The Business Statistics and Analysis Specialization is designed to equip you with a basic understanding of business data analysis tools and techniques. You'll master essential spreadsheet functions, build descriptive business data measures, and develop your aptitude for data modeling. You'll also explore basic probability concepts, including measuring and modeling uncertainty, and you'll use various data distributions, along with the Linear Regression Model, to analyze and inform business decisions. The Specialization culminates with a Capstone Project in which you'll apply the skills and knowledge you've gained to an actual business problem. To successfully complete all course assignments, students must have access to a Windows version of Microsoft Excel 2010 or later.

Petko Aleksandrov is professional trader and mentor at EA Forex Academy. He teaches algorithmic trading in his courses and shares his trading strategies. You will learn how he creates 100s of Expert Advisors – Robots for trading. You will receive included 99 Robots for Ethereum trading. You will see how he tested the strategies for one month period with just few clicks.

Deep learning is a popular subset of machine learning that allows you to build complex models that are faster and give more accurate predictions. Natural Language Processing (NLP) offers powerful ways to interpret and act on spoken and written language. It's used to help deal with customer support enquiries, analyse how customers feel about a product, and provide intuitive user interfaces. This comprehensive 2-in-1 course teaches you to write applications using two popular data science concepts, deep learning and NLP. You'll learn through practical demonstrations, clear explanations, and interesting real-world examples.

MIT Professional Education is pleased to offer the Professional Certificate Program in Machine Learning and Artificial Intelligence. MIT has played a leading role in the rise of AI and the new category of jobs it is creating across the world economy. Our goal is to ensure businesses and individuals have the education and training necessary to succeed in the AI-powered future. This certificate guides participants through the latest advancements and technical approaches in artificial intelligence technologies such as natural language processing, predictive analytics, deep learning, and algorithmic methods to further your knowledge of this ever-evolving industry. Awarded upon successful completion of four qualifying Short Programs courses in Professional Education, this certificate equips you with the best practices and actionable knowledge needed to put you and your organization at the forefront of the AI revolution.

One of the most common problems learners have when jumping into Machine Learning and Data Science is the steep learning curve, and when you add to this the complexity of learning programming languages like Python or R you can get demotivated and lose interest fast. In this course you will learn the basic concepts of machine learning using a visual tool. Where you can just drag drop machine learning algorithms and all other functionality hiding the ugliness of code, making it much more easier to grasp the fundamental concepts. I will "hand-hold" you as we build from scratch 2 different types of supervised machine learning algorithms used in the real world, across several industries and I will explain where and how they are used. The course will teach you those fundamental concepts of machine learning by implementing practical exercises which are based on live examples.

About this course: We all learn numbers from the childhood. Some of us like to count, others hate it, but any person uses numbers everyday to buy things, pay for services, estimated time and necessary resources. People have been wondering about numbers' properties for thousands of years. And for thousands of years it was more or less just a game that was only interesting for pure mathematicians. Famous 20th century mathematician G.H. Hardy once said "The Theory of Numbers has always been regarded as one of the most obviously useless branches of Pure Mathematics".

This course is about the fundamental concepts of machine learning, focusing on regression, SVM, decision trees and neural networks. These topics are getting very hot nowadays because these learning algorithms can be used in several fields from software engineering to investment banking. Learning algorithms can recognize patterns which can help detect cancer for example or we may construct algorithms that can have a very good guess about stock prices movement in the market. In each section we will talk about the theoretical background for all of these algorithms then we are going to implement these problems together. We will use Python with Sklearn, Keras and TensorFlow.

This module, along with module 2B introduces two key concepts in statistics/epidemiology, confounding and effect modification. A relation between an outcome and exposure of interested can be confounded if a another variable (or variables) is associated with both the outcome and the exposure. In such cases the crude outcome/exposure associate may over or under-estimate the association of interest. Confounding is an ever-present threat in non-randomized studies, but results of interest can be adjusted for potential confounders.

It is a practical, hands-on course, i.e. we will spend some time dealing with some of the theoretical concepts related to both statistical and machine learning regression analysis. 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.

PWC Deals Exchange April 26, 2018 How is the economy changing? What are the implications for business? What does technology now make possible that was previously impossible? We have to let go of the maps that are steering us wrong In 1625, we thought California was an island In 2018, we still think in terms of standalone firms. We need to think about every company as if it is a network Networks and the Nature of the Firm "The existence of high transaction costs outside firms led to the emergence of the firm as we know it, and management as we know it….The reverse side of Coase's argument is as important: If the (transaction) costs of exchanging value in the society at large go down drastically as is happening today [because of networks], the form and logic of economic and organizational entities necessarily need to change! The mainstream firm, as we have known it, becomes the more expensive alternative."Esko