Machine learning on images is revolutionizing healthcare, manufacturing, retail, and many other sectors. Many previously difficult problems can now be solved by training machine learning (ML) models to identify objects in images. Our aim in this book is to provide intuitive explanations of the ML architectures that underpin this fast-advancing field, and to provide practical code to employ these ML models to solve problems involving classification, measurement, detection, segmentation, representation, generation, counting, and more. Image classification is the "hello world" of deep learning. Therefore, this book also provides a practical end-to-end introduction to deep learning. It can serve as a stepping stone to other deep learning domains, such as natural language processing.

Learn the core concepts of Machine Learning and its algorithms and how to implement them in Python 3 New Rating: 4.4 out of 54.4 (215 ratings) 32,564 students What you'll learn Description'Machine Learning is all about how a machine with an artificial intelligence learns like a human being' Welcome to the course on Machine Learning and Implementing it using Python 3. As the title says, this course recommends to have a basic knowledge in Python 3 to grasp the implementation part easily but it is not compulsory. This course has strong content on the core concepts of ML such as it's features, the steps involved in building a ML Model - Data Preprocessing, Finetuning the Model, Overfitting, Underfitting, Bias, Variance, Confusion Matrix and performance measures of a ML Model. We'll understand the importance of many preprocessing techniques such as Binarization, MinMaxScaler, Standard Scaler We can implement many ML Algorithms in Python using scikit-learn library in a few lines. Can't we? Yet, that won't help us to understand the algorithms. Hence, in this course, we'll first look into understanding the mathematics and concepts behind the algorithms and then, we'll implement the same in Python.

Get a practical understanding of the Scikit-Learn library and learn the ML implementation New Rating: 4.2 out of 5 What you'll learn Description The goal of this course is to help the trainee's expertise working with the python based Scikit-learn library. This training will enable one to implement the concepts of Machine learning using applications by the virtue of Scikit-learn. The sole purpose of this course is to provide a practical understanding of the Scikit-learn library to the trainees. After completing this training, the trainees will be able to endure the application development that requires ML implementation using the Scikit-learn library. In this unit, you will be getting a brief introduction of the concept which includes all the basic details together with the topics that are important to understand.

Mastering Machine Learning With Python In Six Steps About this Book Master machine learning with Python in six steps and explore fundamental to advanced topics, all designed to make you a worthy practitioner. This book's approach is based on the "Six degrees of separation" theory, which states that everyone and everything is a maximum of six steps away. Mastering Machine Learning with Python in Six Steps presents each topic in two parts: theoretical concepts and practical implementation using suitable Python packages. You'll learn the fundamentals of Python programming language, machine learning history, evolution, and the system development frameworks. Key data mining/analysis concepts, such as feature dimension reduction, regression, time series forecasting and their efficient implementation in Scikit-learn are also covered.

I highly recommend reading the McKinsey Global Institute's new report, "Reskilling China: Transforming The World's Largest Workforce Into Lifelong Learners", which focuses on the country's biggest employment challenge, re-training its workforce and the adoption of practices such as lifelong learning to address the growing digital transformation of its productive fabric. How to transform the country that has become the factory of the world, where manual assembly was the cheapest due to its low labor costs, into an artificial intelligence giant, with the largest public blockchain infrastructure in the world, a digital currency in an advanced stage of development that will see an end to cash payments, along with the world's largest 5G network? Xi Jinping's state capitalism is transforming the Asian giant: the possibility of drawing up and maintaining long-term strategies thanks to political stability is driving change at an unprecedented rate, that includes autonomous driving and digital healthcare, advanced retail or even livestock farming. No matter where you look: the modernization and robotization of Chinese assembly factories has led to enormous reductions in the size of their workforces, which, moreover, immediately correspond not only to an increase in their production capacity, but also to a drastic reduction in the number of errors. And the COVID-19 pandemic, far from slowing the process, has accelerated it even further.

The program aims to prepare community college students for careers tapping AI skills. Intel said Tuesday it's expanding a program that aims to educate tomorrow's engineers and technologists on the intricacies of artificial intelligence and help them find jobs in their chosen field. The AI for Workforce Program offers students courses on data collection, computer vision, AI model training, coding, the societal impacts and ethics of AI technology. Students who complete the program will be awarded a certificate or associate degree in artificial intelligence. The program began as a collaboration with an Arizona community college but is being expanded to 18 community colleges in 11 states through a partnership with Dell Technologies, which will provide guidance on how best to configure AI labs for teaching in-person, hybrid and online students.

We consider statistical methods which invoke a min-max distributionally robust formulation to extract good out-of-sample performance in data-driven optimization and learning problems. Acknowledging the distributional uncertainty in learning from limited samples, the min-max formulations introduce an adversarial inner player to explore unseen covariate data. The resulting Distributionally Robust Optimization (DRO) formulations, which include Wasserstein DRO formulations (our main focus), are specified using optimal transportation phenomena. Upon describing how these infinite-dimensional min-max problems can be approached via a finite-dimensional dual reformulation, the tutorial moves into its main component, namely, explaining a generic recipe for optimally selecting the size of the adversary's budget. This is achieved by studying the limit behavior of an optimal transport projection formulation arising from an inquiry on the smallest confidence region that includes the unknown population risk minimizer. Incidentally, this systematic prescription coincides with those in specific examples in high-dimensional statistics and results in error bounds that are free from the curse of dimensions. Equipped with this prescription, we present a central limit theorem for the DRO estimator and provide a recipe for constructing compatible confidence regions that are useful for uncertainty quantification. The rest of the tutorial is devoted to insights into the nature of the optimizers selected by the min-max formulations and additional applications of optimal transport projections.

This webinar series will be hosted by the International Research Centre in Artificial Intelligence (IRCAI) and supported by UNESCO and Knowledge 4 All Foundation, to present the Fellowship to develop datasets and strengthen capacities and innovation potential for Low Resource African Languages project that is composed of research in natural language processing, open dataset creation and publishing, and the development of an interface between policy and technology sphere. The project delivered three main components from research in natural language processing, dataset creation, and policy creation: 1. Fellowship for African AI researchers focused on African languages, based on previously IDRC and Knowledge 4 All Foundation funded work on language datasets. This work contributes to a roadmap for better integration of African languages on digital platforms in aid of lowering the barrier for African participation in the digital economy, 2. Improvement of the representation of AI research carried out on African languages by creating resources for a variety of NLP tasks and in a variety of African languages that will enable good, data-driven results in AI research, 3. Attract an African community of native speakers as contributors of language resources and language technology tools to adopt and support Masakhane NLP, a platform for sharing, maintaining and making use of language resources and tools; establishing widely agreed benchmarks for NLP tasks and stimulating competition between methods and systems, 4. Be used as a model case to inform African evidence-based policymaking concerning Artificial Intelligence and will be included in UNESCO’s AI Decision maker’s Essential to inform policymakers. Find more information at IRCAI Webinar Series

If you are interested in learning to use NumPy, Pandas, Machine Learning, and more from the comfort of your home then you have landed to the right course. In this course, you will be taught all about using Python for data science and machine learning in the best possible manner. The instructor will explain how you can use spark for big data analysis in detail. Then you will get a chance to understand how to implement machine learning algorithms. Going further, you will get a chance to understand how to use Matplotlib for python plotting.

Understand stock market fundamentals Understand the Modern Portfolio Theory Understand stochastic processes and the famous Black-Scholes mode Understand Monte-Carlo simulations Understand Value-at-Risk (VaR) You should have an interest in quantitative finance as well as in mathematics and programming! This course is about the fundamental basics of financial engineering. First of all you will learn about stocks, bonds and other derivatives. The main reason of this course is to get a better understanding of mathematical models concerning the finance in the main. Markowitz-model is the first step.