Machine Learning
25 courses5 categories
Part of LearnData & AI
Machine learning as a topic covers the classical and deep-learning fundamentals that underpin every modern AI system — supervised learning, neural networks, optimization, and the math beneath them. Unlike the broader AI hub or the LLM-engineering specialty, this topic zooms into the model-building side: training networks from scratch, working with tabular and image data, understanding why models converge, and shipping non-LLM ML in production. No conversational chatbots front-and-center here.
The 2026 toolchain is the same one researchers actually use. PyTorch dominates day-to-day work; JAX holds the high-end research market and TPU workloads; scikit-learn remains the right answer for tabular problems before reaching for a neural net. NumPy, pandas, and polars handle data prep; Weights & Biases and MLflow track experiments; ONNX and TensorRT cover deployment when latency matters. Computer vision pipelines still lean on OpenCV alongside modern vision transformers.
What you'll find under this topic
- Supervised learning: regression, classification, decision trees, gradient boosting (XGBoost, LightGBM)
- Deep learning: feed-forward, convolutional, recurrent, transformer architectures from scratch
- PyTorch deep dives: autograd, custom datasets, distributed training, mixed precision
- Math foundations: linear algebra, probability, calculus, optimization theory for ML
- Computer vision: OpenCV pipelines, object detection, segmentation, image generation
- Model evaluation: cross-validation, calibration, fairness audits, drift detection
- Deployment: ONNX export, TorchServe, edge inference, model compression
Machine learning roles still hire across the whole industry — recommendation systems at Spotify, Netflix, and Uber; fraud detection at fintech; medical imaging at health-tech companies; perception stacks at autonomous-vehicle teams. The skill set is durable because the underlying math has not changed and the production problems (data drift, label noise, evaluation rigor) keep recurring.
Categories(5)
Computer vision is a dynamic field of artificial intelligence focused on enabling machines to interpret and make…
Deep learning is a transformative subset of machine learning centered around neural networks with multiple layers…
Machine learning is a subset of artificial intelligence that enables computers to learn from data and make decisions…
Math & Statistics underpin many aspects of software engineering and data science, providing the foundational tools…
OpenCV is a versatile computer vision library initially released in 2000 and maintained by the OpenCV community. It…
Courses(25)
Showing 1–25 of 25 courses
Updated 1mo agoIntensive course on modern computer vision models with a focus on practice, segmentation, detection, and deployment of solutions.6h 8m
Updated 2mo agoLearn how to create a complete ML solution from data to cloud deployment. Master the end-to-end pipeline and professional code architecture.2h 55m5/5
UpdatedLearn computer vision from first principles: camera geometry, 3D reconstruction, and building perception pipelines from scratch.2h 48m
Updated 3mo agoMaster machine learning with Hugging Face. A practical course from basics to real-world projects. Minimum theory, maximum practice.23h 23m
Updated 3mo agoEnhance your qualifications in machine learning by learning about infrastructure, deployment, and full lifecycle management of ML in 8 weeks. Become in demand.14h 2m
FreeUpdated 4mo agoLearn core regression models and use them in Python. You study linear, logistic, log, and Cox models with clear steps and real data.6h 20m
Updated 9mo agoTake the Let's Rust course and develop an ML service in Rust. Train the model, create a REST API for predictions, and deploy the service in Kubernetes.12h 40m
FreeUpdatedFree course on scaling machine learning with Spark ML: regression, classification, feature engineering, and hyperparameter tuning.2h 7m
UpdatedLearn to build and ship a real ML system, not just a notebook model, through a full project forecasting NYC taxi demand.4h 3m5/5
Updated 1y agoIn this practical course, you will learn how to build a complete data pipeline on the AWS platform - from obtaining data from the Twitter API to analysis, stora1h 33m5/5
Updated 1y agoStudy backpropagation and gradient descent by writing a simple neural network from scratch in Python - without any libraries, just the basics.4h 34m5/5
FreeUpdated 1y agoHave you ever heard the expression "data preparation and cleaning"? This is perhaps the most important part of the entire machine learning process.3h 7m
Updated 1y agoLearn how to build ML systems from scratch in a clear and direct way. You will work with real tasks and skip long theory that slows you down.32h 6m5/5
Updated 1y agoPredictive analytics and machine learning is a course that will help you master key concepts and practical skills in data.55m
Updated 2y agoUnleash the power of linear algebra to conquer the world of data science, machine learning, and artificial intelligence.20h 53m
UpdatedBuild a solid statistics foundation for data science, analysis, and interviews, connecting theory to real-world problems.2h 4m5/5
UpdatedLearn to design A/B tests, analyze results in Python, and explain your reasoning confidently for data science interviews.1h 47m5/5
Updated 2y agoPoker math is a vitally important aspect to No Limit Hold'em, but it is often overlooked or simply not used because many poker players fear it is too difficult9h 49m
Updated54-lesson trigonometry course from the unit circle to polar coordinates and De Moivre's Theorem, with worked examples and quizzes.10h 26m
Updated 2y agoThis 163-lesson course includes video and text explanations of everything from Probability and Statistics, and it includes 45 quizzes (with solutions!) and an a11h 29m5/5
Updated 2y agoThis 186-lesson course includes video and text explanations of everything from the Fundamentals, and it includes 48 quizzes (with solutions!) and an additional5h 38m
Updated 2y agoWelcome to Python for Financial Analysis and Algorithmic Trading! Are you interested in how people use Python to conduct rigorous financial analysis and.16h 54m3/5
Updated 2y agoIn 24 revealing lectures, Macroeconomics Made Clear will introduce you to the subject that people have been theorizing about and modeling for almost 100 years.12h 34m
Updated 2y agoOne universe is not enough. Learn about the Many-Worlds Interpretation of quantum mechanics in this exciting course taught by a renowned expert. Taught by Profe11h 56m5/5
UpdatedLearn OpenCV 3 through real examples: image processing, background subtraction for surveillance, object tracking, and text recognition.3h 53m
Related topics
Frequently asked questions
- Do I need a degree to work in machine learning?
- For pure research roles, usually yes — most ML researcher hires hold a PhD or a strong publication record. For applied ML engineering, no. Solid Python, statistics, the standard ML toolchain (scikit-learn, PyTorch), and a portfolio of real projects open most applied roles. The bachelor's-versus-PhD gap matters at frontier labs, much less at product companies.
- Machine learning vs LLM engineering — which to focus on?
- LLM engineering for fast time-to-impact at most product companies — the skill set leans more on software engineering than math. Classical ML for forecasting, recommendation systems, fraud, ranking, scientific computing, and any tabular-data problem where deep learning is overkill. Strong applied ML engineers know both and pick the right tool per problem.
- What math do I really need for ML?
- Linear algebra (vectors, matrices, eigenvalues conceptually), probability and statistics, basic calculus (gradients, partial derivatives). You don't need to derive everything from scratch; you do need to read papers and debug models without being lost. Most applied engineers refresh these progressively, picking up depth as their model work demands it.
- PyTorch vs TensorFlow vs JAX?
- PyTorch dominates research and an increasing share of production. TensorFlow is still common in Google-adjacent ecosystems and some enterprise. JAX is gaining ground for high-performance research and Google internal work. For a new learner the only sensible pick is PyTorch — by a wide margin the largest community and the easiest path to current research code.
- How long to become an applied ML engineer?
- 12–24 months from a strong software-engineering baseline, longer from scratch. Plan on solid Python, a year of classical ML projects (regression, classification, clustering, time series), a deep-learning specialization, and one real production deployment with monitoring and drift detection. Kaggle competitions help build intuition but don't substitute for shipping models that someone depends on.