Ai For Business - Ai Applications For Business Success

[LeeAndro]

Last updated 12/2021MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHzLanguage: English | Size: 661.53 MB | Duration: 2h 15m

Learn how to leverage the power of AI to solve your business objectives

What you'll learn
Understand and define SMART Goals
Perform SWOT Analysis
Learn from a professional with a proven track record and valuable experience
Become familiar with modern AI techniques
Discover how to use AI to infer causation
Advance your career
Make better and faster decisions using data
Yield the desired results in your strat business objectives
Requirements
You should have some rudimentary knowledge of higher level mathematics
Understanding Python code is required
Description
AI for Business - AI Applications for Business SuccessAI isn't just a fancy concept that powers self-driving vehicles, robots, and high-tech companies.​

Most organizations - regardless of their size and industry - can benefit from the application of artificial intelligence.Charts and dashboards are useful tools, but they often struggle to analyze big and complex datasets. This is precisely when AI outperforms the traditional Business Intelligence approach! Correlation doesn't imply causation, and this is a significant limitation of BI.Artificial Intelligence can help a company in a variety of ways - it can be employed to build customer retention models, increase gross revenue by optimizing your selling price, find a way to minimize costs and optimize business processes, and ultimately - to run an organization more effectively.This is what AI for Business course aims to teach you.Your instructor, Horia Margarit, has earned two Bachelor's degrees in Cognitive and Computer Sciences at the University of California, Berkeley, and a Master's degree in Statistics from Stanford University. With over 10 years of professional experience in the San Francisco Bay Area, he has differentiated himself by applying highly novel methods and approaches to tackling complex business problems. As a result, his predictions for business applications of AI have been featured in both CIO Magazine and Forbes. Horia's primary focus is on technical underpinnings that maximize actual business and customer value.All this makes him uniquely qualified to teach this topic.In the course, you'll get an overview of business analytics and find out how to define SMART goals and conduct SWOT analysis. You'll go through the challenges and opportunities of supply chain analytics to then detee the business problem we'll tackle throughout the course.Moreover, we will consider the key benefits and limitations of using business analytics approach to solving such problems.Having laid the foundations, we'll then dig deeper and focus on attainability. Even more so, you will discover how to leverage the power of AI in order to achieve the set business goals.Here, you will be able to:· Build an AI model from high fidelity data· Extract actionable explanations· Predict the outcome· Evaluate the quality of the predictionsWe won't spend too much obtaining the data and building the AI model. Instead, we will focus on evaluating the performance of those methods.For that, you will go through key algorithms like Gradient Boosted Machines and Convergent Cross Mapping. Most of all, you will have the chance to examine in great detail novel approaches to understanding model performance such as LIME and SHAP values.And that's not all!After we've learned how to obtain accurate predictions from our models, we'll find out how we can show the significance of the obtained results. To do so, we'll rely on parametric tests. More specifically, we will be working with the hybrid expent and quantile difference tests.Take your AI career to new heights!This course is packed with valuable concepts and state-of-the-art techniques.Enroll now and start your journey to AI for business today!

Overview

Section 1: Introduction

Lecture 1 What Does the Course Cover

Lecture 2 Materials for the course

Section 2: Business Goals

Lecture 3 Introduction

Lecture 4 SWOT Analysis

Lecture 5 SMART Goals

Lecture 6 Limitations of the BI Approach

Lecture 7 Correlation vs. Causation

Lecture 8 Making Recommendations with Descriptive Statistics

Section 3: Approaches to solving the business objective

Lecture 9 Introduction

Lecture 10 The BI Approach

Lecture 11 State Space and Takens' Theorem

Lecture 12 Shadow Manifolds and K-Nearest Neighbors

Section 4: Artificial Intelligence in Business

Lecture 13 Quantifying Attainability

Lecture 14 Gradient Boosted Machines: Part 1

Lecture 15 Gradient Boosted Machines: Part 2

Lecture 16 Gradient Boosted Machines: Part 3

Lecture 17 SHAP Values

Lecture 18 Friedman's H-Statistic

Lecture 19 LIME

Lecture 20 Waterfall Charts 1

Lecture 21 Waterfall Charts 2

Lecture 22 Causation: Traditional Statistical Methods

Lecture 23 Causation: Advanced Statistical Methods

Lecture 24 Series Forecasting with Takens' Theorem

Section 5: Artificial Intelligence Recommends Metrics

Lecture 25 Introduction

Lecture 26 The Hybrid Expent

Lecture 27 Quantile Difference Tests

Lecture 28 Next Steps

Data Scientists,ML eeers looking to become team leads,People who want a successful career in Business,Business Executives,Ambitious Managers,Anyone who wants to understand how to leverage the power of AI in a business setting

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