Created by Lazy Programmer Inc. English [Auto-generated] Created by Lazy Programmer Inc. This course is the next logical step in my deep learning, data science, and machine learning series. I've done a lot of courses about deep learning, and I just released a course about unsupervised learning, where I talked about clustering and density estimation. So what do you get when you put these 2 together? In these course we'll start with some very basic stuff - principal components analysis (PCA), and a popular nonlinear dimensionality reduction technique known as t-SNE (t-distributed stochastic neighbor embedding).

Python instructors on Udemy specialize in everything from software development to data analysis, and are known for their effective, friendly instruction for students of all levels. Machine learning is constantly being applied to new industries and new problems. Whether you're a marketer, video game designer, or programmer, this course is here to help you apply machine learning to your work. Welcome to the "Python Programming: Machine Learning, Deep Learning Python" course. In this course, we will learn what is Deep Learning and how does it work.

Deanna provides unbiased, AI-generated trading alerts, providing you with a virtual trading partner that works 24/7. We'll introduce you to the team of experts that developed and supported Deanna, detail how Artificial Intelligence is fast becoming the preferred support tool to anyone investing and trading digital currencies and demonstrate how Deanna has performed recently in these volatile markets. Everyone attending the webinar will receive 3-months at a 50% discounted price to Deanna's Pro-Plan. We'll download a version of the Deanna mobile app at the webinar, discuss how to use the market indicators and alerts that Deanna provides intra-day. You'll respond to your questions and comments.

If you know the basics of Python and you have a drive for deep learning, this course is designed for you. Python is famed as one of the best programming languages for its flexibility. It works in almost all fields, from web development to developing financial applications. However, it's no secret that Python's best application is in deep learning and artificial intelligence tasks. While Python makes deep learning easy, it will still be quite frustrating for someone with no knowledge of how machine learning works in the first place. If you know the basics of Python and you have a drive for deep learning, this course is designed for you.

Python based YOLO Object Detection using Pre-trained Dataset Models as well as Custom Trained Dataset Models. Python based YOLO Object Detection using Pre-trained Dataset Models as well as Custom Trained Dataset Models. This is the fourth course from my Computer Vision series. As you know Object Detection is the most used applications of Computer Vision, in which the computer will be able to recognize and classify objects inside an image. This course is equally divided into two halves.

The Machine Learning Crash Course with TensorFlow APIs is a self-study guide for aspiring machine learning practitioners. Learn how to build Machine Learning projects in this TensorFlow Course created by The Click Reader. In this course, you will be learning about Scalar as well as Tensors and how to create them using TensorFlow. You will also be learning how to perform various kinds of Tensor operations for manipulating and changing tensor values. You will be learning how to create a Linear Regression model from scratch using TensorFlow.

Become a real time professional Data Scientist by learning the intuition and Math Behind each Machine Learning Model. In this course, you are going to learn all types of Machine Learning Models implemented in R Programming Language. The Math behind every model is very important. Without it, you can never become a Good Data Scientist. That is the reason, I have covered the Math behind every model in the intuition part of each Model.

Added: Object-Oriented Programming (OOP) for complete Beginners: with real-world examples and in a way that everyone understands OOP! This is the first-ever comprehensive Python Course for Business and Finance Professionals. You will learn and master Python from Zero and the full Python Data Science Stack with real Examples and Projects taken from the Business and Finance world. You will understand and master all required theoretical concepts behind the projects and the code from scratch. Important: the quality Benchmark for the theory part is the CFA (Chartered Financial Analyst) Curriculum.

Social learning algorithms provide models for the formation of opinions over social networks resulting from local reasoning and peer-to-peer exchanges. Interactions occur over an underlying graph topology, which describes the flow of information and relative influence between pairs of agents. For a given graph topology, these algorithms allow for the prediction of formed opinions. In this work, we study the inverse problem. Given a social learning model and observations of the evolution of beliefs over time, we aim at identifying the underlying graph topology. The learned graph allows for the inference of pairwise influence between agents, the overall influence agents have over the behavior of the network, as well as the flow of information through the social network. The proposed algorithm is online in nature and can adapt dynamically to changes in the graph topology or the true hypothesis.

In this work we study statistical properties of graph-based clustering algorithms that rely on the optimization of balanced graph cuts, the main example being the optimization of Cheeger cuts. We consider proximity graphs built from data sampled from an underlying distribution supported on a generic smooth compact manifold $M$. In this setting, we obtain high probability convergence rates for both the Cheeger constant and the associated Cheeger cuts towards their continuum counterparts. The key technical tools are careful estimates of interpolation operators which lift empirical Cheeger cuts to the continuum, as well as continuum stability estimates for isoperimetric problems. To our knowledge the quantitative estimates obtained here are the first of their kind.