AI and Machine Learning Courses
Browse 40 structured AI and ML courses on neo-ai — each includes video lessons, reading material, and interactive quizzes.
All courses
- Python for AI (Beginner) — Learn Python with a focus on data science, ML, and AI workflows.
- Mathematics for Machine Learning (Beginner) — Build the mathematical foundation essential for understanding ML algorithms.
- Data Science Fundamentals (Beginner) — Learn data wrangling, EDA, feature engineering, and pipeline design.
- Machine Learning Basics (Beginner) — Master the core algorithms and concepts of machine learning.
- Practical Prompt Engineering (Beginner) — Learn to communicate with AI models effectively — craft prompts that get reliable, high-quality outputs every time.
- SQL for Data & AI (Beginner) — Master SQL — the universal language for querying, transforming, and engineering data that powers AI systems.
- Neural Networks Deep Dive (Intermediate) — Understand how neural networks work from perceptrons to deep architectures.
- PyTorch Mastery (Intermediate) — Build and train neural networks with PyTorch from beginner to expert.
- TensorFlow & Keras (Intermediate) — Build production-ready models with TensorFlow and the Keras API.
- AI Agents: Foundations & Patterns (Intermediate) — Build autonomous AI agents that reason, plan, and act — from the ReAct pattern to tool calling and memory systems.
- RAG & Vector Databases (Intermediate) — Build retrieval-augmented AI systems that answer questions from your own documents using semantic search and vector stores.
- Multi-Agent Systems & Orchestration (Intermediate) — Design and build networks of specialized AI agents that collaborate, delegate, and self-correct to solve complex tasks.
- Transformers & NLP (Advanced) — Master the Transformer architecture and modern NLP from attention to LLMs.
- Computer Vision with Deep Learning (Advanced) — From image classification to object detection, segmentation, and generative models.
- Reinforcement Learning (Advanced) — Teach agents to make optimal decisions through rewards, exploration, and policy optimization.
- Generative AI & Foundation Models (Advanced) — Master modern generative models — from diffusion to large language models and multimodal AI.
- MLOps & Model Deployment (Advanced) — Take models from notebook to production — CI/CD, monitoring, and infrastructure.
- AI Ethics & Safety (Advanced) — Understand bias, fairness, safety, and the societal impact of AI systems.
- Technical AI Safety (Advanced) — Alignment, scalable oversight, evaluation and red-teaming, and catastrophic-risk framing for frontier ML systems.
- AI Security (Advanced) — Defend ML systems and LLM applications against adversarial attacks, pipeline abuse, prompt injection, model theft, and supply-chain compromise.
- General Cybersecurity (Intermediate) — Core security concepts for anyone building or operating software — from the CIA triad and IAM to networks, crypto, app risks, incidents, and zero trust.
- Git & Version Control for AI (Beginner) — Master Git for AI projects — branching, merging, collaboration, and reproducible experiments.
- Jupyter & Notebooks for Data Science (Beginner) — Master Jupyter notebooks for exploratory analysis, prototyping, and reproducible research.
- APIs & Web Scraping for AI (Beginner) — Fetch data from APIs and scrape the web for AI training and applications.
- Linux & Command Line for ML (Beginner) — Master the terminal for ML workflows — file operations, scripting, and cloud servers.
- Statistics for Data Science (Beginner) — Build statistical foundations for data analysis and machine learning.
- Fine-Tuning LLMs (Intermediate) — Adapt pre-trained language models for your specific tasks and domains.
- LangChain & LLM Applications (Intermediate) — Build production LLM applications with LangChain — chains, agents, and tools.
- Time Series & Forecasting (Intermediate) — Forecast temporal data with statistical and deep learning methods.
- ML Experiment Tracking (Intermediate) — Track experiments, compare runs, and reproduce ML workflows.
- Large-Scale ML Systems (Advanced) — Design and operate ML systems at scale — distributed training, serving, and infrastructure.
- Federated Learning (Advanced) — Train models on distributed data without centralizing it — privacy-preserving ML.
- Causal Inference for ML (Advanced) — Move beyond correlation to causal inference — causal graphs, identification, and estimation.
- Production LLM Deployment (Advanced) — Deploy and optimize LLMs for production — inference, latency, cost, and scaling.
- Quantum Physics (Beginner) — Build intuition for wavefunctions, superposition, measurement, and the postulates that underpin quantum computing.
- Quantum Computing Basics (Beginner) — Learn qubits, quantum gates, circuits, and how quantum computers differ from classical machines.
- Intermediate Quantum Computing (Intermediate) — Algorithms, error mitigation, variational methods, and practical programming for NISQ-era hardware.
- Advanced Quantum Computing (Advanced) — Fault tolerance, error correction, advanced algorithms, and research frontiers in quantum information.