LinkedIn Auto Connect Bot
Build a LinkedIn automation bot in Python using Selenium and BeautifulSoup that sends personalized connection requests to suggested profiles automatically.
Code-first, practical tutorials taking you from data visualization fundamentals to multi-agent architectures with LangGraph, local LLMs, and RAG systems.
Follow sequential roadmap models carefully designed to build core production skills.
A structured, progressive roadmap for developers seeking to master AI agents, covering Python foundations, LangChain, LangGraph, and private multi-agent RAG.
A comprehensive curriculum taking you from zero programming knowledge to professional data manipulation, mathematical visualization, and core ML model building.
Dive deep into specialized branches of Artificial Intelligence and Software Engineering.
LLMs, RAG, Agents, LangGraph & custom AI applications.
Data analysis, EDA, feature engineering & classic ML models.
PyTorch, neural networks, CV & advanced architectures.
Transformers, BERT, text analysis & sequence learning.
Access free code-centric tutorials across multiple fields, clean and free of ads.
Build a LinkedIn automation bot in Python using Selenium and BeautifulSoup that sends personalized connection requests to suggested profiles automatically.
Learn how to reduce high-dimensional feature spaces using LDA and PCA with scikit-learn. Applied to the Santander customer dataset with accuracy and speed comparisons.
Learn how to use mutual information (entropy gain) to select the most predictive features for classification and regression in Python with scikit-learn.
Learn how to apply Recursive Feature Elimination (RFE) in Python using Random Forest and Gradient Boosting estimators to select the most predictive features from the breast cancer dataset.
Master end-to-end NLP text processing in Python. Covers Bag of Words, TF-IDF, Word2Vec, spaCy tokenization, and classification with machine learning.
A hands-on guide to building line, bar, histogram, box, scatter, KDE, Andrews curve, and subplot visualizations directly from a pandas DataFrame or Series.
Enroll in complete structured packages with production codebases, private GitHub repositories, and verifiable certificates.
Build MCP servers & clients with Python, Streamlit, ChromaDB, LangChain, LangGraph agents, and Ollama integrations.
Step-by-Step Guide to RAG with LangChain, LangGraph, and Ollama | DeepSeek R1, QWEN, LLAMA, FAISS.
Master Langchain v1, Local LLM Projects, Ollama, DeepSeek, LLAMA 3.2, Complete Integration Guide.
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