--- title: Machine Learning description: This discipline focuses on developing algorithms that allow computers to learn from and make predictions on data without explicit programming. Learners will understand how to apply supervised, unsupervised, and evaluation techniques to solve predictive problems. category: programming-tech subcategory: artificial-intelligence difficulty: beginner, intermediate, advanced url: /subject/machine-learning --- # Machine Learning This discipline focuses on developing algorithms that allow computers to learn from and make predictions on data without explicit programming. Learners will understand how to apply supervised, unsupervised, and evaluation techniques to solve predictive problems. ## Available Resources 3 Videos • 4 Books • 11 Courses • 9 Websites • 1 Papers ## Websites ### 1. Machine Learning for Beginners from Microsoft Microsoft's twelve-week, 26-lesson open curriculum teaching classical machine learning with Scikit-learn: regression, classification, clustering, NLP, and time series, each lesson paired with quizzes and assignments. Deep learning is deliberately left out. **Difficulty:** Intermediate | **Language:** English | **Price:** Free **Link:** https://lnkd.in/dBj3BAEY **Tags:** machine-learning, scikit-learn, curriculum, python, microsoft ### 2. Designing Machine Learning Systems Covers the full lifecycle of production machine learning: framing business problems, data engineering and sampling, feature engineering, model evaluation, deployment, monitoring for distribution shift, and the team structures that keep systems running reliably. **Difficulty:** Intermediate | **Language:** English | **Price:** Free **Link:** https://lnkd.in/dEx8sQJK **Tags:** mlops, production-ml, system-design, data-engineering, model-deployment ### 3. ML Cheatsheet Open-source reference documenting core machine learning concepts such as loss functions, activation functions, optimizers, regularization and common algorithms, with short definitions, formulas and Python snippets. Useful for checking a term or equation while working through a course. **Difficulty:** Beginner | **Price:** Free **Link:** https://ml-cheatsheet.readthedocs.io/ **Tags:** reference, glossary, loss-functions, optimization, algorithms ### 4. Distill.pub Peer-reviewed web journal of machine learning explanations, publishing interactive articles on topics like feature visualisation, attention and neural network interpretability. Archive remains readable, though the journal went on indefinite hiatus in 2021 and no longer publishes. **Difficulty:** Intermediate | **Price:** Free **Link:** https://distill.pub/ **Tags:** deep-learning, neural-networks, interpretability, interactive-visualization, machine-learning ### 5. Scikit-learn Official Documentation Official user guide, API reference and worked examples for scikit-learn, the standard Python machine learning library. Covers classification, regression, clustering, dimensionality reduction, pipelines, preprocessing and model selection, so readers can build, tune and evaluate models with cross-validation. **Difficulty:** Intermediate | **Language:** English | **Price:** Free **Link:** https://scikit-learn.org/stable/documentation.html **Tags:** scikit-learn, python, machine-learning, model-evaluation, supervised-learning ### 6. scikit-learn.org Home of the scikit-learn Python library, with an API reference for every estimator and a user guide explaining the statistical reasoning behind each method. A gallery of runnable examples covers preprocessing, model selection, cross-validation, and pipeline construction. **Difficulty:** Intermediate | **Language:** English | **Price:** Free **Link:** https://scikit-learn.org **Tags:** websites, technology-computer-science, data-science--ai ### 7. deeplearning.ai deeplearning.ai is an education platform focused on making AI and deep learning accessible through practical online courses and programs, including the Deep Learning Specialization and AI for Everyone. It offers structured curricula with video lessons, labs, assignments, and certificates covering neural networks, deep learning, TensorFlow, NLP, and broader AI topics. **Difficulty:** Intermediate | **Language:** English | **Price:** Free **Link:** https://deeplearning.ai **Tags:** websites, technology-computer-science, ai-ml ### 8. machinelearningmastery.com Machine Learning Mastery is a practical, hands-on tutorial site by Jason Brownlee that teaches machine learning and deep learning through step-by-step, code-focused guides. It features Python-based tutorials with project-based examples (scikit-learn, Keras) covering model building, evaluation, and deployment. **Difficulty:** Intermediate | **Language:** English | **Price:** Free **Link:** https://machinelearningmastery.com **Tags:** websites, technology-computer-science, ai-ml ### 9. mlcourse.ai MLCourse.ai is a free online course that teaches machine learning basics through structured lessons and practical labs. It provides theory, Python/Jupyter notebooks, and graded assignments to practice core ML concepts and algorithms. **Difficulty:** Intermediate | **Language:** English | **Price:** Free **Link:** https://mlcourse.ai **Tags:** websites, technology-computer-science, data-science--ai ## Courses ### 1. Matrix Calculus for Machine Learning and Beyond (MIT 18.S096) **Author:** Alan Edelman, Steven G. Johnson Extends calculus to matrices and general vector spaces: derivatives as linear operators, Jacobians, derivatives of matrix factorizations, adjoint methods and automatic differentiation. 17 lecture videos, lecture notes, and problem sets with solutions teach learners to derive and compute gradients for large-scale optimization. **Difficulty:** Intermediate | **Price:** Free **Link:** https://ocw.mit.edu/courses/18-s096-matrix-calculus-for-machine-learning-and-beyond-january-iap-2023/ **Tags:** matrix-calculus, automatic-differentiation, jacobians, adjoint-methods, gradient-based-optimization ### 2. Matrix Methods in Data Analysis, Signal Processing, and Machine Learning (MIT 18.065) **Author:** Gilbert Strang Linear algebra for data science and deep learning: singular value decomposition, low-rank approximation, least squares, PCA, randomized linear algebra, gradient descent and the structure of neural networks. Includes 37 lecture videos and problem sets. Afterwards you can recognise the matrix computations inside modern machine learning. **Difficulty:** Intermediate | **Price:** Free **Link:** https://ocw.mit.edu/courses/18-065-matrix-methods-in-data-analysis-signal-processing-and-machine-learning-spring-2018/ **Tags:** singular-value-decomposition, matrix-factorization, principal-component-analysis, optimization, deep-learning ### 3. Machine Learning Course - CS 156 Master theoretical machine learning with Yaser Abu-Mostafa's renowned "Learning From Data" course. Explore fundamental concepts and algorithms. **Difficulty:** Advanced | **Price:** Free **Link:** https://www.youtube.com/playlist?list=PLD63A284B7615313A **Tags:** machine-learning, statistical-learning-theory, generalization, caltech ### 4. Machine Learning Crash Course Google's free introductory course on machine learning, combining short video lessons, interactive visualizations, and hands-on Colab exercises. Covers linear and logistic regression, loss functions, gradient descent, overfitting, feature engineering, neural networks, embeddings, and fairness considerations for production models. **Difficulty:** Beginner | **Price:** Free **Link:** https://developers.google.com/machine-learning/crash-course **Tags:** fundamentals, supervised-learning, tensorflow, interactive-exercises, free-course ### 5. Practical Deep Learning for Coders fast.ai's free course teaching deep learning top-down: you train working image, text, and tabular models in the first lessons, then work back to the underlying mechanics. Assumes about a year of coding experience, uses PyTorch and the fastai library. **Difficulty:** Beginner | **Price:** Free **Link:** https://course.fast.ai/ **Tags:** deep-learning, pytorch, fastai, computer-vision, top-down-teaching ### 6. Machine Learning In the first course of the Machine Learning Specialization, you will: • Build machine learning models in Python using popular machine learning libraries NumPy and scikit-learn. • Build and train supervised machine learning models for prediction and binary classification tasks, including linear regression and logistic regression The Machine Learning Specialization is a foundational online program created in collaboration between DeepLearning.AI and Stanford Online. In this beginner-friendly program, you will learn the fundamentals of machine learning and how to use these techniques to build real-world AI applications. This Specialization is taught by Andrew Ng, an AI visionary who has led critical research at Stanford University and groundbreaking work at Google Brain, Baidu, and Landing.AI to advance the AI field. This 3-course Specialization is an updated and expanded version of Andrew’s pioneering Machine Learning course, rated 4.9 out of 5 and taken by over 4.8 million learners since it launched in 2012. It provides a broad introduction to modern machine learning, including supervised learning (multiple linear regression, logistic regression, neural networks, and decision trees), unsupervised learning (clustering, dimensionality reduction, recommender systems), and some of the best practices used in Silicon Valley for artificial intelligence and machine learning innovation (evaluating and tuning models, taking a data-centric approach to improving performance, and more.) By the end of this Specialization, you will have mastered key concepts and gained the practical know-how to quickly and powerfully apply machine learning to challenging real-world problems. If you’re looking to break into AI or build a career in machine learning, the new Machine Learning Specialization is the best place to start. **Difficulty:** Beginner | **Price:** Free **Link:** https://www.coursera.org/learn/machine-learning **Tags:** supervised-learning, linear-regression, logistic-regression, python, scikit-learn ### 7. Introduction to Deep Learning (MIT 6.S191) **Author:** Alexander Amini, Ava Amini Annual one-week bootcamp on deep learning: neural network foundations, sequence models and transformers, convolutional networks for vision, generative models, and reinforcement learning. Recorded lectures, slides and software labs in TensorFlow and PyTorch let learners build and train working models. **Difficulty:** Intermediate | **Language:** English | **Price:** Free **Link:** https://introtodeeplearning.com/ **Tags:** deep-learning, neural-networks, transformers, convolutional-neural-networks, generative-models ### 8. Andrew Ng's Deep Learning Specialization This specialization is one of the most popular and comprehensive introductions to deep learning. It covers foundational concepts of neural networks, including how to build and train them, and delves into convolutional networks, recurrent networks, and more. While the certificate requires payment, auditing the courses (watching lectures, accessing many materials) is typically free. **Difficulty:** Beginner | **Language:** English | **Price:** Free **Link:** https://www.coursera.org/specializations/deep-learning **Tags:** deep-learning, neural-networks, convolutional-neural-networks, recurrent-neural-networks, hyperparameter-tuning ### 9. Machine Learning with Python Python is a core skill in machine learning, and this course equips you with the tools to apply it effectively. You’ll learn key ML concepts, build models with scikit-learn, and gain hands-on experience using Jupyter Notebooks. Start with regression techniques like linear, multiple linear, polynomial, and logistic regression. Then move into supervised models such as decision trees, K-Nearest Neighbors, and support vector machines. You’ll also explore unsupervised learning, including clustering methods and dimensionality reduction with PCA, t-SNE, and UMAP. Through real-world labs, you’ll practice model evaluation, cross-validation, regularization, and pipeline optimization. A final project on rainfall prediction and a course-wide exam will help you apply and reinforce your skills. Enroll now to start building machine learning models with confidence using Python. **Difficulty:** Beginner | **Language:** English | **Duration:** 5-6 weeks of study, 3-6 hours per week | **Price:** Free **Link:** https://www.coursera.org/learn/machine-learning-with-python **Tags:** courses, technology-computer-science, ai-ml ### 10. Supervised A University System of Maryland course on edX covering the main supervised learning algorithms: k-nearest neighbours, support vector machines, decision trees, random forests, and regression. Models are built in Python with scikit-learn, with attention to evaluation and algorithm trade-offs. **Difficulty:** Beginner | **Language:** English | **Price:** Free **Link:** https://www.edx.org/learn/computer-science/university-system-of-maryland-supervised-learning-2 **Tags:** courses, technology-computer-science, data-science--ai ### 11. Advanced Machine Learning Columbia University's survey course on edX, presented with the underlying mathematics rather than library calls. Works through regression, maximum likelihood and Bayesian estimation, classification, kernel methods, support vector machines, boosting, clustering, expectation maximisation, hidden Markov models, and matrix factorisation. **Difficulty:** Intermediate | **Language:** English | **Price:** Free **Link:** https://www.edx.org/learn/machine-learning/columbia-university-machine-learning **Tags:** courses, technology-computer-science, ai-ml ## Videos ### 1. StatQuest: Linear Regression Clearly Explained Josh Starmer walks through fitting a line by least squares, then explains R-squared and the p-value for the fit using simple visuals. Good for building intuition before meeting the algebra of regression. **Difficulty:** Beginner | **Price:** Free **Link:** https://www.youtube.com/watch?v=nk2CQITm_eo **Tags:** linear-regression, r-squared, least-squares, statistics, statquest ### 2. StatQuest with Josh Starmer - Neural Networks videos Josh Starmer's StatQuest provides clear, concise, and often humorous explanations of statistical and machine learning concepts. His videos on neural networks break down complex ideas into easily digestible 'quests.' **Difficulty:** Beginner | **Language:** English | **Price:** Free **Link:** https://www.youtube.com/playlist?list=PLXe8IwAUB2B-Ews9ADFJBHE7nVAZAfV05 **Tags:** neural-networks, backpropagation, machine-learning, statistics, video-series ### 3. 3Blue1Brown - Neural Networks series Grant Sanderson's 3Blue1Brown channel offers visually intuitive and mathematically rigorous explanations of complex topics. His series on neural networks is particularly praised for making the core concepts, like backpropagation, understandable through animated visuals. **Difficulty:** Beginner | **Language:** English | **Price:** Free **Link:** https://www.youtube.com/playlist?list=PLW_xnxm7jEso **Tags:** neural-networks, backpropagation, gradient-descent, deep-learning, visual-explanation ## Podcasts ### 1. The TWIML AI Podcast **Author:** Sam Charrington Long-running interview podcast hosted by Sam Charrington in which ML and AI researchers and practitioners discuss their work on deep learning, natural language processing, neural networks and data science, helping listeners follow current research and how it is applied in industry. **Difficulty:** Intermediate | **Language:** en | **Price:** Free **Link:** https://twimlai.com **Tags:** machine-learning, artificial-intelligence, deep-learning, ai-research ### 2. ML Podcast **Author:** Jon Krohn Long-form interview podcast hosted by Jon Krohn, featuring practitioners and researchers in data science and machine learning. Episodes alternate between technical walkthroughs of models and tooling, and career discussions on breaking into and advancing within data roles. **Difficulty:** Beginner | **Language:** en | **Price:** Free **Link:** https://www.superdatascience.com/podcast **Tags:** podcast, interviews, data-science-careers, deep-learning, industry-trends ### 3. The Gradient **Author:** Daniel Bashir Interview podcast from The Gradient, an AI publication, with researchers and engineers working on machine learning. Conversations run long and stay technical, covering research agendas, the history behind current methods, and disagreements about where the field is heading. **Difficulty:** Beginner | **Language:** en | **Price:** Free **Link:** https://thegradientpub.substack.com/s/podcast **Tags:** podcast, interviews, ai-research, nlp, research-trends ### 4. Learning Machines 101 **Author:** Richard M. Golden Podcast by UT Dallas researcher Richard Golden introducing artificial intelligence and statistical machine learning. Episodes explain how learning algorithms work, from neural networks and anomaly detection to convergence guarantees and probability theory, building conceptual understanding of how machines learn from data. **Difficulty:** Intermediate | **Language:** en | **Price:** Free **Link:** https://www.learningmachines101.com **Tags:** machine-learning, artificial-intelligence, statistical-learning, neural-networks ## Books ### 1. Information Theory, Inference, and Learning Algorithms **Author:** David J. C. MacKay MacKay's Cambridge text develops entropy, source coding, channel capacity, Hamming and LDPC codes, then shows the same mathematics driving Bayesian inference, clustering and neural networks. MacKay refuses to separate coding from inference, so a learner leaves with one mental model instead of two disconnected ones. Full PDF is free from the author's site. Heavy exercise sets with worked solutions. **Difficulty:** Intermediate | **Language:** English | **Price:** Free **Link:** https://www.inference.org.uk/itprnn/book.html **Tags:** information-theory, bayesian-inference, error-correcting-codes, ldpc-codes, machine-learning ### 2. Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow (3rd Edition) **Author:** Aurelien Geron The standard reference for the TensorFlow/Keras side of deep learning, moving from scikit-learn regression and ensembles into Keras APIs, custom layers, tf.data, training loops, CNNs, transformers, and TFX deployment. Notebooks for every chapter are on GitHub. **Difficulty:** Intermediate | **Language:** English | **Price:** Paid **Link:** https://www.amazon.com/dp/1098125975?tag=edmonddante07-20 **Tags:** tensorflow, keras, scikit-learn, deep-learning, machine-learning ### 3. Neural Networks and Deep Learning **Author:** Michael Nielsen A foundational and widely acclaimed online book that explains neural networks and deep learning concepts from scratch, with clear explanations and interactive examples. It's an excellent resource for building a strong theoretical understanding. **Difficulty:** Beginner | **Language:** English | **Price:** Free **Link:** https://neuralnetworksanddeeplearning.com/ **Tags:** neural-networks, backpropagation, gradient-descent, deep-learning, free-textbook ### 4. Pattern Recognition and Machine Learning **Author:** Christopher M. Bishop Graduate-level text presenting machine learning from a Bayesian probabilistic viewpoint: linear models, kernel methods, graphical models, mixture models, EM, variational inference and sampling. Requires comfort with linear algebra and calculus, and rewards careful work through its exercises. **Difficulty:** Intermediate | **Language:** English | **Price:** Paid **Link:** https://www.amazon.com/dp/8132209060?tag=edmonddante07-20 **Tags:** books, technology-computer-science, ai-ml ## Papers ### 1. Deep Learning **Author:** Yann LeCun, Yoshua Bengio, Geoffrey Hinton A seminal review paper by three pioneers of deep learning, providing an overview of the field, its history, key concepts, and future directions. It's an excellent read for understanding the landscape of deep learning. **Difficulty:** Advanced | **Language:** English | **Price:** Free **Link:** https://www.nature.com/articles/nature14539 **Tags:** deep-learning, neural-networks, representation-learning, backpropagation, review-paper --- *This content is part of Dantes.io - Your Treasure Map to Knowledge* *Curated by humans at Dantes.io. Personal study use welcome; republishing this curation requires permission (team@dantes.io).* View this page online: https://dantes.io/subject/machine-learning