Applying Machine Learning (ML) to solve real problems accurately and robustly requires more than just training the latest ML model. First, you will learn practical techniques to deal with data. This matters since real data is often not independently and identically distributed. It includes detecting covariate, concept, and label shifts, and modeling dependent random variables such as the ones in time series and graphs. Next, you will learn how to efficiently train ML models, such as tuning hyper-parameters, model combination, and transfer learning. Last, you will learn about fairness and model explainability, and how to efficiently deploy models.

I've been an entrepreneur for 15 years, have coached 1,000 entrepreneurs in person, taught 100,000 students. My work has had a positive impact on millions of entrepreneurs, improving lives in every country of the world, creating 6 and 7-figure businesses in the process, and I would love to help you next. I've helped many aspiring YouTubers and course creators improve their video production quality and I look forward to helping you.

Link: Machine Learning A-Z: Hands-On Python & R In Data Science udemy free Machine Learning A-Z: Hands-On Python & R In Data Science Udemy Free Download Learn to create Machine Learning Algorithms in ...BESTSELLER 4.5 (84,517 ratings) 421,098 students enrolled Created by Kirill Eremenko, Hadelin de Ponteves, SuperDataScience Team, SuperDataScience Support What you'll learn Master Machine Learning on Python & R Have a great intuition of many Machine Learning models Make accurate predictions Make powerful analysis Make robust Machine Learning models Create strong added value to your business Use Machine Learning for personal purpose Handle specific topics like Reinforcement Learning, NLP and Deep Learning Handle advanced techniques like Dimensionality Reduction Know which Machine Learning model to choose for each type of problem Build an army of powerful Machine Learning models and know how to combine them to solve any problem Requirements Just some high school mathematics level. This course has been designed by two professional Data Scientists so that we can share our knowledge and help you learn complex theory, algorithms and coding libraries in a simple way. We will walk you step-by-step into the World of Machine Learning. With every tutorial you will develop new skills and improve your understanding of this challenging yet lucrative sub-field of Data Science. This course is fun and exciting, but at the same time we dive deep into Machine Learning.

Let's brainstorm what can be written on your data science, machine learning resume today. Disclaimer, we are not counselors nor are we professionals. We just want to talk about what have worked for the staff on a personal basis. Read our full disclaimer article which states that all our articles are for personal purpose only, and cannot be used for commercial purpose. We write beginner friendly articles for bootcamp, online class graduates.

Do you want to leverage the power of Machine Learning without writing any code? Do you want to break into Machine Learning, but you feel overwhelmed and intimidated? Do you want to leverage Machine Learning for your business, but you don't have data science or mathematics background? If the answer is yes to any of these questions, you came to the right place! This course is the only course available online that empowers anyone with zero coding and mathematics background to build, train, test and deploy machine learning models at scale.

It is a practical, hands-on course, i.e. we will spend some time dealing with some of the theoretical concepts related to data science. However, majority of the course will focus on implementing different techniques on real data and interpret the results.. After each video you will learn a new concept or technique which you may apply to your own projects!

What is data science, why is it so popular, and why did the Harvard Business Review hail it as the "sexiest job of the 21st century"? Welcome to the Data Science for All course, where you will learn everything that you need to know about this rapidly growing exiting field of DS. I am Anmol Tomar, a Data Scientist with over 5.5 years of experience in Data Science. I have worked with various fortune 500 clients in various domains such as retail, insurance, banking and helped them take data driven decisions. In this non-technical course, i'll be introduced to everything you were ever too afraid to ask about this fast-growing and exciting field, without needing to write a single line of code.

The Laplace operator was first applied to the study of celestial mechanics, or the motion of objects in outer space, by Pierre-Simon de Laplace, and as such has been named after him. The Laplace operator has since been used to describe many different phenomena, from electric potentials, to the diffusion equation for heat and fluid flow, and quantum mechanics. It has also been recasted to the discrete space, where it has been used in applications related to image processing and spectral clustering. In this tutorial, you will discover a gentle introduction to the Laplacian. A Gentle Introduction to the Laplacian Photo by Aziz Acharki, some rights reserved.

Uncertainty in machine learning is not generally taught as general knowledge in Machine Learning course curricula. In this paper we propose a short curriculum for a course about uncertainty in machine learning, and complement the course with a selection of use cases, aimed to trigger discussion and let students play with the concepts of uncertainty in a programming setting. Our use cases cover the concept of output uncertainty, Bayesian neural networks and weight distributions, sources of uncertainty, and out of distribution detection. We expect that this curriculum and set of use cases motivates the community to adopt these important concepts into courses for safety in AI.

In generative adversarial imitation learning (GAIL), the agent aims to learn a policy from an expert demonstration so that its performance cannot be discriminated from the expert policy on a certain predefined reward set. In this paper, we study GAIL in both online and offline settings with linear function approximation, where both the transition and reward function are linear in the feature maps. Besides the expert demonstration, in the online setting the agent can interact with the environment, while in the offline setting the agent only accesses an additional dataset collected by a prior. For online GAIL, we propose an optimistic generative adversarial policy optimization algorithm (OGAP) and prove that OGAP achieves $\widetilde{\mathcal{O}}(H^2 d^{3/2}K^{1/2}+KH^{3/2}dN_1^{-1/2})$ regret. Here $N_1$ represents the number of trajectories of the expert demonstration, $d$ is the feature dimension, and $K$ is the number of episodes. For offline GAIL, we propose a pessimistic generative adversarial policy optimization algorithm (PGAP). For an arbitrary additional dataset, we obtain the optimality gap of PGAP, achieving the minimax lower bound in the utilization of the additional dataset. Assuming sufficient coverage on the additional dataset, we show that PGAP achieves $\widetilde{\mathcal{O}}(H^{2}dK^{-1/2} +H^2d^{3/2}N_2^{-1/2}+H^{3/2}dN_1^{-1/2} \ )$ optimality gap. Here $N_2$ represents the number of trajectories of the additional dataset with sufficient coverage.