Want to know how Machine Learning algorithms work and how people apply it to solve data science problems? You are looking at right course! This course has been created, designed and assembled by professional Data Scientists who have worked in this field for nearly a decade. We can help you understand the complex machine learning algorithms while keeping you grounded to the implementation on real business and data science problems. We will let you feel the water and coach you to become a full swimmer in the realm of data science and Machine Learning.

You learn important information on a daily basis, either to gain knowledge or for school, but you are losing a lot of this useful information because you didn't learn it properly in the first place It doesn't have to stay this way. My complete Learning Strategy & Speed Reading course will show you the exact techniques and strategies you need to learn the right way and gain knowledge or remember important information easily. For less than a movie ticket, you will get over 4 hours of video lectures and the freedom to ask me any questions regarding the course as you go through it. What Is In This Course? Your Learning and Reading Sessions Will Never Be The Same.

Complex statistics in Machine Learning worry a lot of developers. Knowing statistics helps you build strong Machine Learning models that are optimized for a given problem statement. This video will teach you all it takes to perform complex statistical computations required for Machine Learning. Understand the real-world examples that discuss the statistical side of Machine Learning and familiarize yourself with it. We will discuss the application of frequently used algorithms on various domain problems, using both Python and R programming.

About this course: How can we create agile micro aerial vehicles that are able to operate autonomously in cluttered indoor and outdoor environments? You will gain an introduction to the mechanics of flight and the design of quadrotor flying robots and will be able to develop dynamic models, derive controllers, and synthesize planners for operating in three dimensional environments. You will be exposed to the challenges of using noisy sensors for localization and maneuvering in complex, three-dimensional environments. Finally, you will gain insights through seeing real world examples of the possible applications and challenges for the rapidly-growing drone industry. Mathematical prerequisites: Students taking this course are expected to have some familiarity with linear algebra, single variable calculus, and differential equations.

A central question in statistical learning is to design algorithms that not only perform well on training data, but also generalize to new and unseen data. In this paper, we tackle this question by formulating a distributionally robust stochastic optimization (DRSO) problem, which seeks a solution that minimizes the worst-case expected loss over a family of distributions that are close to the empirical distribution in Wasserstein distances. We establish a connection between such Wasserstein DRSO and regularization. More precisely, we identify a broad class of loss functions, for which the Wasserstein DRSO is asymptotically equivalent to a regularization problem with a gradient-norm penalty. Such relation provides new interpretations for problems involving regularization, including a great number of statistical learning problems and discrete choice models (e.g. multinomial logit). The connection suggests a principled way to regularize high-dimensional, non-convex problems. This is demonstrated through the training of Wasserstein generative adversarial networks in deep learning.

Kernel-based methods enjoy powerful generalization capabilities in handling a variety of learning tasks. When such methods are provided with sufficient training data, broadly-applicable classes of nonlinear functions can be approximated with desired accuracy. Nevertheless, inherent to the nonparametric nature of kernel-based estimators are computational and memory requirements that become prohibitive with large-scale datasets. In response to this formidable challenge, the present work puts forward a low-rank, kernel-based, feature extraction approach that is particularly tailored for online operation, where data streams need not be stored in memory. A novel generative model is introduced to approximate high-dimensional (possibly infinite) features via a low-rank nonlinear subspace, the learning of which leads to a direct kernel function approximation. Offline and online solvers are developed for the subspace learning task, along with affordable versions, in which the number of stored data vectors is confined to a predefined budget. Analytical results provide performance bounds on how well the kernel matrix as well as kernel-based classification and regression tasks can be approximated by leveraging budgeted online subspace learning and feature extraction schemes. Tests on synthetic and real datasets demonstrate and benchmark the efficiency of the proposed method when linear classification and regression is applied to the extracted features.

In this lecture, we will learn how to predict an outcome that can have multiple values. We are going to use the wine quality dataset and predict the quality of wine based on various characteristics or physiochemical properties of wine, that may affect its quality, such s the acidity, citric acid, residual sugar in it, density and so on.

About this course: This course will teach you the "magic" of getting deep learning to work well. Rather than the deep learning process being a black box, you will understand what drives performance, and be able to more systematically get good results. You will also learn TensorFlow. This is the second course of the Deep Learning Specialization.

The winning team was comprised of four students: Michael Lopez-Brau and Stefan Uddenberg, both doctoral students in Yale's psychology department; Alex Cui, an undergraduate who studies machine learning at the California Institute of Technology; and Jeff An, who studies computer science at the University of Waterloo and business at Wilfrid Laurier University in Ontario.

Python is a dynamic programming language used in a wide range of domains by programmers who find it simple yet powerful. In today's world, everyone wants to gain insights from the deluge of data coming their way. Data mining provides a way of finding these insights, and Python is one of the most popular languages for data mining, providing both power and flexibility in analysis. Python has become the language of choice for data scientists for data analysis, visualization, and machine learning. In this course, you will discover the key concepts of data mining and learn how to apply different data mining techniques to find the valuable insights hidden in real-world data.