Premium AI & ML Courses
High-level training to master the most in-demand AI technologies
Introduction to AI — Complete
The full journey through modern AI, from foundations to LLM-powered products.
Python for AI — Complete
NumPy, Pandas, scikit-learn, matplotlib and the tooling you actually ship with.
Mathematics for AI
Linear algebra, calculus and statistics, taught for practitioners.
Supervised Learning
Regression, classification, model evaluation and the complete workflow.
Unsupervised Learning
Clustering, dimensionality reduction and anomaly detection in practice.
Feature Engineering
Turn raw data into features your models can actually learn from.
Deep Learning Fundamentals
Rigorous walkthrough of neural networks, optimization and regularization.
TensorFlow & Keras
Build, train and export production models with Google's framework.
PyTorch
The framework used by most research teams and modern AI products.
Convolutional Neural Networks
From convolution arithmetic to modern architectures for vision.
RNN & LSTM
Sequence modelling: recurrent networks, LSTM, GRU and their limits.
Transformers & Attention
The architecture behind every modern LLM, explained end-to-end.
Natural Language Processing
Tokenization, embeddings, classification and modern NLP pipelines.
Advanced Computer Vision
Detection, segmentation and tracking with production-grade models.
Generative AI
How models generate images, text and code — GANs, VAEs and diffusion.
Large Language Models
Architectures, training, evaluation and safe deployment of LLMs.
Prompt Engineering
Structured prompting techniques that outperform intuition every time.
RAG Systems
Retrieval-Augmented Generation: ground your LLM in real, up-to-date data.
Model Fine-tuning
LoRA, QLoRA and full fine-tuning — pick the right one and do it well.
MLOps — Complete
Pipelines, feature stores, model registries, monitoring and rollback.
AWS SageMaker
The full lifecycle of an ML model on the largest cloud provider.
Azure Machine Learning
Train, deploy and monitor models with the Azure ML platform.
GCP Vertex AI
Managed training, pipelines and endpoints on Google Cloud.
AI Ethics & Responsible AI
Bias, fairness, governance and the practical questions engineers face.
Reinforcement Learning
From Q-learning to policy gradients and RLHF.
LangChain
Compose LLMs, tools and memory into real applications.
Stable Diffusion
Understand and control image generation, from LoRA to ControlNet.
Small Language Models (SLMs)
Compact models: distillation, quantization and edge deployment.
Ollama — Local LLMs
Run capable open-source models on your own hardware, offline and private.
AI Agents & AutoGPT
Autonomous agents that plan, use tools and act — with sane guardrails.
CrewAI — Multi-Agents
Orchestrate teams of specialized agents that collaborate on tasks.
Apache Spark ML
Distributed machine learning on datasets that do not fit in memory.
Feature Stores
Serve consistent features online and offline with Feast and friends.
Time Series Forecasting
From classical ARIMA to deep learning approaches for forecasting.
Recommender Systems
Collaborative filtering, embeddings and ranking at scale.
TensorFlow Lite
Ship models to mobile and embedded devices without losing accuracy.
ONNX Runtime
One model format, every runtime: export, optimize and accelerate.
Streamlit
Turn notebooks into shareable data apps in an afternoon.
Gradio
Build demos of your models that anyone can try in the browser.
FastAPI for ML
Serve models as robust, documented, production-ready APIs.
Claude Code & Slash Commands
Every slash command, CLAUDE.md, skills, hooks, subagents, MCP, plugins and CI: agentic development from A to Z.
Elasticsearch, Kibana & Neo4j
Search and graphs in practice: 200,000 indexed articles, Kibana, OpenSearch, Cypher and recommendation, in a tested Docker kit.
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Courses created by AI engineers with several years of production experience
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