AI & Machine Learning

Comprehensive course on AI Agent Engineering — from Python & ML foundation, NLP & LLMs (LLaMA, Mistral, Qwen, Phi), RAG with Vector DB (FAISS, Milvus, Pinecone), to building AI Agent systems with LangChain, LlamaIndex, CrewAI, LangGraph. Practice Fine-tuning, Prompt Engineering, Tool Calling, Multi-Agent. Deploy production with FastAPI, Docker, Microservices, MLOps, CI/CD on AWS/Azure/GCP. Fully meets AI/ML Engineer recruitment requirements at leading technology companies.

A hands-on series for building a local AI stack with Gemma 4 on Apple Silicon following engineering best practices. From Ollama setup, API integration, RAG pipeline, hybrid retrieval, to observability and hardening for internal environments.

Machine Learning roadmap for beginners starts from zero, following an easy-to-understand method: intuition first, code later, just enough math. The course goes from environment setup, first model, proper evaluation, overfitting/data leakage prevention, to common models (Linear, Logistic, Tree, XGBoost), unsupervised, time series, and production deployment. Each cluster of lessons has mini-projects, real-life challenges and a clear output checklist.

Comprehensive guide to running LLM locally on Mac Apple Silicon (M1/M2/M3/M4) with Ollama and MLX. From initial installation to 3x acceleration with the MLX framework, managing multiple models, integrating APIs into applications, and optimizing GPU/RAM performance. All hands-on, privacy-first, no internet required.

Comprehensive course on AI in Healthcare — from Medical Imaging with CNN, NLP for medical records, Drug Discovery with GNN, to implementing HIPAA/FDA compliant AI. Practice with Python, PyTorch, Hugging Face, and standard medical datasets such as MIMIC, CheXpert, PubMed.

Comprehensive course on Generative AI for images and video — from GAN, VAE platforms to Stable Diffusion, DALL-E, Midjourney API. Practice image generation, inpainting, style transfer, video generation, and build Generative AI pipeline production-ready with Python, Hugging Face Diffusers, and ComfyUI.

Comprehensive course on Recommendation Systems — from Collaborative Filtering, Content-based, Matrix Factorization to Deep Learning RecSys with Two-Tower, Graph Neural Networks, Sequence Models. Hands-on with Python, PyTorch, LightFM, and deploy production-ready recommendation engine.

Comprehensive course on Multimodal AI — from Vision-Language Models (CLIP, LLaVA), Visual Question Answering, Image Captioning, to Document AI, Video Understanding. Practice with Python, PyTorch, Hugging Face Transformers, and state-of-the-art models like GPT-4V, Gemini, LLaVA.

Comprehensive course on Natural Language Processing (NLP) — from tokenization platforms, word embeddings, to Transformer architecture, BERT, GPT. Practice text classification, NER, sentiment analysis, machine translation, question answering and build a production-ready NLP pipeline with Python, Hugging Face, and spaCy.

Comprehensive Reinforcement Learning course — from MDP, Q-Learning to Deep RL platforms with DQN, Policy Gradient, PPO, SAC. Practice game AI, robotics simulation, RLHF for LLM, and deploy RL agents production-ready with Python, Gymnasium, Stable-Baselines3.

Comprehensive AI for Speech & Audio course — from audio signal processing, Speech Recognition (ASR) with Whisper, Text-to-Speech (TTS) with VITS, Voice Cloning, Speaker Verification, to Music AI. Practice with Python, PyTorch, Hugging Face, librosa, and state-of-the-art models.

A comprehensive course on Time Series AI — from Statistical Methods (ARIMA, ETS), Machine Learning (XGBoost, LightGBM), to Deep Learning (LSTM, Transformer, TimesFM). Application of forecasting, anomaly detection, classification. Practice with Python, statsmodels, scikit-learn, PyTorch, and specialized frameworks.

The series builds an entire AI system for a Fashion & Print-on-Demand platform — from AI Design Generation (Stable Diffusion, ControlNet), AI Editing in natural language, Personalization System, Virtual Try-On with Computer Vision, to Print File Optimization and AI Product Generation. Each article is an independent AI module that can be deployed in production.

A hands-on series on building a complete AI Agent Platform using TypeScript — from monorepo design, LLM Router, Tool Registry, RAG Pipeline, Workflow Engine, Multi-tenant RBAC to deploying Docker production. Learn through the actual source code of xClaw — an open-source platform running in production.

Practical course on Computer Vision — from CNN, Object Detection (YOLO), Image Segmentation (SAM) to Vision Transformer and Multimodal AI. Hands-on with PyTorch, Ultralytics YOLO, Hugging Face. Deploy CV model to production with TensorRT and ONNX.

In-depth course on MLOps and LLMOps — the art of bringing AI models from prototype to production safely and effectively. From experiment tracking, CI/CD for ML, to LLM observability, cost optimization, guardrails, and compliance. Highest paying skills in AI.

Comprehensive course on Prompt Engineering — from writing basic prompts to Chain-of-Thoughts, Tree-of-Thoughts, Multimodal Prompting and building a Prompt Library for production. The most essential skill in the AI ​​age, immediately applicable to ChatGPT, Claude, Gemini.

In-depth course on Retrieval-Augmented Generation (RAG) — techniques for connecting the LLM to your own data. From basic RAG to Graph RAG, Agentic RAG, Multimodal RAG. Hands-on with ChromaDB, Qdrant, LangChain, LlamaIndex. Deploy "Chat with Documents" to production.

Comprehensive course on Fine-tuning Large Language Models — when to fine-tune, data preparation, fine-tuning on Google Gemini/Vertex AI, OpenAI, and open-source (LoRA/QLoRA). Compare Fine-tuning vs RAG, model evaluation methods, and production deployment. Calculate actual costs.

Practical course on building AI Agents — from simple chatbots to complex Multi-Agent systems. Proficient in Function Calling, Tool Use, RAG, MCP, LangGraph, CrewAI and deploying Agent to production. Each lesson is coded hands-on with Python.

Comprehensive course on Artificial Intelligence and Large Language Models — from Neural Networks platform, Transformer architecture, to Fine-tuning, RAG, AI Agents and production deployment. Learn hands-on with Python, PyTorch, and LLM APIs.