The vacations are upon us, and when you’re on the lookout for one thing to occupy some down time in between occasions with household, buddies, and coworkers, why not achieve this with high quality, free on-line programs?
This can be a assortment of free, high quality on-line machine studying programs, from some esteemed universities in addition to different on-line shops. Whether or not you’re on the lookout for introductory or extra superior subjects, theoretical or sensible, or common or specified subjects, the programs right here ought to assist scratch that year-end studying itch. Check out the programs beneath and get studying one thing new right this moment.
Remember to take a look at the associated posts on the backside for extra posts with free machine studying programs.
1. Machine Learning in Python
Machine studying is without doubt one of the hottest new applied sciences to emerge within the final decade, remodeling fields from shopper electronics and healthcare to retail. This has led to intense curiosity concerning the trade amongst many college students and dealing professionals.
When you’re a tech skilled—similar to a software program developer, enterprise analyst, or perhaps a product supervisor—you is perhaps inquisitive about how machine studying can change the way in which you’re employed and take your profession to the subsequent stage. Nevertheless, as a busy skilled, you’re most likely additionally on the lookout for a approach to get a strong understanding of machine studying that’s not solely rigorous and sensible, but in addition concise and quick. This machine studying tutorial will make it easier to obtain your targets.
2. Introduction to Machine Learning
College at Buffalo
Machine studying is an thrilling subject about designing machines that may study from examples. The course covers the mandatory idea, rules and algorithms for machine studying. The strategies are primarily based on statistics and probability– which have now develop into important to designing methods exhibiting synthetic intelligence.
Reference textbooks for various components of the course are “Pattern Recognition and Machine Learning” by Chris Bishop (Springer 2006) and “Probabilistic Graphical Models” by Daphne Koller and Nir Friedman (MIT Press 2009) and “Deep Learning” by Goodfellow, Bengio and Courville (MIT Press 2016).
3. Practical AI: A practical approach to machine learning
4. A Machine Learning Course with Python
Machine Studying Mindset
Machine Studying, as a instrument for Synthetic Intelligence, is without doubt one of the most generally adopted scientific fields. A substantial quantity of literature has been revealed on Machine Studying. The aim of this undertaking is to supply an important points of Machine Studying by presenting a sequence of straightforward and but complete tutorials utilizing Python. On this undertaking, we constructed our tutorials utilizing many alternative well-known Machine Studying frameworks similar to Scikit-learn.
5. Machine Learning for Intelligent Systems
The sector of machine studying is anxious with the query of how one can assemble pc packages that robotically enhance with expertise. Just lately, many profitable machine studying functions have been developed, starting from data-mining packages that study to detect fraudulent bank card transactions, to information-filtering methods that study customers’ studying preferences, to autonomous automobiles that study to drive. There have additionally been essential advances within the idea and algorithms that kind the muse of this area. This course will present a broad introduction to the sector of machine studying. Conditions: CSE 241 and ample mathematical maturity (Matrix Algebra, chance idea / statistics, multivariate calculus). The trainer will maintain a take-home placement examination (on primary mathematical information) that’s due on January 30th.
6. Deep Learning
College at Buffalo
Deep Studying algorithms study multi-level representations of knowledge, with every stage explaining the information in a hierarchical method. Such algorithms have been efficient at uncovering underlying construction in information, e.g., options to discriminate between courses. They’ve been profitable in lots of synthetic intelligence issues together with picture classification, speech recognition and pure language processing. The course, which will probably be taught by way of lectures and tasks, will cowl the underlying idea, the vary of functions to which it has been utilized, and studying from very massive information units. The course will cowl connectionist architectures generally related to deep studying, e.g., primary neural networks, convolutional neural networks and recurrent neural networks. Strategies to coach and optimize the architectures and strategies to carry out efficient inference with them, would be the fundamental focus. College students will probably be inspired to make use of open supply software program libraries similar to Tensorflow.
7. Deep Learning in Computer Vision
Laptop Imaginative and prescient is broadly outlined because the examine of recovering helpful properties of the world from a number of photos. Lately, Deep Studying has emerged as a strong instrument for addressing pc imaginative and prescient duties. This course will cowl a spread of foundational subjects on the intersection of Deep Studying and Laptop Imaginative and prescient.
8. Interpretability and Explainability in Machine Learning
As machine studying fashions are more and more being employed to help choice makers in high-stakes settings similar to healthcare and legal justice, you will need to make sure that the choice makers (finish customers) appropriately perceive and consequently belief the performance of those fashions. This graduate stage course goals to familiarize college students with the current advances within the rising area of interpretable and explainable ML. On this course, we’ll overview seminal place papers of the sector, perceive the notion of mannequin interpretability and explainability, focus on intimately completely different courses of interpretable fashions (e.g., prototype primarily based approaches, sparse linear fashions, rule primarily based strategies, generalized additive fashions), post-hoc explanations (black-box explanations together with counterfactual explanations and saliency maps), and discover the connections between interpretability and causality, debugging, and equity. The course may also emphasize on numerous functions which may immensely profit from mannequin interpretability together with legal justice and healthcare.
Editor’s word: course in progress at time of writing, not all sources accessible but on-line.
9. Topics in Natural Language Processing
Ben-Gurion College of the Negev
The course is an introduction to Pure Language Processing. The principle goal of the course is to discover ways to develop sensible pc methods able to performing clever duties on pure language: analyze, perceive and generate written textual content. This process requires studying materials from a number of fields: linguistics, machine studying and statistical evaluation, and core pure language strategies.
10. Probabilistic Graphical Models Course
College at Buffalo
Probabilistic graphical fashions are graphical representations of chance distributions. Such fashions are versatile in representing complicated chance distributions encountered in lots of scientific and engineering functions. They’ve now develop into important to designing methods exhibiting superior synthetic intelligence, similar to generative fashions for deep studying.
The course covers idea, rules and algorithms related to probabilistic graphical fashions. Each directed graphical fashions (Bayesian networks) and undirected graphical fashions (Markov networks) are mentioned overlaying illustration, inference and studying.