Resources - Familiarize yourself with AI development tools and platforms. - Learn how to access and use AI datasets and APIs. Day 10: AI Project - Work on a small AI project. For example, build a basic chatbot, create an image classifier, or analyze a dataset using AI techniques.

🛜 Nmap For Penetration Testing: Beginner To Advanced 🌟 4.5 - 416 votes 💰 Original Price: $49.99

📖 Master network mapping, scanning and enumeration with Nmap.

🔊 Taught By: HackerSploit Academy 📤 Download Full Course 📤 Download All Courses

🔅 More Python Tips, Tricks, and Techniques for Data Science 🌐 Author: Harshit Tyagi 🔰 Level: Intermediate ⏰ Duration: 2h 15m

🌀 Deliver valuable insights to your users with Python. Get practical tips and techniques that can help you enhance your Python data science workflow.

📗 Topics: Data Science, Python 📤 Join Data Analysis and Databases for more courses

🔗 Essential Tools, Libraries, and Frameworks to learn Artificial Intelligence 1. Programming Languages: Python R Java Julia 2. AI Frameworks: TensorFlow PyTorch Keras MXNet Caffe 3. Machine Learning Libraries: Scikit-learn: For classical machine learning models. XGBoost: For boosting algorithms. LightGBM: For gradient boosting models. 4. Deep Learning Tools: TensorFlow PyTorch Keras Theano 5. Natural Language Processing (NLP) Tools: NLTK (Natural Language Toolkit) SpaCy Hugging Face Transformers Gensim 6. Computer Vision Libraries: OpenCV DLIB Detectron2 7. Reinforcement Learning Frameworks: Stable-Baselines3 RLlib OpenAI Gym 8. AI Development Platforms: IBM Watson Google AI Platform Microsoft AI 9. Data Visualization Tools: Matplotlib Seaborn Plotly Tableau 10. Robotics Frameworks: ROS (Robot Operating System) MoveIt! 11. Big Data Tools for AI: Apache Spark Hadoop 12. Cloud Platforms for AI Deployment: Google Cloud AI AWS SageMaker Microsoft Azure AI 13. Popular AI APIs and Services: Google Cloud Vision API Microsoft Azure Cognitive Services IBM Watson AI APIs 14. Learning Resources and Communities: Kaggle GitHub AI Projects Papers with Code ENJOY LEARNING 👍👍