Are YOU looking For Free Machine Learning Online Courses and Tutorials?
As we as a whole finally realized that machine learning or artificial intelligence has progressively increased greater fame in the recent years and still keeps on doing as such.
As at the exact instant Big Data is the present trend in the tech business, machine learning ends up being amazingly effective with regards to making expectations or ascertained recommendations that depend on a lot of data.
The significance it conveys alongside the universe of surprise it conveys is outstanding and caught on. Be that as it may, where people for the most part need or stop at is one fundamental inquiry:
So if an individual needs to take in more about machine learning, how would you begin and from where?
Below are the Top 10 Best machine learning courses for FREE
Description: Use this platform to learn ML, explore topics and build a portfolio to showcase your impact.
Tools to enable this will be launching January 2020.
A Machine Learning Course with Python – Machine Learning Mindset
Description: Machine Learning, as a tool for Artificial Intelligence, is one of the most widely adopted scientific fields. A considerable amount of literature has been published on Machine Learning. The purpose of this project is to provide the most important aspects of Machine Learning by presenting a series of simple and yet comprehensive tutorials using Python.
In this project, we built our tutorials using many different well-known Machine Learning frameworks such as Scikit-learn. In this project you will learn:
- What is the definition of Machine Learning?
- When it started and what is the trending evolution?
- What are the Machine Learning categories and subcategories?
- What are the mostly used Machine Learning algorithms and how to implement them?
Machine Learning for Intelligent Systems – Cornell University
Description: The goal of this course is to give an introduction to the field of machine learning. The course will teach you basic skills to decide which learning algorithm to use for what problem, code up your own learning algorithm and evaluate and debug it.
The field of machine learning is concerned with the question of how to construct computer programs that automatically improve with experience. Recently, many successful machine learning applications have been developed, ranging from data-mining programs that learn to detect fraudulent credit card transactions, to information-filtering systems that learn users’ reading preferences, to autonomous vehicles that learn to drive.
There have also been important advances in the theory and algorithms that form the foundation of this field. This course will provide a broad introduction to the field of machine learning. Prerequisites: CSE 241 and sufficient mathematical maturity (Matrix Algebra, probability theory / statistics, multivariate calculus). The instructor will hold a take-home placement exam (on basic mathematical knowledge) that is due on January 30th.
Introduction to Machine Learning in Python – Springboard
Already Enrolled 20,413 Learners
Description: Machine learning is one of the hottest new technologies to emerge in the last decade, transforming fields from consumer electronics and healthcare to retail. This has led to intense curiosity about the industry among many students and working professionals.
If you’re a tech professional—such as a software developer, business analyst, or even a product manager—you might be curious about how machine learning can change the way you work and take your career to the next level. However, as a busy professional, you’re probably also looking for a way to get a solid understanding of machine learning that’s not only rigorous and practical, but also concise and fast. This machine learning tutorial will help you achieve your goals.
University at Buffalo
Description: Machine learning is an exciting topic about designing machines that can learn from examples. The course covers the necessary theory, principles and algorithms for machine learning. The methods are based on statistics and probability– which have now become essential to designing systems exhibiting artificial intelligence.
The course is taught during the Fall semester, succeeded by a course focusing on Probabilistic Graphical Models in the Spring semester. A stand-alone course on Deep Learning is offered in the Fall semester.
Deep Learning – University at Buffalo
Instructor – Srihari
Description: Deep Learning algorithms learn multi-level representations of data, with each level explaining the data in a hierarchical manner. Such algorithms have been effective at uncovering underlying structure in data, e.g., features to discriminate between classes. They have been successful in many artificial intelligence problems including image classification, speech recognition and natural language processing.
The course, which will be taught through lectures and projects, will cover the underlying theory, the range of applications to which it has been applied, and learning from very large data sets. The course will cover connectionist architectures commonly associated with deep learning, e.g., basic neural networks, convolutional neural networks and recurrent neural networks. Methods to train and optimize the architectures and methods to perform effective inference with them, will be the main focus. Students will be encouraged to use open source software libraries such as Tensorflow.
Instructor Prof. Kosta Derpanis
Description: Computer Vision is broadly defined as the study of recovering useful properties of the world from one or more images. In recent years, Deep Learning has emerged as a powerful tool for addressing computer vision tasks. This course will cover a range of foundational topics at the intersection of Deep Learning and Computer Vision.
Interpretability and Explainability in Machine Learning – Harvard University
Description: As machine learning models are increasingly being employed to aid decision makers in high-stakes settings such as healthcare and criminal justice, it is important to ensure that the decision makers (end users) correctly understand and consequently trust the functionality of these models. This graduate level course aims to familiarize students with the recent advances in the emerging field of interpretable and explainable ML.
In this course, we will review seminal position papers of the field, understand the notion of model interpretability and explainability, discuss in detail different classes of interpretable models (e.g., prototype based approaches, sparse linear models, rule based techniques, generalized additive models), post-hoc explanations (black-box explanations including counterfactual explanations and saliency maps), and explore the connections between interpretability and causality, debugging, and fairness.
The course will also emphasize on various applications which can immensely benefit from model interpretability including criminal justice and healthcare.
Instructor – Ben-Gurion
Description: The course is an introduction to Natural Language Processing. The main objective of the course is to learn how to develop practical computer systems capable of performing intelligent tasks on natural language: analyze, understand and generate written text.
This task requires learning material from several fields: linguistics, machine learning and statistical analysis, and core natural language techniques.
Probabilistic Graphical Models Course –University at Buffalo
Instructor – Srihari
Description: The first wave of Artificial Intelligence, known as knowledge-based systems, was based on pre-programmed logic. The second wave, which is based on deep learning, has made spectacular advances for sensing and perception. The next advance will be based on probabilistic reasoning– so as to take uncertainty into account as well as to address current liitations of deep learning, e.g., provide explanations of decisions, ethical AI, etc.
Probabilistic graphical models are graphical representations of probability distributions. Such models are versatile in representing complex probability distributions encountered in many scientific and engineering applications. They have now become essential to designing systems exhibiting advanced artificial intelligence, such as generative models for deep learning.
The course covers theory, principles and algorithms associated with probabilistic graphical models. Both directed graphical models (Bayesian networks) and undirected graphical models (Markov networks) are discussed covering representation, inference and learning.
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