Advanced Machine Learning Specialization

Offered By National Research University Higher School of Economics

Deep Dive Into The Modern AI Techniques. You will teach the computer to see, draw, read, talk, play games and solve industry problems.

About this Specialization

This specialization gives an introduction to deep learning, reinforcement learning, natural language understanding, computer vision, and Bayesian methods. Top Kaggle machine learning practitioners and CERN scientists will share their experience of solving real-world problems and help you to fill the gaps between theory and practice. Upon completion of 7 courses, you will be able to apply modern machine learning methods in enterprise and understand the caveats of real-world data and settings.

Machine Learning Offered By Stanford University

About this Course

Machine learning is the science of getting computers to act without being explicitly programmed. In the past decade, machine learning has given us self-driving cars, practical speech recognition, effective web search, and a vastly improved understanding of the human genome. Machine learning is so pervasive today that you probably use it dozens of times a day without knowing it. Many researchers also think it is the best way to make progress towards human-level AI. In this class, you will learn about the most effective machine learning techniques, and gain practice implementing them and getting them to work for yourself. More importantly, you'll learn about not only the theoretical underpinnings of learning but also gain the practical know-how needed to quickly and powerfully apply these techniques to new problems. Finally, you'll learn about some of Silicon Valley's best practices in innovation as it pertains to machine learning and AI.

This course provides a broad introduction to machine learning, data mining, and statistical pattern recognition. Topics include: (i) Supervised learning (parametric/non-parametric algorithms, support vector machines, kernels, neural networks). (ii) Unsupervised learning (clustering, dimensionality reduction, recommender systems, deep learning). (iii) Best practices in machine learning (bias/variance theory; innovation process in machine learning and AI). The course will also draw from numerous case studies and applications so that you'll also learn how to apply learning algorithms to building smart robots (perception, control), text understanding (web search, anti-spam), computer vision, medical informatics, audio, database mining, and other areas.

Getting Started with AWS Machine Learning Offered By Amazon Web Services

About this Course

Machine learning (ML) is one of the fastest-growing areas in technology and a highly sought after skillset in today's job market. The World Economic Forum states the growth of artificial intelligence (AI) could create 58 million net new jobs in the next few years, yet it's estimated that currently there are 300,000 AI engineers worldwide, but millions are needed. This means there is a unique and immediate opportunity for you to get started with learning the essential ML concepts that are used to build AI applications - no matter what your skill levels are. Learning the foundations of ML now will help you keep pace with this growth, expand your skills and even help advance your career.

This course will teach you how to get started with AWS Machine Learning. Key topics include Machine Learning on AWS, Computer Vision on AWS, and Natural Language Processing (NLP) on AWS. Each topic consists of several modules deep-diving into a variety of ML concepts, AWS services as well as insights from experts to put the concepts into practice.

Machine Learning with TensorFlow on Google Cloud Platform Specialization Offered By Google Cloud

About this Specialization

What is machine learning, and what kinds of problems can it solve? What are the five phases of converting a candidate use case to be driven by machine learning, and why is it important that the phases not be skipped? Why are neural networks so popular now? How can you set up a supervised learning problem and find a good, generalizable solution using gradient descent and a thoughtful way of creating datasets? Learn how to write distributed machine learning models that scale in Tensorflow, scale out the training of those models. and offer high-performance predictions. Convert raw data to features in a way that allows ML to learn important characteristics from the data and bring human insight to bear on the problem. Finally, learn how to incorporate the right mix of parameters that yields accurate, generalized models and knowledge of the theory to solve specific types of ML problems. You will experiment with end-to-end ML, starting from building an ML-focused strategy and progressing into model training, optimization, and productionalization with hands-on labs using Google Cloud Platform.

Mathematics for Machine Learning Specialization Offered By Imperial College London

Mathematics for Machine Learning. Learn about the prerequisite mathematics for applications in data science and machine learning

About this Specialization

For a lot of higher-level courses in Machine Learning and Data Science, you find you need to freshen up on the basics in mathematics - stuff you may have studied before in school or university, but which was taught in another context, or not very intuitively, such that you struggle to relate it to how it's used in Computer Science. This specialization aims to bridge that gap, getting you up to speed in the underlying mathematics, building an intuitive understanding, and relating it to Machine Learning and Data Science.

In the first course on Linear Algebra, we look at what linear algebra is and how it relates to data. Then we look through what vectors and matrices are and how to work with them.

The second course, Multivariate Calculus, builds on this to look at how to optimize fitting functions to get good fits to data. It starts from introductory calculus and then uses the matrices and vectors from the first course to look at data fitting.

The third course, Dimensionality Reduction with Principal Component Analysis, uses the mathematics from the first two courses to compress high-dimensional data. This course is of intermediate difficulty and will require Python and numpy knowledge.

At the end of this specialization, you will have gained the prerequisite mathematical knowledge to continue your journey and take more advanced courses in machine learning.

Source: HOB