Last updated 12/2021
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz
Language: 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 strategic 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 determine 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 time 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 experiment 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 Time Series Forecasting with Takens' Theorem
Section 5: Artificial Intelligence Recommends Metrics
Lecture 25 Introduction
Lecture 26 The Hybrid Experiment
Lecture 27 Quantile Difference Tests
Lecture 28 Next Steps
Data Scientists,ML engineers 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
Homepage
Recommend Download Link Hight Speed | Please Say Thanks Keep Topic Live
Links are Interchangeable - No Password - Single Extraction
